hot research topics in data science

37 Research Topics In Data Science To Stay On Top Of

• February 22, 2024

As a data scientist, staying on top of the latest research in your field is essential.

The data science landscape changes rapidly, and new techniques and tools are constantly being developed.

To keep up with the competition, you need to be aware of the latest trends and topics in data science research.

In this article, we will provide an overview of 37 hot research topics in data science.

We will discuss each topic in detail, including its significance and potential applications.

These topics could be an idea for a thesis or simply topics you can research independently.

Stay tuned – this is one blog post you don’t want to miss!

37 Research Topics in Data Science

1.) predictive modeling.

Predictive modeling is a significant portion of data science and a topic you must be aware of.

Simply put, it is the process of using historical data to build models that can predict future outcomes.

Predictive modeling has many applications, from marketing and sales to financial forecasting and risk management.

As businesses increasingly rely on data to make decisions, predictive modeling is becoming more and more important.

While it can be complex, predictive modeling is a powerful tool that gives businesses a competitive advantage.

2.) Big Data Analytics

These days, it seems like everyone is talking about big data.

And with good reason – organizations of all sizes are sitting on mountains of data, and they’re increasingly turning to data scientists to help them make sense of it all.

But what exactly is big data? And what does it mean for data science?

Simply put, big data is a term used to describe datasets that are too large and complex for traditional data processing techniques.

Big data typically refers to datasets of a few terabytes or more.

But size isn’t the only defining characteristic – big data is also characterized by its high Velocity (the speed at which data is generated), Variety (the different types of data), and Volume (the amount of the information).

Given the enormity of big data, it’s not surprising that organizations are struggling to make sense of it all.

That’s where data science comes in.

Data scientists use various methods to wrangle big data, including distributed computing and other decentralized technologies.

With the help of data science, organizations are beginning to unlock the hidden value in their big data.

By harnessing the power of big data analytics, they can improve their decision-making, better understand their customers, and develop new products and services.

3.) Auto Machine Learning

Auto machine learning is a research topic in data science concerned with developing algorithms that can automatically learn from data without intervention.

This area of research is vital because it allows data scientists to automate the process of writing code for every dataset.

This allows us to focus on other tasks, such as model selection and validation.

Auto machine learning algorithms can learn from data in a hands-off way for the data scientist – while still providing incredible insights.

This makes them a valuable tool for data scientists who either don’t have the skills to do their own analysis or are struggling.

4.) Text Mining

Text mining is a research topic in data science that deals with text data extraction.

This area of research is important because it allows us to get as much information as possible from the vast amount of text data available today.

Text mining techniques can extract information from text data, such as keywords, sentiments, and relationships.

This information can be used for various purposes, such as model building and predictive analytics.

5.) Natural Language Processing

Natural language processing is a data science research topic that analyzes human language data.

This area of research is important because it allows us to understand and make sense of the vast amount of text data available today.

Natural language processing techniques can build predictive and interactive models from any language data.

Natural Language processing is pretty broad, and recent advances like GPT-3 have pushed this topic to the forefront.

6.) Recommender Systems

Recommender systems are an exciting topic in data science because they allow us to make better products, services, and content recommendations.

Businesses can better understand their customers and their needs by using recommender systems.

This, in turn, allows them to develop better products and services that meet the needs of their customers.

Recommender systems are also used to recommend content to users.

This can be done on an individual level or at a group level.

Think about Netflix, for example, always knowing what you want to watch!

Recommender systems are a valuable tool for businesses and users alike.

7.) Deep Learning

Deep learning is a research topic in data science that deals with artificial neural networks.

These networks are composed of multiple layers, and each layer is formed from various nodes.

Deep learning networks can learn from data similarly to how humans learn, irrespective of the data distribution.

This makes them a valuable tool for data scientists looking to build models that can learn from data independently.

The deep learning network has become very popular in recent years because of its ability to achieve state-of-the-art results on various tasks.

There seems to be a new SOTA deep learning algorithm research paper on  https://arxiv.org/  every single day!

8.) Reinforcement Learning

Reinforcement learning is a research topic in data science that deals with algorithms that can learn on multiple levels from interactions with their environment.

This area of research is essential because it allows us to develop algorithms that can learn non-greedy approaches to decision-making, allowing businesses and companies to win in the long term compared to the short.

9.) Data Visualization

Data visualization is an excellent research topic in data science because it allows us to see our data in a way that is easy to understand.

Data visualization techniques can be used to create charts, graphs, and other visual representations of data.

This allows us to see the patterns and trends hidden in our data.

Data visualization is also used to communicate results to others.

This allows us to share our findings with others in a way that is easy to understand.

There are many ways to contribute to and learn about data visualization.

Some ways include attending conferences, reading papers, and contributing to open-source projects.

10.) Predictive Maintenance

Predictive maintenance is a hot topic in data science because it allows us to prevent failures before they happen.

This is done using data analytics to predict when a failure will occur.

This allows us to take corrective action before the failure actually happens.

While this sounds simple, avoiding false positives while keeping recall is challenging and an area wide open for advancement.

11.) Financial Analysis

Financial analysis is an older topic that has been around for a while but is still a great field where contributions can be felt.

Current researchers are focused on analyzing macroeconomic data to make better financial decisions.

This is done by analyzing the data to identify trends and patterns.

Financial analysts can use this information to make informed decisions about where to invest their money.

Financial analysis is also used to predict future economic trends.

This allows businesses and individuals to prepare for potential financial hardships and enable companies to be cash-heavy during good economic conditions.

Overall, financial analysis is a valuable tool for anyone looking to make better financial decisions.

12.) Image Recognition

Image recognition is one of the hottest topics in data science because it allows us to identify objects in images.

This is done using artificial intelligence algorithms that can learn from data and understand what objects you’re looking for.

This allows us to build models that can accurately recognize objects in images and video.

This is a valuable tool for businesses and individuals who want to be able to identify objects in images.

Think about security, identification, routing, traffic, etc.

Image Recognition has gained a ton of momentum recently – for a good reason.

13.) Fraud Detection

Fraud detection is a great topic in data science because it allows us to identify fraudulent activity before it happens.

This is done by analyzing data to look for patterns and trends that may be associated with the fraud.

Once our machine learning model recognizes some of these patterns in real time, it immediately detects fraud.

This allows us to take corrective action before the fraud actually happens.

Fraud detection is a valuable tool for anyone who wants to protect themselves from potential fraudulent activity.

14.) Web Scraping

Web scraping is a controversial topic in data science because it allows us to collect data from the web, which is usually data you do not own.

This is done by extracting data from websites using scraping tools that are usually custom-programmed.

This allows us to collect data that would otherwise be inaccessible.

For obvious reasons, web scraping is a unique tool – giving you data your competitors would have no chance of getting.

I think there is an excellent opportunity to create new and innovative ways to make scraping accessible for everyone, not just those who understand Selenium and Beautiful Soup.

15.) Social Media Analysis

Social media analysis is not new; many people have already created exciting and innovative algorithms to study this.

However, it is still a great data science research topic because it allows us to understand how people interact on social media.

This is done by analyzing data from social media platforms to look for insights, bots, and recent societal trends.

Once we understand these practices, we can use this information to improve our marketing efforts.

For example, if we know that a particular demographic prefers a specific type of content, we can create more content that appeals to them.

Social media analysis is also used to understand how people interact with brands on social media.

This allows businesses to understand better what their customers want and need.

Overall, social media analysis is valuable for anyone who wants to improve their marketing efforts or understand how customers interact with brands.

16.) GPU Computing

GPU computing is a fun new research topic in data science because it allows us to process data much faster than traditional CPUs .

Due to how GPUs are made, they’re incredibly proficient at intense matrix operations, outperforming traditional CPUs by very high margins.

While the computation is fast, the coding is still tricky.

There is an excellent research opportunity to bring these innovations to non-traditional modules, allowing data science to take advantage of GPU computing outside of deep learning.

17.) Quantum Computing

Quantum computing is a new research topic in data science and physics because it allows us to process data much faster than traditional computers.

It also opens the door to new types of data.

There are just some problems that can’t be solved utilizing outside of the classical computer.

For example, if you wanted to understand how a single atom moved around, a classical computer couldn’t handle this problem.

You’ll need to utilize a quantum computer to handle quantum mechanics problems.

This may be the “hottest” research topic on the planet right now, with some of the top researchers in computer science and physics worldwide working on it.

You could be too.

18.) Genomics

Genomics may be the only research topic that can compete with quantum computing regarding the “number of top researchers working on it.”

Genomics is a fantastic intersection of data science because it allows us to understand how genes work.

This is done by sequencing the DNA of different organisms to look for insights into our and other species.

Once we understand these patterns, we can use this information to improve our understanding of diseases and create new and innovative treatments for them.

Genomics is also used to study the evolution of different species.

Genomics is the future and a field begging for new and exciting research professionals to take it to the next step.

19.) Location-based services

Location-based services are an old and time-tested research topic in data science.

Since GPS and 4g cell phone reception became a thing, we’ve been trying to stay informed about how humans interact with their environment.

This is done by analyzing data from GPS tracking devices, cell phone towers, and Wi-Fi routers to look for insights into how humans interact.

Once we understand these practices, we can use this information to improve our geotargeting efforts, improve maps, find faster routes, and improve cohesion throughout a community.

Location-based services are used to understand the user, something every business could always use a little bit more of.

While a seemingly “stale” field, location-based services have seen a revival period with self-driving cars.

20.) Smart City Applications

Smart city applications are all the rage in data science research right now.

By harnessing the power of data, cities can become more efficient and sustainable.

But what exactly are smart city applications?

In short, they are systems that use data to improve city infrastructure and services.

This can include anything from traffic management and energy use to waste management and public safety.

Data is collected from various sources, including sensors, cameras, and social media.

It is then analyzed to identify tendencies and habits.

This information can make predictions about future needs and optimize city resources.

As more and more cities strive to become “smart,” the demand for data scientists with expertise in smart city applications is only growing.

21.) Internet Of Things (IoT)

The Internet of Things, or IoT, is exciting and new data science and sustainability research topic.

IoT is a network of physical objects embedded with sensors and connected to the internet.

These objects can include everything from alarm clocks to refrigerators; they’re all connected to the internet.

That means that they can share data with computers.

And that’s where data science comes in.

Data scientists are using IoT data to learn everything from how people use energy to how traffic flows through a city.

They’re also using IoT data to predict when an appliance will break down or when a road will be congested.

Really, the possibilities are endless.

With such a wide-open field, it’s easy to see why IoT is being researched by some of the top professionals in the world.

22.) Cybersecurity

Cybersecurity is a relatively new research topic in data science and in general, but it’s already garnering a lot of attention from businesses and organizations.

After all, with the increasing number of cyber attacks in recent years, it’s clear that we need to find better ways to protect our data.

While most of cybersecurity focuses on infrastructure, data scientists can leverage historical events to find potential exploits to protect their companies.

Sometimes, looking at a problem from a different angle helps, and that’s what data science brings to cybersecurity.

Also, data science can help to develop new security technologies and protocols.

As a result, cybersecurity is a crucial data science research area and one that will only become more important in the years to come.

23.) Blockchain

Blockchain is an incredible new research topic in data science for several reasons.

First, it is a distributed database technology that enables secure, transparent, and tamper-proof transactions.

Did someone say transmitting data?

This makes it an ideal platform for tracking data and transactions in various industries.

Second, blockchain is powered by cryptography, which not only makes it highly secure – but is a familiar foe for data scientists.

Finally, blockchain is still in its early stages of development, so there is much room for research and innovation.

As a result, blockchain is a great new research topic in data science that vows to revolutionize how we store, transmit and manage data.

24.) Sustainability

Sustainability is a relatively new research topic in data science, but it is gaining traction quickly.

To keep up with this demand, The Wharton School of the University of Pennsylvania has  started to offer an MBA in Sustainability .

This demand isn’t shocking, and some of the reasons include the following:

Sustainability is an important issue that is relevant to everyone.

Datasets on sustainability are constantly growing and changing, making it an exciting challenge for data scientists.

There hasn’t been a “set way” to approach sustainability from a data perspective, making it an excellent opportunity for interdisciplinary research.

As data science grows, sustainability will likely become an increasingly important research topic.

25.) Educational Data

Education has always been a great topic for research, and with the advent of big data, educational data has become an even richer source of information.

By studying educational data, researchers can gain insights into how students learn, what motivates them, and what barriers these students may face.

Besides, data science can be used to develop educational interventions tailored to individual students’ needs.

Imagine being the researcher that helps that high schooler pass mathematics; what an incredible feeling.

With the increasing availability of educational data, data science has enormous potential to improve the quality of education.

26.) Politics

As data science continues to evolve, so does the scope of its applications.

Originally used primarily for business intelligence and marketing, data science is now applied to various fields, including politics.

By analyzing large data sets, political scientists (data scientists with a cooler name) can gain valuable insights into voting patterns, campaign strategies, and more.

Further, data science can be used to forecast election results and understand the effects of political events on public opinion.

With the wealth of data available, there is no shortage of research opportunities in this field.

As data science evolves, so does our understanding of politics and its role in our world.

27.) Cloud Technologies

Cloud technologies are a great research topic.

It allows for the outsourcing and sharing of computer resources and applications all over the internet.

This lets organizations save money on hardware and maintenance costs while providing employees access to the latest and greatest software and applications.

I believe there is an argument that AWS could be the greatest and most technologically advanced business ever built (Yes, I know it’s only part of the company).

Besides, cloud technologies can help improve team members’ collaboration by allowing them to share files and work on projects together in real-time.

As more businesses adopt cloud technologies, data scientists must stay up-to-date on the latest trends in this area.

By researching cloud technologies, data scientists can help organizations to make the most of this new and exciting technology.

28.) Robotics

Robotics has recently become a household name, and it’s for a good reason.

First, robotics deals with controlling and planning physical systems, an inherently complex problem.

Second, robotics requires various sensors and actuators to interact with the world, making it an ideal application for machine learning techniques.

Finally, robotics is an interdisciplinary field that draws on various disciplines, such as computer science, mechanical engineering, and electrical engineering.

As a result, robotics is a rich source of research problems for data scientists.

29.) HealthCare

Healthcare is an industry that is ripe for data-driven innovation.

Hospitals, clinics, and health insurance companies generate a tremendous amount of data daily.

This data can be used to improve the quality of care and outcomes for patients.

This is perfect timing, as the healthcare industry is undergoing a significant shift towards value-based care, which means there is a greater need than ever for data-driven decision-making.

As a result, healthcare is an exciting new research topic for data scientists.

There are many different ways in which data can be used to improve healthcare, and there is a ton of room for newcomers to make discoveries.

30.) Remote Work

There’s no doubt that remote work is on the rise.

In today’s global economy, more and more businesses are allowing their employees to work from home or anywhere else they can get a stable internet connection.

But what does this mean for data science? Well, for one thing, it opens up a whole new field of research.

For example, how does remote work impact employee productivity?

What are the best ways to manage and collaborate on data science projects when team members are spread across the globe?

And what are the cybersecurity risks associated with working remotely?

These are just a few of the questions that data scientists will be able to answer with further research.

So if you’re looking for a new topic to sink your teeth into, remote work in data science is a great option.

31.) Data-Driven Journalism

Data-driven journalism is an exciting new field of research that combines the best of both worlds: the rigor of data science with the creativity of journalism.

By applying data analytics to large datasets, journalists can uncover stories that would otherwise be hidden.

And telling these stories compellingly can help people better understand the world around them.

Data-driven journalism is still in its infancy, but it has already had a major impact on how news is reported.

In the future, it will only become more important as data becomes increasingly fluid among journalists.

It is an exciting new topic and research field for data scientists to explore.

32.) Data Engineering

Data engineering is a staple in data science, focusing on efficiently managing data.

Data engineers are responsible for developing and maintaining the systems that collect, process, and store data.

In recent years, there has been an increasing demand for data engineers as the volume of data generated by businesses and organizations has grown exponentially.

Data engineers must be able to design and implement efficient data-processing pipelines and have the skills to optimize and troubleshoot existing systems.

If you are looking for a challenging research topic that would immediately impact you worldwide, then improving or innovating a new approach in data engineering would be a good start.

33.) Data Curation

Data curation has been a hot topic in the data science community for some time now.

Curating data involves organizing, managing, and preserving data so researchers can use it.

Data curation can help to ensure that data is accurate, reliable, and accessible.

It can also help to prevent research duplication and to facilitate the sharing of data between researchers.

Data curation is a vital part of data science. In recent years, there has been an increasing focus on data curation, as it has become clear that it is essential for ensuring data quality.

As a result, data curation is now a major research topic in data science.

There are numerous books and articles on the subject, and many universities offer courses on data curation.

Data curation is an integral part of data science and will only become more important in the future.

34.) Meta-Learning

Meta-learning is gaining a ton of steam in data science. It’s learning how to learn.

So, if you can learn how to learn, you can learn anything much faster.

Meta-learning is mainly used in deep learning, as applications outside of this are generally pretty hard.

In deep learning, many parameters need to be tuned for a good model, and there’s usually a lot of data.

You can save time and effort if you can automatically and quickly do this tuning.

In machine learning, meta-learning can improve models’ performance by sharing knowledge between different models.

For example, if you have a bunch of different models that all solve the same problem, then you can use meta-learning to share the knowledge between them to improve the cluster (groups) overall performance.

I don’t know how anyone looking for a research topic could stay away from this field; it’s what the  Terminator  warned us about!

35.) Data Warehousing

A data warehouse is a system used for data analysis and reporting.

It is a central data repository created by combining data from multiple sources.

Data warehouses are often used to store historical data, such as sales data, financial data, and customer data.

This data type can be used to create reports and perform statistical analysis.

Data warehouses also store data that the organization is not currently using.

This type of data can be used for future research projects.

Data warehousing is an incredible research topic in data science because it offers a variety of benefits.

Data warehouses help organizations to save time and money by reducing the need for manual data entry.

They also help to improve the accuracy of reports and provide a complete picture of the organization’s performance.

Data warehousing feels like one of the weakest parts of the Data Science Technology Stack; if you want a research topic that could have a monumental impact – data warehousing is an excellent place to look.

36.) Business Intelligence

Business intelligence aims to collect, process, and analyze data to help businesses make better decisions.

Business intelligence can improve marketing, sales, customer service, and operations.

It can also be used to identify new business opportunities and track competition.

BI is business and another tool in your company’s toolbox to continue dominating your area.

Data science is the perfect tool for business intelligence because it combines statistics, computer science, and machine learning.

Data scientists can use business intelligence to answer questions like, “What are our customers buying?” or “What are our competitors doing?” or “How can we increase sales?”

Business intelligence is a great way to improve your business’s bottom line and an excellent opportunity to dive deep into a well-respected research topic.

37.) Crowdsourcing

One of the newest areas of research in data science is crowdsourcing.

Crowdsourcing is a process of sourcing tasks or projects to a large group of people, typically via the internet.

This can be done for various purposes, such as gathering data, developing new algorithms, or even just for fun (think: online quizzes and surveys).

But what makes crowdsourcing so powerful is that it allows businesses and organizations to tap into a vast pool of talent and resources they wouldn’t otherwise have access to.

And with the rise of social media, it’s easier than ever to connect with potential crowdsource workers worldwide.

Imagine if you could effect that, finding innovative ways to improve how people work together.

That would have a huge effect.

Final Thoughts, Are These Research Topics In Data Science For You?

Thirty-seven different research topics in data science are a lot to take in, but we hope you found a research topic that interests you.

If not, don’t worry – there are plenty of other great topics to explore.

The important thing is to get started with your research and find ways to apply what you learn to real-world problems.

We wish you the best of luck as you begin your data science journey!

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Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

• Developing machine learning models for real-time fraud detection in online transactions.
• The use of big data analytics in predicting and managing urban traffic flow.
• Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
• The application of predictive analytics in personalizing cancer treatment plans.
• Analyzing consumer behavior through big data to enhance retail marketing strategies.
• The role of data science in optimizing renewable energy generation from wind farms.
• Developing natural language processing algorithms for real-time news aggregation and summarization.
• The application of big data in monitoring and predicting epidemic outbreaks.
• Investigating the use of machine learning in automating credit scoring for microfinance.
• The role of data analytics in improving patient care in telemedicine.
• Developing AI-driven models for predictive maintenance in the manufacturing industry.
• The use of big data analytics in enhancing cybersecurity threat intelligence.
• Investigating the impact of sentiment analysis on brand reputation management.
• The application of data science in optimizing logistics and supply chain operations.
• Developing deep learning techniques for image recognition in medical diagnostics.
• The role of big data in analyzing climate change impacts on agricultural productivity.
• Investigating the use of data analytics in optimizing energy consumption in smart buildings.
• The application of machine learning in detecting plagiarism in academic works.
• Analyzing social media data for trends in political opinion and electoral predictions.
• The role of big data in enhancing sports performance analytics.
• Developing data-driven strategies for effective water resource management.
• The use of big data in improving customer experience in the banking sector.
• Investigating the application of data science in fraud detection in insurance claims.
• The role of predictive analytics in financial market risk assessment.
• Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

• The application of big data in public transportation systems for route optimization.
• Investigating the impact of big data analytics on e-commerce recommendation systems.
• The use of data mining techniques in understanding consumer preferences in the entertainment industry.
• Developing predictive models for real estate pricing and market trends.
• The role of big data in tracking and managing environmental pollution.
• Investigating the use of data analytics in improving airline operational efficiency.
• The application of machine learning in optimizing pharmaceutical drug discovery.
• Analyzing online customer reviews to inform product development in the tech industry.
• The role of data science in crime prediction and prevention strategies.
• Developing models for analyzing financial time series data for investment strategies.
• The use of big data in assessing the impact of educational policies on student performance.
• Investigating the effectiveness of data visualization techniques in business reporting.
• The application of data analytics in human resource management and talent acquisition.
• Developing algorithms for anomaly detection in network traffic data.
• The role of machine learning in enhancing personalized online learning experiences.
• Investigating the use of big data in urban planning and smart city development.
• The application of predictive analytics in weather forecasting and disaster management.
• Analyzing consumer data to drive innovations in the automotive industry.
• The role of data science in optimizing content delivery networks for streaming services.
• Developing machine learning models for automated text classification in legal documents.
• The use of big data in tracking global supply chain disruptions.
• Investigating the application of data analytics in personalized nutrition and fitness.
• The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
• Developing predictive models for customer churn in the telecommunications industry.
• The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
• Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
• Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
• Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
• An Essay on How Data Science Can Strengthen Business (Santos, 2023)
• A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
• You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
• Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
• Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
• A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
• Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
• Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
• Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
• Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
• TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
• Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
• MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
• COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
• Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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Analytics Insight

10 Best Research and Thesis Topic Ideas for Data Science in 2022

These research and thesis topics for data science will ensure more knowledge and skills for both students and scholars

• Handling practical video analytics in a distributed cloud:  With increased dependency on the internet, sharing videos has become a mode of data and information exchange. The role of the implementation of the Internet of Things (IoT), telecom infrastructure, and operators is huge in generating insights from video analytics. In this perspective, several questions need to be answered, like the efficiency of the existing analytics systems, the changes about to take place if real-time analytics are integrated, and others.
• Smart healthcare systems using big data analytics: Big data analytics plays a significant role in making healthcare more efficient, accessible, and cost-effective. Big data analytics enhances the operational efficiency of smart healthcare providers by providing real-time analytics. It enhances the capabilities of the intelligent systems by using short-span data-driven insights, but there are still distinct challenges that are yet to be addressed in this field.
• Identifying fake news using real-time analytics:  The circulation of fake news has become a pressing issue in the modern era. The data gathered from social media networks might seem legit, but sometimes they are not. The sources that provide the data are unauthenticated most of the time, which makes it a crucial issue to be addressed.
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• Secure federated learning with real-world applications : Federated learning is a technique that trains an algorithm across multiple decentralized edge devices and servers. This technique can be adopted to build models locally, but if this technique can be deployed at scale or not, across multiple platforms with high-level security is still obscure.
• Big data analytics and its impact on marketing strategy : The advent of data science and big data analytics has entirely redefined the marketing industry. It has helped enterprises by offering valuable insights into their existing and future customers. But several issues like the existence of surplus data, integrating complex data into customers’ journeys, and complete data privacy are some of the branches that are still untrodden and need immediate attention.
• Impact of big data on business decision-making: Present studies signify that big data has transformed the way managers and business leaders make critical decisions concerning the growth and development of the business. It allows them to access objective data and analyse the market environments, enabling companies to adapt rapidly and make decisions faster. Working on this topic will help students understand the present market and business conditions and help them analyse new solutions.
• Implementing big data to understand consumer behaviour : In understanding consumer behaviour, big data is used to analyse the data points depicting a consumer’s journey after buying a product. Data gives a clearer picture in understanding specific scenarios. This topic will help understand the problems that businesses face in utilizing the insights and develop new strategies in the future to generate more ROI.
• Applications of big data to predict future demand and forecasting : Predictive analytics in data science has emerged as an integral part of decision-making and demand forecasting. Working on this topic will enable the students to determine the significance of the high-quality historical data analysis and the factors that drive higher demand in consumers.
• The importance of data exploration over data analysis : Exploration enables a deeper understanding of the dataset, making it easier to navigate and use the data later. Intelligent analysts must understand and explore the differences between data exploration and analysis and use them according to specific needs to fulfill organizational requirements.
• Data science and software engineering : Software engineering and development are a major part of data science. Skilled data professionals should learn and explore the possibilities of the various technical and software skills for performing critical AI and big data tasks.

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214 Best Big Data Research Topics for Your Thesis Paper

Finding an ideal big data research topic can take you a long time. Big data, IoT, and robotics have evolved. The future generations will be immersed in major technologies that will make work easier. Work that was done by 10 people will now be done by one person or a machine. This is amazing because, in as much as there will be job loss, more jobs will be created. It is a win-win for everyone.

Big data is a major topic that is being embraced globally. Data science and analytics are helping institutions, governments, and the private sector. We will share with you the best big data research topics.

On top of that, we can offer you the best writing tips to ensure you prosper well in your academics. As students in the university, you need to do proper research to get top grades. Hence, you can consult us if in need of research paper writing services.

Big Data Analytics Research Topics for your Research Project

Are you looking for an ideal big data analytics research topic? Once you choose a topic, consult your professor to evaluate whether it is a great topic. This will help you to get good grades.

• Which are the best tools and software for big data processing?
• Evaluate the security issues that face big data.
• An analysis of large-scale data for social networks globally.
• The influence of big data storage systems.
• The best platforms for big data computing.
• The relation between business intelligence and big data analytics.
• The importance of semantics and visualization of big data.
• Analysis of big data technologies for businesses.
• The common methods used for machine learning in big data.
• The difference between self-turning and symmetrical spectral clustering.
• The importance of information-based clustering.
• Evaluate the hierarchical clustering and density-based clustering application.
• How is data mining used to analyze transaction data?
• The major importance of dependency modeling.
• The influence of probabilistic classification in data mining.

Interesting Big Data Analytics Topics

Who said big data had to be boring? Here are some interesting big data analytics topics that you can try. They are based on how some phenomena are done to make the world a better place.

• Discuss the privacy issues in big data.
• Evaluate the storage systems of scalable in big data.
• The best big data processing software and tools.
• Data mining tools and techniques are popularly used.
• Evaluate the scalable architectures for parallel data processing.
• The major natural language processing methods.
• Which are the best big data tools and deployment platforms?
• The best algorithms for data visualization.
• Analyze the anomaly detection in cloud servers
• The scrutiny normally done for the recruitment of big data job profiles.
• The malicious user detection in big data collection.
• Learning long-term dependencies via the Fourier recurrent units.
• Nomadic computing for big data analytics.
• The elementary estimators for graphical models.
• The memory-efficient kernel approximation.

Big Data Latest Research Topics

Do you know the latest research topics at the moment? These 15 topics will help you to dive into interesting research. You may even build on research done by other scholars.

• Evaluate the data mining process.
• The influence of the various dimension reduction methods and techniques.
• The best data classification methods.
• The simple linear regression modeling methods.
• Evaluate the logistic regression modeling.
• What are the commonly used theorems?
• The influence of cluster analysis methods in big data.
• The importance of smoothing methods analysis in big data.
• How is fraud detection done through AI?
• Analyze the use of GIS and spatial data.
• How important is artificial intelligence in the modern world?
• What is agile data science?
• Analyze the behavioral analytics process.
• Semantic analytics distribution.
• How is domain knowledge important in data analysis?

Big Data Debate Topics

If you want to prosper in the field of big data, you need to try even hard topics. These big data debate topics are interesting and will help you to get a better understanding.

• The difference between big data analytics and traditional data analytics methods.
• Why do you think the organization should think beyond the Hadoop hype?
• Does the size of the data matter more than how recent the data is?
• Is it true that bigger data are not always better?
• The debate of privacy and personalization in maintaining ethics in big data.
• The relation between data science and privacy.
• Do you think data science is a rebranding of statistics?
• Who delivers better results between data scientists and domain experts?
• According to your view, is data science dead?
• Do you think analytics teams need to be centralized or decentralized?
• The best methods to resource an analytics team.
• The best business case for investing in analytics.
• The societal implications of the use of predictive analytics within Education.
• Is there a need for greater control to prevent experimentation on social media users without their consent?
• How is the government using big data; for the improvement of public statistics or to control the population?

University Dissertation Topics on Big Data

Are you doing your Masters or Ph.D. and wondering the best dissertation topic or thesis to do? Why not try any of these? They are interesting and based on various phenomena. While doing the research ensure you relate the phenomenon with the current modern society.

• The machine learning algorithms are used for fall recognition.
• The divergence and convergence of the internet of things.
• The reliable data movements using bandwidth provision strategies.
• How is big data analytics using artificial neural networks in cloud gaming?
• How is Twitter accounts classification done using network-based features?
• How is online anomaly detection done in the cloud collaborative environment?
• Evaluate the public transportation insights provided by big data.
• Evaluate the paradigm for cancer patients using the nursing EHR to predict the outcome.
• Discuss the current data lossless compression in the smart grid.
• How does online advertising traffic prediction helps in boosting businesses?
• How is the hyperspectral classification done using the multiple kernel learning paradigm?
• The analysis of large data sets downloaded from websites.
• How does social media data help advertising companies globally?
• Which are the systems recognizing and enforcing ownership of data records?
• The alternate possibilities emerging for edge computing.

The Best Big Data Analysis Research Topics and Essays

There are a lot of issues that are associated with big data. Here are some of the research topics that you can use in your essays. These topics are ideal whether in high school or college.

• The various errors and uncertainty in making data decisions.
• The application of big data on tourism.
• The automation innovation with big data or related technology
• The business models of big data ecosystems.
• Privacy awareness in the era of big data and machine learning.
• The data privacy for big automotive data.
• How is traffic managed in defined data center networks?
• Big data analytics for fault detection.
• The need for machine learning with big data.
• The innovative big data processing used in health care institutions.
• The money normalization and extraction from texts.
• How is text categorization done in AI?
• The opportunistic development of data-driven interactive applications.
• The use of data science and big data towards personalized medicine.
• The programming and optimization of big data applications.

The Latest Big Data Research Topics for your Research Proposal

Doing a research proposal can be hard at first unless you choose an ideal topic. If you are just diving into the big data field, you can use any of these topics to get a deeper understanding.

• The data-centric network of things.
• Big data management using artificial intelligence supply chain.
• The big data analytics for maintenance.
• The high confidence network predictions for big biological data.
• The performance optimization techniques and tools for data-intensive computation platforms.
• The predictive modeling in the legal context.
• Analysis of large data sets in life sciences.
• How to understand the mobility and transport modal disparities sing emerging data sources?
• How do you think data analytics can support asset management decisions?
• An analysis of travel patterns for cellular network data.
• The data-driven strategic planning for citywide building retrofitting.
• How is money normalization done in data analytics?
• Major techniques used in data mining.
• The big data adaptation and analytics of cloud computing.
• The predictive data maintenance for fault diagnosis.

Interesting Research Topics on A/B Testing In Big Data

A/B testing topics are different from the normal big data topics. However, you use an almost similar methodology to find the reasons behind the issues. These topics are interesting and will help you to get a deeper understanding.

• How is ultra-targeted marketing done?
• The transition of A/B testing from digital to offline.
• How can big data and A/B testing be done to win an election?
• Evaluate the use of A/B testing on big data
• Evaluate A/B testing as a randomized control experiment.
• How does A/B testing work?
• The mistakes to avoid while conducting the A/B testing.
• The most ideal time to use A/B testing.
• The best way to interpret results for an A/B test.
• The major principles of A/B tests.
• Evaluate the cluster randomization in big data
• The best way to analyze A/B test results and the statistical significance.
• How is A/B testing used in boosting businesses?
• The importance of data analysis in conversion research
• The importance of A/B testing in data science.

Amazing Research Topics on Big Data and Local Governments

Governments are now using big data to make the lives of the citizens better. This is in the government and the various institutions. They are based on real-life experiences and making the world better.

• Assess the benefits and barriers of big data in the public sector.
• The best approach to smart city data ecosystems.
• The big analytics used for policymaking.
• Evaluate the smart technology and emergence algorithm bureaucracy.
• Evaluate the use of citizen scoring in public services.
• An analysis of the government administrative data globally.
• The public values are found in the era of big data.
• Public engagement on local government data use.
• Data analytics use in policymaking.
• How are algorithms used in public sector decision-making?
• The democratic governance in the big data era.
• The best business model innovation to be used in sustainable organizations.
• How does the government use the collected data from various sources?
• The role of big data for smart cities.
• How does big data play a role in policymaking?

Easy Research Topics on Big Data

Who said big data topics had to be hard? Here are some of the easiest research topics. They are based on data management, research, and data retention. Pick one and try it!

• Who uses big data analytics?
• Evaluate structure machine learning.
• Explain the whole deep learning process.
• Which are the best ways to manage platforms for enterprise analytics?
• Which are the new technologies used in data management?
• What is the importance of data retention?
• The best way to work with images is when doing research.
• The best way to promote research outreach is through data management.
• The best way to source and manage external data.
• Does machine learning improve the quality of data?
• Describe the security technologies that can be used in data protection.
• Evaluate token-based authentication and its importance.
• How can poor data security lead to the loss of information?
• How to determine secure data.
• What is the importance of centralized key management?

Unique IoT and Big Data Research Topics

Internet of Things has evolved and many devices are now using it. There are smart devices, smart cities, smart locks, and much more. Things can now be controlled by the touch of a button.

• Evaluate the 5G networks and IoT.
• Analyze the use of Artificial intelligence in the modern world.
• How do ultra-power IoT technologies work?
• Evaluate the adaptive systems and models at runtime.
• How have smart cities and smart environments improved the living space?
• The importance of the IoT-based supply chains.
• How does smart agriculture influence water management?
• The internet applications naming and identifiers.
• How does the smart grid influence energy management?
• Which are the best design principles for IoT application development?
• The best human-device interactions for the Internet of Things.
• The relation between urban dynamics and crowdsourcing services.
• The best wireless sensor network for IoT security.
• The best intrusion detection in IoT.
• The importance of big data on the Internet of Things.

Big Data Database Research Topics You Should Try

Big data is broad and interesting. These big data database research topics will put you in a better place in your research. You also get to evaluate the roles of various phenomena.

• The best cloud computing platforms for big data analytics.
• The parallel programming techniques for big data processing.
• The importance of big data models and algorithms in research.
• Evaluate the role of big data analytics for smart healthcare.
• How is big data analytics used in business intelligence?
• The best machine learning methods for big data.
• Evaluate the Hadoop programming in big data analytics.
• What is privacy-preserving to big data analytics?
• The best tools for massive big data processing
• IoT deployment in Governments and Internet service providers.
• How will IoT be used for future internet architectures?
• How does big data close the gap between research and implementation?
• What are the cross-layer attacks in IoT?
• The influence of big data and smart city planning in society.
• Why do you think user access control is important?

Big Data Scala Research Topics

Scala is a programming language that is used in data management. It is closely related to other data programming languages. Here are some of the best scala questions that you can research.

• Which are the most used languages in big data?
• How is scala used in big data research?
• Is scala better than Java in big data?
• How is scala a concise programming language?
• How does the scala language stream process in real-time?
• Which are the various libraries for data science and data analysis?
• How does scala allow imperative programming in data collection?
• Evaluate how scala includes a useful REPL for interaction.
• Evaluate scala’s IDE support.
• The data catalog reference model.
• Evaluate the basics of data management and its influence on research.
• Discuss the behavioral analytics process.
• What can you term as the experience economy?
• The difference between agile data science and scala language.
• Explain the graph analytics process.

Independent Research Topics for Big Data

These independent research topics for big data are based on the various technologies and how they are related. Big data will greatly be important for modern society.

• The biggest investment is in big data analysis.
• How are multi-cloud and hybrid settings deep roots?
• Why do you think machine learning will be in focus for a long while?
• Discuss in-memory computing.
• What is the difference between edge computing and in-memory computing?
• The relation between the Internet of things and big data.
• How will digital transformation make the world a better place?
• How does data analysis help in social network optimization?
• How will complex big data be essential for future enterprises?
• Compare the various big data frameworks.
• The best way to gather and monitor traffic information using the CCTV images
• Evaluate the hierarchical structure of groups and clusters in the decision tree.
• Which are the 3D mapping techniques for live streaming data.
• How does machine learning help to improve data analysis?
• Evaluate DataStream management in task allocation.
• How is big data provisioned through edge computing?
• The model-based clustering of texts.
• The best ways to manage big data.
• The use of machine learning in big data.

Is Your Big Data Thesis Giving You Problems?

These are some of the best topics that you can use to prosper in your studies. Not only are they easy to research but also reflect on real-time issues. Whether in University or college, you need to put enough effort into your studies to prosper. However, if you have time constraints, we can provide professional writing help. Are you looking for online expert writers? Look no further, we will provide quality work at a cheap price.

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List of Best Research and Thesis Topic Ideas for Data Science in 2022

In an era driven by digital and technological transformation, businesses actively seek skilled and talented data science potentials capable of leveraging data insights to enhance business productivity and achieve organizational objectives. In keeping with an increasing demand for data science professionals, universities offer various data science and big data courses to prepare students for the tech industry. Research projects are a crucial part of these programs and a well- executed data science project can make your CV appear more robust and compelling. A  broad range of data science topics exist that offer exciting possibilities for research but choosing data science research topics can be a real challenge for students . After all, a good research project relies first and foremost on data analytics research topics that draw upon both mono-disciplinary and multi-disciplinary research to explore endless possibilities for real –world applications.

As one of the top-most masters and PhD online dissertation writing services , we are geared to assist students in the entire research process right from the initial conception to the final execution to ensure that you have a truly fulfilling and enriching research experience. These resources are also helpful for those students who are taking online classes .

By taking advantage of our best digital marketing research topics in data science you can be assured of producing an innovative research project that will impress your research professors and make a huge difference in attracting the right employers.

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Data science thesis topics

We have compiled a list of data science research topics for students studying data science that can be utilized in data science projects in 2022. our team of professional data experts have brought together master or MBA thesis topics in data science  that cater to core areas  driving the field of data science and big data that will relieve all your research anxieties and  provide a solid grounding for  an interesting research projects . The article will feature data science thesis ideas that can be immensely beneficial for students as they cover a broad research agenda for future data science . These ideas have been drawn from the 8 v’s of big data namely Volume, Value, Veracity, Visualization, Variety, Velocity, Viscosity, and Virility that provide interesting and challenging research areas for prospective researches  in their masters or PhD thesis . Overall, the general big data research topics can be divided into distinct categories to facilitate the research topic selection process.

• Security and privacy issues
• Cloud Computing Platforms for Big Data Adoption and Analytics
• Real-time data analytics for processing of image , video and text
• Modeling uncertainty

How “The Research Guardian” Can Help You A lot!

Our top thesis writing experts are available 24/7 to assist you the right university projects. Whether its critical literature reviews to complete your PhD. or Master Levels thesis.

DATA SCIENCE PHD RESEARCH TOPICS

The article will also guide students engaged in doctoral research by introducing them to an outstanding list of data science thesis topics that can lead to major real-time applications of big data analytics in your research projects.

• Intelligent traffic control ; Gathering and monitoring traffic information using CCTV images.
• Asymmetric protected storage methodology over multi-cloud service providers in Big data.
• Leveraging disseminated data over big data analytics environment.
• Internet of Things.
• Large-scale data system and anomaly detection.

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We offer all –round and superb research services that have a distinguished track record in helping students secure their desired grades in research projects in big data analytics and hence pave the way for a promising career ahead. These are the features that set us apart in the market for research services that effectively deal with all significant issues in your research for.

• Plagiarism –free ; We strictly adhere to a non-plagiarism policy in all our research work to  provide you with well-written, original content  with low similarity index   to maximize  chances of acceptance of your research submissions.
• Publication; We don’t just suggest PhD data science research topics but our PhD consultancy services take your research to the next level by ensuring its publication in well-reputed journals. A PhD thesis is indispensable for a PhD degree and with our premier best PhD thesis services that  tackle all aspects  of research writing and cater to  essential requirements of journals , we will bring you closer to your dream of being a PhD in the field of data analytics.
• Research ethics: Solid research ethics lie at the core of our services where we actively seek to protect the  privacy and confidentiality of  the technical and personal information of our valued customers.
• Research experience: We take pride in our world –class team of computing industry professionals equipped with the expertise and experience to assist in choosing data science research topics and subsequent phases in research including findings solutions, code development and final manuscript writing.
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Now, we’ll proceed to cover specific research problems encompassing both data analytics research topics and big data thesis topics that have applications across multiple domains.

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TheresearchGuardian.com providing expert thesis assistance for university students at any sort of level. Our thesis writing service has been serving students since 2011.

Multi-modal Transfer Learning for Cross-Modal Information Retrieval

Aim and objectives.

The research aims to examine and explore the use of CMR approach in bringing about a flexible retrieval experience by combining data across different modalities to ensure abundant multimedia data.

• Develop methods to enable learning across different modalities in shared cross modal spaces comprising texts and images as well as consider the limitations of existing cross –modal retrieval algorithms.
• Investigate the presence and effects of bias in cross modal transfer learning and suggesting strategies for bias detection and mitigation.
• Develop a tool with query expansion and relevance feedback capabilities to facilitate search and retrieval of multi-modal data.
• Investigate the methods of multi modal learning and elaborate on the importance of multi-modal deep learning to provide a comprehensive learning experience.

The Role of Machine Learning in Facilitating the Implication of the Scientific Computing and Software Engineering

• Evaluate how machine learning leads to improvements in computational APA reference generator tools and thus aids in  the implementation of scientific computing
• Evaluating the effectiveness of machine learning in solving complex problems and improving the efficiency of scientific computing and software engineering processes.
• Assessing the potential benefits and challenges of using machine learning in these fields, including factors such as cost, accuracy, and scalability.
• Examining the ethical and social implications of using machine learning in scientific computing and software engineering, such as issues related to bias, transparency, and accountability.

Trustworthy AI

The research aims to explore the crucial role of data science in advancing scientific goals and solving problems as well as the implications involved in use of AI systems especially with respect to ethical concerns.

• Investigate the value of digital infrastructures  available through open data   in  aiding sharing  and inter linking of data for enhanced global collaborative research efforts
• Provide explanations of the outcomes of a machine learning model  for a meaningful interpretation to build trust among users about the reliability and authenticity of data
• Investigate how formal models can be used to verify and establish the efficacy of the results derived from probabilistic model.
• Review the concept of Trustworthy computing as a relevant framework for addressing the ethical concerns associated with AI systems.

The Implementation of Data Science and their impact on the management environment and sustainability

The aim of the research is to demonstrate how data science and analytics can be leveraged in achieving sustainable development.

• To examine the implementation of data science using data-driven decision-making tools
• To evaluate the impact of modern information technology on management environment and sustainability.
• To examine the use of  data science in achieving more effective and efficient environment management
• Explore how data science and analytics can be used to achieve sustainability goals across three dimensions of economic, social and environmental.

Big data analytics in healthcare systems

The aim of the research is to examine the application of creating smart healthcare systems and   how it can   lead to more efficient, accessible and cost –effective health care.

• Identify the potential Areas or opportunities in big data to transform the healthcare system such as for diagnosis, treatment planning, or drug development.
• Assessing the potential benefits and challenges of using AI and deep learning in healthcare, including factors such as cost, efficiency, and accessibility
• Evaluating the effectiveness of AI and deep learning in improving patient outcomes, such as reducing morbidity and mortality rates, improving accuracy and speed of diagnoses, or reducing medical errors
• Examining the ethical and social implications of using AI and deep learning in healthcare, such as issues related to bias, privacy, and autonomy.

Large-Scale Data-Driven Financial Risk Assessment

The research aims to explore the possibility offered by big data in a consistent and real time assessment of financial risks.

• Investigate how the use of big data can help to identify and forecast risks that can harm a business.
• Categories the types of financial risks faced by companies.
• Describe the importance of financial risk management for companies in business terms.
• Train a machine learning model to classify transactions as fraudulent or genuine.

Scalable Architectures for Parallel Data Processing

Big data has exposed us to an ever –growing volume of data which cannot be handled through traditional data management and analysis systems. This has given rise to the use of scalable system architectures to efficiently process big data and exploit its true value. The research aims to analyses the current state of practice in scalable architectures and identify common patterns and techniques to design scalable architectures for parallel data processing.

• To design and implement a prototype scalable architecture for parallel data processing
• To evaluate the performance and scalability of the prototype architecture using benchmarks and real-world datasets
• To compare the prototype architecture with existing solutions and identify its strengths and weaknesses
• To evaluate the trade-offs and limitations of different scalable architectures for parallel data processing
• To provide recommendations for the use of the prototype architecture in different scenarios, such as batch processing, stream processing, and interactive querying

Robotic manipulation modelling

The aim of this research is to develop and validate a model-based control approach for robotic manipulation of small, precise objects.

• Develop a mathematical model of the robotic system that captures the dynamics of the manipulator and the grasped object.
• Design a control algorithm that uses the developed model to achieve stable and accurate grasping of the object.
• Test the proposed approach in simulation and validate the results through experiments with a physical robotic system.
• Evaluate the performance of the proposed approach in terms of stability, accuracy, and robustness to uncertainties and perturbations.
• Identify potential applications and areas for future work in the field of robotic manipulation for precision tasks.

Big data analytics and its impacts on marketing strategy

The aim of this research is to investigate the impact of big data analytics on marketing strategy and to identify best practices for leveraging this technology to inform decision-making.

• Review the literature on big data analytics and marketing strategy to identify key trends and challenges
• Conduct a case study analysis of companies that have successfully integrated big data analytics into their marketing strategies
• Identify the key factors that contribute to the effectiveness of big data analytics in marketing decision-making
• Develop a framework for integrating big data analytics into marketing strategy.
• Investigate the ethical implications of big data analytics in marketing and suggest best practices for responsible use of this technology.

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Platforms for large scale data computing: big data analysis and acceptance

To investigate the performance and scalability of different large-scale data computing platforms.

• To compare the features and capabilities of different platforms and determine which is most suitable for a given use case.
• To identify best practices for using these platforms, including considerations for data management, security, and cost.
• To explore the potential for integrating these platforms with other technologies and tools for data analysis and visualization.
• To develop case studies or practical examples of how these platforms have been used to solve real-world data analysis challenges.

Distributed data clustering

Distributed data clustering can be a useful approach for analyzing and understanding complex datasets, as it allows for the identification of patterns and relationships that may not be immediately apparent.

To develop and evaluate new algorithms for distributed data clustering that is efficient and scalable.

• To compare the performance and accuracy of different distributed data clustering algorithms on a variety of datasets.
• To investigate the impact of different parameters and settings on the performance of distributed data clustering algorithms.
• To explore the potential for integrating distributed data clustering with other machine learning and data analysis techniques.
• To apply distributed data clustering to real-world problems and evaluate its effectiveness.

Analyzing and predicting urbanization patterns using GIS and data mining techniques".

The aim of this project is to use GIS and data mining techniques to analyze and predict urbanization patterns in a specific region.

• To collect and process relevant data on urbanization patterns, including population density, land use, and infrastructure development, using GIS tools.
• To apply data mining techniques, such as clustering and regression analysis, to identify trends and patterns in the data.
• To use the results of the data analysis to develop a predictive model for urbanization patterns in the region.
• To present the results of the analysis and the predictive model in a clear and visually appealing way, using GIS maps and other visualization techniques.

Use of big data and IOT in the media industry

Big data and the Internet of Things (IoT) are emerging technologies that are transforming the way that information is collected, analyzed, and disseminated in the media sector. The aim of the research is to understand how big data and IoT re used to dictate information flow in the media industry

• Identifying the key ways in which big data and IoT are being used in the media sector, such as for content creation, audience engagement, or advertising.
• Analyzing the benefits and challenges of using big data and IoT in the media industry, including factors such as cost, efficiency, and effectiveness.
• Examining the ethical and social implications of using big data and IoT in the media sector, including issues such as privacy, security, and bias.
• Determining the potential impact of big data and IoT on the media landscape and the role of traditional media in an increasingly digital world.

Exigency computer systems for meteorology and disaster prevention

The research aims to explore the role of exigency computer systems to detect weather and other hazards for disaster prevention and response

• Identifying the key components and features of exigency computer systems for meteorology and disaster prevention, such as data sources, analytics tools, and communication channels.
• Evaluating the effectiveness of exigency computer systems in providing accurate and timely information about weather and other hazards.
• Assessing the impact of exigency computer systems on the ability of decision makers to prepare for and respond to disasters.
• Examining the challenges and limitations of using exigency computer systems, such as the need for reliable data sources, the complexity of the systems, or the potential for human error.

Network security and cryptography

Overall, the goal of research is to improve our understanding of how to protect communication and information in the digital age, and to develop practical solutions for addressing the complex and evolving security challenges faced by individuals, organizations, and societies.

• Developing new algorithms and protocols for securing communication over networks, such as for data confidentiality, data integrity, and authentication
• Investigating the security of existing cryptographic primitives, such as encryption and hashing algorithms, and identifying vulnerabilities that could be exploited by attackers.
• Evaluating the effectiveness of different network security technologies and protocols, such as firewalls, intrusion detection systems, and virtual private networks (VPNs), in protecting against different types of attacks.
• Exploring the use of cryptography in emerging areas, such as cloud computing, the Internet of Things (IoT), and blockchain, and identifying the unique security challenges and opportunities presented by these domains.
• Investigating the trade-offs between security and other factors, such as performance, usability, and cost, and developing strategies for balancing these conflicting priorities.

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Hot Topics in Research Methods: Big Data Analysis

Welcome to the Sage Research Methods Hot Topics page, tied in this edition to the theme of big data analysis. Use the links below to gain access to examples of our various content types. Past editions can be found at the  Hot Topics archive page.

Using Text Mining Methods in Social Science Research

Watch as sociologist Gabe Ignatow discusses text mining and its applications in the social sciences. Real-world examples, resources, and advice are found in this video from Sage's data science and digital methods collection.

'Big Social Science': Doing Big Data in the Social Sciences

Learn about the emergence of a big data approach to social science, as a result of human life becoming ever more quantifiable. In this chapter of the Sage Handbook of Online Research Methods , Jonathan Bright lays out the basic practicalities of large-scale quantitative research and considers its challenges for social scientists.

Gary King on Big Data Analysis

Listen as Gary King, director of Harvard's Institute for Quantitative Social Science, considers the ever-expanding universe of data being generated and captured from daily life activities. He shares experiences from his work studying Chinese censorship of social media and shines light on the changing arenas of political action in an age of data revolution.

Big Data and Financial Crime Research: Methodological Problems

Read about the experience of conducting primary research on a quarter-century of financial crimes activity. Readers will learn of the obstacles and complexities faced by the author when deciding how to code raw data drawn from multiple sources, including tribunal hearings held in multiple languages.

Learn About Logistic Regression in R

Use this interactive dataset —a subset of data from the 2012 Cooperative Congressional National Election Study —to learn the method of logistic regression with the software program R. Logistic regression, or logit, is a widely used method of analysis in social science and is the foundation for more complex methods in big data analytics.

Readers will find a complete introduction to logistic regression with a teaching guide, student guide, and how-to guide for R, alongside a downloadable dataset, codebook, and script files for R and other common software packages.

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7 Key Data Science Trends For 2024-2027

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Here are the 7 fastest-growing data science trends for 2024 and beyond.

We'll also outline how these trends will impact both data scientists’ work and everyday life.

Whether you’re actively involved in the data science community, or just concerned about your data privacy, these are the top trends to monitor.

1. Explosion in deepfake video and audio

Deepfakes use artificial intelligence to manipulate or create content to represent someone else.

Often this is an image or video of one person modified to someone else’s likeness.

But it can be audio too.

Back in 2019, an AI company deepfaked popular podcaster Joe Rogan’s voice so effectively it instantly went viral on social media.

And the tech has only improved since.

There’s huge scope for this technology to be used maliciously.

Another voice deep fake was used to scam a UK-based energy company out of €220,000 .

The CEO believed he was on the phone with a colleague and was told to urgently transfer the money to the bank account of a Hungarian supplier.

In fact, the call had been spoofed with deep fake technology to mimic the man’s voice and “melody”.

In fact, there's growing search interest in a practice known as "voice phishing". Which is essentially the "official" term for the practice.

As well as hoaxes and financial fraud, deepfakes can also be weaponized to discredit business figures and politicians.

Governments are starting to protect against this with legislation and social media regulation.

And with technology that can identify deepfake videos.

But the battle with deepfakes has only just begun.

2. More applications created with Python

Python is the go-to programming language for data analysis.

Why is this?

Because Python has a huge number of free data science libraries such as Pandas and machine learning libraries like Scikit-learn .

It can even be used to develop blockchain applications.

Add to this a friendly learning curve for beginners, and you have a recipe for success.

Python is now ranked as the 3rd most popular language in general by the analyst firm RedMonk.

And the popularity growth trend shows it’s on track to become number 1 within the next three years.

3. Increased demand for End-to-end AI solutions

“Dataiku” searches are up by 146% in 5 years, growing quickly even before Google acquired them.

Enterprise AI company Dataiku is now worth $4.6 billion ( according to TechCrunch ) after Google bought a stake in the company in December 2019.

The AI startup helps enterprise customers clean their large data sets and build machine learning models.

This way, companies like General Electric and Unilever can gain valuable, deep-learning insights from their massive amounts of data.

And automate important data management tasks.

Previously, businesses would have to seek expertise in all the different parts of the process and piece it together themselves.

But Dataiku handles the entire data science cycle from start to finish with a single product.

And because of this, they stand out.

Businesses want end-to-end data science solutions. And startups that provide this will eat the market.

4. Companies hire more data analysts

“Data analyst” searches are up by 265% in 5 years. Interest in this data science role displays hockey stick growth.

Demand for data analysts has shot through the roof over the last few years.

And, thanks largely to data coming in from the Internet of Things (IoT) and advances in cloud computing, global data storage is set to grow from 45 zettabytes to 175 zettabytes by 2025 .

So the need for experts to parse and analyze all of this data is set to rise.

Why are so many data analysts required?

After all, there are plenty of data analytics programs out there that can sort through it all.

And "digital transformation" has supposedly replaced many human-led business tasks.

Sure, machines can help analyze data.

But big data is often extremely messy and lacking in proper structure.

Which is why humans are needed to manually tidy training data before it is ingested by machine learning algorithms.

It’s also increasingly common for data people to be involved on the output end too.

AI-produced results are not always reliable or accurate, so machine learning companies often use humans to clean up the final data.

And write up an analysis of what they find in a way that non-tech stakeholders can understand it.

Amazon's Mechanical Turk is the biggest platform where "Turkers" complete data labeling and cleaning jobs .

The data science and machine learning methods of the 2020s will be less artificial and automated than initially expected.

Augmented intelligence and human-in-the-loop artificial intelligence will likely become a big trend in data science.

5. Data scientists joining Kaggle

Search growth for “Kaggle” has increased by 223% over 5 years. The data science platform has over 5 million users across 194 countries.

Kaggle has grown quickly to become the world's largest data science community.

And with over 8 million users across 194 countries, it’s not slowing down.

Many budding data scientists now start with Kaggle to begin their machine learning journey. 

And post the progress of their machine learning projects in real-time.

Users can even share data sets and enter competitions to solve data science challenges with neural networks.

Or work with other data scientists to build models in Kaggle’s web-based data science workbench.

Kaggle competitions can have hefty prize sums.

Academic papers have actually been published based on Kaggle competition findings too.

Successful projects from Kaggle’s hundreds of competitions will likely continue to push boundaries in the field of data science.

6. Increased interest in consumer data protection

“Data privacy”  has seen a search growth of 441% over the last 10 years. People are now searching about their data privacy in greater numbers by the month.

Consumer awareness about data privacy rose in the wake of the Cambridge Analytica scandal .

In fact, CIGI-Ipsos found that more than half of all consumers became more interested in data privacy in the year following the revelations.

Platforms like Facebook and Google, which previously harvested and shared user data freely, have since faced legal backlash and public scrutiny.

Facebook now has a large guide on privacy basics and what it does with your data.

This broader data privacy trend means that large data sets will soon be walled off and harder to come by.

Businesses and data scientists will need to navigate legislation such as the California Consumer Privacy Act which came into effect at the start of 2020.

And this could become a bane for data science when it comes to the future acquisition and use of consumer data.

7. AI devs combating adversarial machine learning

“Adversarial machine learning” searches have grown significantly in the last decade by 2,500%.

Adversarial machine learning is where an attacker inputs data into a machine learning model with the aim of causing mistakes.

Essentially, it is an optical illusion designed for a machine.

Adversarial Fashion's clothing lines trick machine-learning models with bold patterns and lettering.

Anti-surveillance clothing takes this approach to the masses.

They’re specifically designed to confuse face detection algorithms with bold shapes and patterns.

According to a Northeastern University study , this clothing can help prevent individuals' automated tracking via surveillance cameras.

Data scientists will need to defend against adversarial inputs like this. And provide trick examples for models to train on so as not to be fooled.

Adversarial training measures for models like this will become essential in the next decade.

Wrapping Up

Those are the 7 biggest data science trends over the next 3-4 years.

Data science, like any science, is changing by the day. From data governance to deepfake technology, the data science industry is set for some major shakeups.

Hopefully keeping tabs on these trends will help you stay one step ahead.

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Insight and Analysis for the CTO

Hot topics and emerging trends in data science

We gauged the perspectives of experts in data science, asking them about the biggest emerging trends in data science.

As one of the fastest evolving areas of tech, data science has seen a rise up the corporate agenda as less and less leaders base business decisions on guess work. With added capabilities such as artificial intelligence (AI) and the edge complementing the work of data scientists , the field is becoming more accessible to employees, but this still requires training of data skills, on the most part. In this article, we explore some key emerging trends in data science, as believed by experts in the field.

Increased involvement of AI and ML

Firstly, it’s believed that the involvement of AI and machine learning (ML) will increase further, and enable more industries to become truly data-centric.

“As businesses start to see the benefits of artificial intelligence and machine learning enabled platforms, they will invest in these technologies further,” said Douggie Melville-Clarke , head of data science at Duco .

“In fact, the Duco State of Reconciliation report – which surveyed 300 heads of global reconciliation utilities, including chief operating officers, heads of financial control and heads of finance transformation – found that 42% of those surveyed will investigate the use of more machine learning in 2021 for the purposes of intelligent data automation.”

Data science in insurance

Melville-Clarke went on to cite the insurance industry, often perceived as a sector that’s had difficulty innovating due to high levels of regulation, as an example for future success when it comes to data science.

He explained: “The insurance industry, for example, has already embraced automation for processes such as underwriting and quote generation. But the more valuable use of artificial intelligence and machine learning is to increase your service and market share through uses like constrained customisation.

“Personalisation is one of the key ways that banks and insurance companies can differentiate themselves, but without machine learning this can be a lengthy and expensive process.

“Machine learning can help these industries tailor their products to meet the individual consumers’ needs in a much more cost-effective way, bettering the customer experience and increasing customisation.”

Digital transformation in the insurance sector: cultural and organisational Johanna Von Geyr, partner and EMEA lead banking, financial services & insurance at ISG, explores digital transformation in the insurance sector. Read here

The evolution of hyperautomation

Along with rising use of AI and ML models, organisations have been combining AI with robotic process automation (RPA), to reduce operational costs through automating decision making. This trend, known as hyperautomation , is predicted to help companies to continue innovating fast in a post-COVID environment in the next few years.

“In many ways, this isn’t a new concept — the key goal of enterprise investment in data science for the past decade has been to automate decision-making processes based on AI and ML,” explained Rich Pugh , co-founder and chief data scientist at Mango Solutions , an Ascent company.

“What is new here is that hyperautomation is underpinned by an ‘RPA-first’ approach that can turbocharge process automation and drive increased collaboration across analytic and IT functions.

“Business leaders need to focus on how to harness enterprise automation and continuous intelligence to elevate the customer experience. Whether that is embedding intelligent thinking into the processes that will drive more informed decision making, such as deploying automation around pricing decisions to deliver a more efficient and personalised service, or leveraging richer real-time customer insights in conjunction with automation to execute highly relevant offers and new services at speed.

“Embarking on the hyperautomation journey begins with achieving some realistic and measurable future outcomes. Specifically, this should include aiming for high-value processes, focusing on automation and change, and initiating a structure to gather the data that will enable future success.”

SaaS and self-service

Dan Sommer , senior director at Qlik , identified software-as-a-service (SaaS) and a self-service approach among users, along with a shift in advanced analytics , as a notable emerging trend in data science.

“To those in the industry, it’s clear that SaaS will be everyone’s new best friend – with a greater migration of databases and applications from on premise to cloud environments,” said Sommer.

“Cloud computing has helped many businesses, organisations, and schools to keep the lights on in virtual environments – and we’re now going to see an enhanced focus on SaaS as hybrid operations look set to remain.

“In addition, we’ll see self-service evolving to self-sufficiency when it comes to effectively using data and analytics. Empowering users to access data, insights and business logic earlier and more intuitively will enable the move from visualisation self-service to data self-sufficiency in the near future.

“Finally, advanced analytics need to look different. In uncertain times, we can no longer count on backward-looking data to build a comprehensive model of the future. Instead, we need to give particular focus to, rather than exclude outliers – and this will define how we tackle threats going forward too.”

The value of SaaS offerings in a post-Covid business environment Jonathan Bowl, AVP & general manager, UK, Ireland & Nordics at Commvault, explores the value of SaaS offerings in a post-COVID business environment. Read here

Data fabric

With employees gradually becoming more comfortable with using data science tools to make decisions, while aided by automation and machine intelligence, a concept that’s materialised as a hot topic for the next stage of development is the concept of ‘data fabric’.

Trevor Morgan , product manager at comforte AG , explained: “A data fabric is more of an architectural overlay on top of massive enterprise data ecosystems. The data fabric unifies disparate data sources and streams across many different topologies (both on-premise and in the cloud), and provides multiple ways of accessing and working with that data for organisational personnel, and with the larger fabric as a contextual backdrop.

“For large enterprises that are moving with hyper-agility while working with multiple or many Big Data environments, data fabric technology will provide the means to harness all this information and make it workable throughout the enterprise.”

New career paths and roles

Another important trend to consider regarding the future of data science is the new career paths and jobs that are set to emerge in the coming years.

“According to the World Economic Forum ( WEF )’s Future of Job’s Report 2020 , 94% of UK employers plan to hire new permanent staff with skills relevant to new technologies and expect existing employees to pick up new skills on the job,” said Anthony Tattersall , vice-president, enterprise, EMEA at Coursera .

“What’s more, WEF’s top emerging jobs in the UK — data scientists, AI and machine learning specialists, big data and Internet of Things — all call for skills of this nature.

“We therefore envision access to a variety of job-relevant credentials, including a path to entry-level digital jobs, will be key to reskilling at scale and accelerating economic recovery in the years ahead.”

The ‘Industrial Data Scientist’

In regards to new roles to emerge in data science, Adi Pendyala , senior director at Aspen Technology , predicts the emergence of the ‘Industrial Data Scientist’: “These scientists will be a new breed of tech-driven, data-empowered domain experts with access to more industrial data than ever before, as well as the accessible AI/ML and analytics tools needed to translate that information into actionable intelligence across the enterprise.

“Industrial data scientists will represent a new kind of crossroads between our traditional understanding of citizen data scientists and industrial domain experts: workers who possess the domain expertise of the latter but are increasingly shifting over to the data realm occupied by the former.”

How to embark on a data science career To kick off our Data Science month, this article will explore how you can embark on a career in data science, and the key factors to consider. Read here

Many organisations are being impacted by a shortage of data scientists in proportion to demand, but Julien Alteirac , regional vice-president, UK&I at Snowflake , believes that new tools, powered by ML, could help to mitigate this skills gap in the near future.

“When it comes to analysing data, most organisations employ an abundance of data analysts and a limited number of data scientists, due in large part to the limited supply and high costs associated with data scientists,” said Alteirac.

“Since analysts lack the data science expertise required to build ML models, data scientists have become a potential bottleneck for broadening the use of ML. However, new and improved ML tools which are more user-friendly are helping organisations realise the power of data science.

“Data analysts are empowered with access to powerful models without needing to manually build them. Specifically, automated machine learning ( AutoML ) and AI services via APIs are removing the need to manually prepare data and then build and train models. AutoML tools and AI services lower the barrier to entry for ML, so almost anyone will now be able to access and use data science without requiring an academic background.”

Aaron Hurst

Aaron Hurst is Information Age's senior reporter, providing news and features around the hottest trends across the tech industry. More by Aaron Hurst

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Artificial intelligence and data science became front-page news in 2023. The rise of generative AI, of course, drove this dramatic surge in visibility. So, what might happen in the field in 2024 that will keep it on the front page? And how will these trends really affect businesses?

During the past several months, we’ve conducted three surveys of data and technology executives. Two involved MIT’s Chief Data Officer and Information Quality Symposium attendees — one sponsored by Amazon Web Services (AWS) and another by Thoughtworks . The third survey was conducted by Wavestone , formerly NewVantage Partners, whose annual surveys we’ve written about in the past . In total, the new surveys involved more than 500 senior executives, perhaps with some overlap in participation.

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Surveys don’t predict the future, but they do suggest what those people closest to companies’ data science and AI strategies and projects are thinking and doing. According to those data executives, here are the top five developing issues that deserve your close attention:

1. Generative AI sparkles but needs to deliver value.

As we noted, generative AI has captured a massive amount of business and consumer attention. But is it really delivering economic value to the organizations that adopt it? The survey results suggest that although excitement about the technology is very high , value has largely not yet been delivered. Large percentages of respondents believe that generative AI has the potential to be transformational; 80% of respondents to the AWS survey said they believe it will transform their organizations, and 64% in the Wavestone survey said it is the most transformational technology in a generation. A large majority of survey takers are also increasing investment in the technology. However, most companies are still just experimenting, either at the individual or departmental level. Only 6% of companies in the AWS survey had any production application of generative AI, and only 5% in the Wavestone survey had any production deployment at scale.

Surveys suggest that though excitement about generative AI is very high, value has largely not yet been delivered.

Production deployments of generative AI will, of course, require more investment and organizational change, not just experiments. Business processes will need to be redesigned, and employees will need to be reskilled (or, probably in only a few cases, replaced by generative AI systems). The new AI capabilities will need to be integrated into the existing technology infrastructure.

Perhaps the most important change will involve data — curating unstructured content, improving data quality, and integrating diverse sources. In the AWS survey, 93% of respondents agreed that data strategy is critical to getting value from generative AI, but 57% had made no changes to their data thus far.

2. Data science is shifting from artisanal to industrial.

Companies feel the need to accelerate the production of data science models . What was once an artisanal activity is becoming more industrialized. Companies are investing in platforms, processes and methodologies, feature stores, machine learning operations (MLOps) systems, and other tools to increase productivity and deployment rates. MLOps systems monitor the status of machine learning models and detect whether they are still predicting accurately. If they’re not, the models might need to be retrained with new data.

Producing data models — once an artisanal activity — is becoming more industrialized.

Most of these capabilities come from external vendors, but some organizations are now developing their own platforms. Although automation (including automated machine learning tools, which we discuss below) is helping to increase productivity and enable broader data science participation, the greatest boon to data science productivity is probably the reuse of existing data sets, features or variables, and even entire models.

3. Two versions of data products will dominate.

In the Thoughtworks survey, 80% of data and technology leaders said that their organizations were using or considering the use of data products and data product management. By data product , we mean packaging data, analytics, and AI in a software product offering, for internal or external customers. It’s managed from conception to deployment (and ongoing improvement) by data product managers. Examples of data products include recommendation systems that guide customers on what products to buy next and pricing optimization systems for sales teams.

But organizations view data products in two different ways. Just under half (48%) of respondents said that they include analytics and AI capabilities in the concept of data products. Some 30% view analytics and AI as separate from data products and presumably reserve that term for reusable data assets alone. Just 16% say they don’t think of analytics and AI in a product context at all.

We have a slight preference for a definition of data products that includes analytics and AI, since that is the way data is made useful. But all that really matters is that an organization is consistent in how it defines and discusses data products. If an organization prefers a combination of “data products” and “analytics and AI products,” that can work well too, and that definition preserves many of the positive aspects of product management. But without clarity on the definition, organizations could become confused about just what product developers are supposed to deliver.

4. Data scientists will become less sexy.

Data scientists, who have been called “ unicorns ” and the holders of the “ sexiest job of the 21st century ” because of their ability to make all aspects of data science projects successful, have seen their star power recede. A number of changes in data science are producing alternative approaches to managing important pieces of the work. One such change is the proliferation of related roles that can address pieces of the data science problem. This expanding set of professionals includes data engineers to wrangle data, machine learning engineers to scale and integrate the models, translators and connectors to work with business stakeholders, and data product managers to oversee the entire initiative.

Another factor reducing the demand for professional data scientists is the rise of citizen data science , wherein quantitatively savvy businesspeople create models or algorithms themselves. These individuals can use AutoML, or automated machine learning tools, to do much of the heavy lifting. Even more helpful to citizens is the modeling capability available in ChatGPT called Advanced Data Analysis . With a very short prompt and an uploaded data set, it can handle virtually every stage of the model creation process and explain its actions.

Of course, there are still many aspects of data science that do require professional data scientists. Developing entirely new algorithms or interpreting how complex models work, for example, are tasks that haven’t gone away. The role will still be necessary but perhaps not as much as it was previously — and without the same degree of power and shimmer.

5. Data, analytics, and AI leaders are becoming less independent.

This past year, we began to notice that increasing numbers of organizations were cutting back on the proliferation of technology and data “chiefs,” including chief data and analytics officers (and sometimes chief AI officers). That CDO/CDAO role, while becoming more common in companies, has long been characterized by short tenures and confusion about the responsibilities. We’re not seeing the functions performed by data and analytics executives go away; rather, they’re increasingly being subsumed within a broader set of technology, data, and digital transformation functions managed by a “supertech leader” who usually reports to the CEO. Titles for this role include chief information officer, chief information and technology officer, and chief digital and technology officer; real-world examples include Sastry Durvasula at TIAA, Sean McCormack at First Group, and Mojgan Lefebvre at Travelers.

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This evolution in C-suite roles was a primary focus of the Thoughtworks survey, and 87% of respondents (primarily data leaders but some technology executives as well) agreed that people in their organizations are either completely, to a large degree, or somewhat confused about where to turn for data- and technology-oriented services and issues. Many C-level executives said that collaboration with other tech-oriented leaders within their own organizations is relatively low, and 79% agreed that their organization had been hindered in the past by a lack of collaboration.

We believe that in 2024, we’ll see more of these overarching tech leaders who have all the capabilities to create value from the data and technology professionals reporting to them. They’ll still have to emphasize analytics and AI because that’s how organizations make sense of data and create value with it for employees and customers. Most importantly, these leaders will need to be highly business-oriented, able to debate strategy with their senior management colleagues, and able to translate it into systems and insights that make that strategy a reality.

About the Authors

Thomas H. Davenport ( @tdav ) is the President’s Distinguished Professor of Information Technology and Management at Babson College, a fellow of the MIT Initiative on the Digital Economy, and senior adviser to the Deloitte Chief Data and Analytics Officer Program. He is coauthor of All in on AI: How Smart Companies Win Big With Artificial Intelligence (HBR Press, 2023) and Working With AI: Real Stories of Human-Machine Collaboration (MIT Press, 2022). Randy Bean ( @randybeannvp ) is an industry thought leader, author, founder, and CEO and currently serves as innovation fellow, data strategy, for global consultancy Wavestone. He is the author of Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI (Wiley, 2021).

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20 Data Science Topics and Areas

It is no doubt that data science topics and areas are some of the hottest business points today.

We collected some basic and advanced topics in data science to give you ideas on where to master your skills.

In today’s landscape, businesses are investing in corporate data science training to enhance their employees’ data science capabilities.

Data science topics also are hot subjects you can use as directions to prepare yourself for data science job interview questions.

1. The core of data mining process

This is an example of a wide data science topic.

What is it?

Data mining is an iterative process that involves discovering patterns in large data sets. It includes methods and techniques such as machine learning, statistics, database systems and etc.

The two main data mining objectives are to find out patterns and establish trends and relationship in a dataset in order to solve problems.

The general stages of the data mining process are: problem definition, data exploration, data preparation, modeling, evaluation, and deployment.

Core terms related to data mining are classification, predictions, association rules, data reduction, data exploration, supervised and unsupervised learning, datasets organization, sampling from datasets, building a model and etc.

2. Data visualization

Data visualization is the presentation of data in a graphical format.

It enables decision-makers of all levels to see data and analytics presented visually, so they can identify valuable patterns or trends.

Data visualization is another broad subject that covers the understanding and use of basic types of graphs (such as line graphs, bar graphs, scatter plots , histograms, box and whisker plots , heatmaps.

You cannot go without these graphs. In addition, here you need to learn about multidimensional variables with adding variables and using colors, size, shapes, animations.

Manipulation also plays a role here. You should be able to rascal, zoom, filter, aggregate data.

Using some specialized visualizations such as map charts and tree maps is a hot skill too.

3. Dimension reduction methods and techniques

Dimension Reduction process involves converting a data set with vast dimensions into a dataset with lesser dimensions ensuring that it provides similar information in short.

In other words, dimensionality reduction consists of series of techniques and methods in machine learning and statistics to decrease the number of random variables.

There are so many methods and techniques to perform dimension reduction.

The most popular of them are Missing Values, Low Variance, Decision Trees, Random Forest, High Correlation, Factor Analysis, Principal Component Analysis, Backward Feature Elimination.

4. Classification

Classification is a core data mining technique for assigning categories to a set of data.

The purpose is to support gathering accurate analysis and predictions from the data.

Classification is one of the key methods for making the analysis of a large amount of datasets effective.

Classification is one of the hottest data science topics too. A data scientist should know how to use classification algorithms to solve different business problems.

This includes knowing how to define a classification problem, explore data with univariate and bivariate visualization, extract and prepare data, build classification models, evaluate models, and etc. Linear and non-linear classifiers are some of the key terms here.

5. Simple and multiple linear regression

Linear regression models are among the basic statistical models for studying relationships between an independent variable X and Y dependent variable.

It is a mathematical modeling which allows you to make predictions and prognosis for the value of Y depending on the different values of X.

There are two main types of linear regression: simple linear regression models and multiple linear regression models.

Key points here are terms such as correlation coefficient, regression line, residual plot, linear regression equation and etc. For the beginning, see some simple linear regression examples .

6. K-nearest neighbor (k-NN) 

N-nearest-neighbor is a data classification algorithm that evaluates the likelihood a data point to be a member of one group. It depends on how near the data point is to that group.

As one of the key non-parametric method used for regression and classification, k-NN can be classified as one of the best data science topics ever.

Determining neighbors, using classification rules, choosing k are a few of the skills a data scientist should have. K-nearest neighbor is also one of the key text mining and anomaly detection algorithms .

7. Naive Bayes

Naive Bayes is a collection of classification algorithms which are based on the so-called Bayes Theorem .

Widely used in Machine Learning, Naive Bayes has some crucial applications such as spam detection and document classification.

There are different Naive Bayes variations. The most popular of them are the Multinomial Naive Bayes, Bernoulli Naive Bayes, and Binarized Multinomial Naive Bayes.

8. Classification and regression trees (CART)

When it comes to algorithms for predictive modeling machine learning, decision trees algorithms have a vital role.

The decision tree is one of the most popular predictive modeling approaches used in data mining, statistics and machine learning that builds classification or regression models in the shape of a tree (that’s why they are also known as regression and classification trees).

They work for both categorical data and continuous data.

Some terms and topics you should master in this field involve CART decision tree methodology, classification trees, regression trees, interactive dihotomiser, C4.5, C5.5, decision stump, conditional decision tree, M5, and etc.

9. Logistic regression

Logistic regression is one of the oldest data science topics and areas and as the linear regression, it studies the relationship between dependable and independent variable.

However, we use logistic regression analysis where the dependent variable is dichotomous (binary).

You will face terms such as sigmoid function, S-shaped curve, multiple logistic regression with categorical explanatory variables, multiple binary logistic regression with a combination of categorical and continuous predictors and etc.

10. Neural Networks

Neural Networks act as a total hit in the machine learning nowadays. Neural networks (also known as artificial neural networks) are systems of hardware and/or software that mimic the human brain neurons operation.

The above were some of the basic data science topics. Here is a list of more interesting and advanced topics:

11. Discriminant analysis

12. Association rules

13. Cluster analysis

14. Time series

15. Regression-based forecasting

16. Smoothing methods

17. Time stamps and financial modeling

18. Fraud detection

19. Data engineering – Hadoop, MapReduce, Pregel.

20. GIS and spatial data

For continuous learning, explore  online data science  courses for mastering these topics.

What are your favorite data science topics? Share your thoughts in the comment field above.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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12 Data Science Projects for Beginners and Experts

Data science is a profession that requires a variety of scientific tools, processes, algorithms and knowledge extraction systems that are used to identify meaningful patterns in structured and unstructured data alike.

If you fancy data science and are eager to get a solid grip on the technology, now is as good a time as ever to hone your skills to comprehend and manage the upcoming challenges facing the profession. The purpose behind this article is to share some practicable ideas for your next project, which will not only boost your confidence in data science but also play a critical part in enhancing your skills .

12 Data Science Projects to Experiment With

• Building chatbots.
• Credit card fraud detection.
• Fake news detection.
• Forest fire prediction.
• Classifying breast cancer.
• Driver drowsiness detection.
• Recommender systems.
• Sentiment analysis.
• Exploratory data analysis.
• Gender detection and age detection.
• Recognizing speech emotion.
• Customer segmentation.

Top Data Science Projects

Understanding data science can be quite confusing at first, but with consistent practice, you’ll start to grasp the various notions and terminologies in the subject. The best way to gain more exposure to data science apart from going through the literature is to take on some helpful projects that will upskill you and make your resume more impressive.

In this section, we’ll share a handful of fun and interesting project ideas with you spread across all skill levels ranging from beginners to intermediate to veterans.

More on Data Science: How to Build Optical Character Recognition (OCR) in Python

1. Building Chatbots

• Language: Python
• Data set: Intents JSON file
• Source code: Build Your First Python Chatbot Project

Chatbots play a pivotal role for businesses as they can effortlessly   without any slowdown. They automate a majority of the customer service process,  single-handedly reducing the customer service workload. The chatbots utilize a variety of techniques backed with artificial intelligence, machine learning and data science.

Chatbots analyze the input from the customer and reply with an appropriate mapped response. To train the chatbot, you can use recurrent neural networks with the intents JSON dataset , while the implementation can be handled using Python . Whether you want your chatbot to be domain-specific or open-domain depends on its purpose. As these chatbots process more interactions, their intelligence and accuracy also increase.

2. Credit Card Fraud Detection

• Language: R or Python
• Data set: Data on the transaction of credit cards is used here as a data set.
• Source code: Credit Card Fraud Detection Using Python

Credit card fraud is more common than you think, and lately, they’ve been on the rise. We’re on the path to cross a billion credit card users by the end of 2022. But thanks to the innovations in technologies like artificial intelligence, machine learning and data science, credit card companies have been able to successfully identify and intercept these frauds with sufficient accuracy.

Simply put, the idea behind this is to analyze the customer’s usual spending behavior, including mapping the location of those spendings to identify the fraudulent transactions from the non-fraudulent ones. For this project, you can use either R or Python with the customer’s transaction history as the data set and ingest it into decision trees , artificial neural networks , and logistic regression . As you feed more data to your system, you should be able to increase its overall accuracy.

3. Fake News Detection

• Data set/Packages: news.csv
• Source code: Detecting Fake News

Fake news needs no introduction. In today’s connected world, it’s become ridiculously easy to share fake news over the internet. Every once in a while, you’ll see false information being spread online from unauthorized sources that not only cause problems to the people targeted but also has the potential to cause widespread panic and even violence.

To curb the spread of fake news, it’s crucial to identify the authenticity of information, which can be done using this data science project. You can use Python and build a model with TfidfVectorizer and PassiveAggressiveClassifier to separate the real news from the fake one. Some Python libraries best suited for this project are pandas, NumPy and scikit-learn . For the data set, you can use News.csv.

4. Forest Fire Prediction

Building a forest fire and wildfire prediction system is another good use of data science’s capabilities. A wildfire or forest fire is an uncontrolled fire in a forest. Every forest wildfire has caused an immense amount of damage to  nature, animal habitats and human property.

To control and even predict the chaotic nature of wildfires, you can use k-means clustering to identify major fire hotspots and their severity. This could be useful in properly allocating resources. You can also make use of meteorological data to find common periods and seasons for wildfires to increase your model’s accuracy.

More on Data Science: K-Nearest Neighbor Algorithm: An Introduction

5. Classifying Breast Cancer

• Data set: IDC (Invasive Ductal Carcinoma)
• Source code: Breast Cancer Classification with Deep Learning

If you’re looking for a healthcare project to add to your portfolio, you can try building a breast cancer detection system using Python. Breast cancer cases have been on the rise, and the best possible way to fight breast cancer is to identify it at an early stage and take appropriate preventive measures.

To build a system with Python, you can use the invasive ductal carcinoma (IDC) data set, which contains histology images for cancer-inducing malignant cells. You can train your model with it, too. For this project, you’ll find convolutional neural networks are better suited for the task, and as for Python libraries, you can use NumPy , OpenCV , TensorFlow , Keras, scikit-learn and Matplotlib .

6. Driver Drowsiness Detection

• Source code: Driver Drowsiness Detection System with OpenCV & Keras

Road accidents take many lives every year, and one of the root causes of road accidents is sleepy drivers. One of the best ways to prevent this is to implement a drowsiness detection system.

A driver drowsiness detection system that constantly assesses the driver’s eyes and alerts them with alarms if the system detects frequently closing eyes is yet another project that has the potential to save many lives .

A webcam is a must for this project in order for  the system to periodically monitor the driver’s eyes. This Python project will require a deep learning model and libraries such as OpenCV , TensorFlow , Pygame , and Keras .

More on Data Science: 8 Data Visualization Tools That Every Data Scientist Should Know

7. Recommender Systems (Movie/Web Show Recommendation)

• Language: R
• Data set: MovieLens
• Packages: Recommenderlab, ggplot2, data.table, reshape2
• Source code: Movie Recommendation System Project in R

Have you ever wondered how media platforms like YouTube, Netflix and others recommend what to watch next? They use a tool called the recommender/recommendation system . It takes several metrics into consideration, such as age, previously watched shows, most-watched genre and watch frequency, and it feeds them into a machine learning model that then generates what the user might like to watch next.

Based on your preferences and input data, you can try to build either a content-based recommendation system or a collaborative filtering recommendation system. For this project, you can use R with the MovieLens data set, which covers ratings for over 58,000 movies. As for the packages, you can use recommenderlab , ggplot2 , reshap2 and data.table.

8. Sentiment Analysis

• Data set: janeaustenR
• Source code: Sentiment Analysis Project in R

Also known as opinion mining, sentiment analysis is a tool backed by artificial intelligence, which essentially allows you to identify, gather and analyze people’s opinions about a subject or a product. These opinions could be from a variety of sources, including online reviews or survey responses, and could span a range of emotions such as happy, angry, positive, love, negative, excitement and more.

Modern data-driven companies benefit the most from a sentiment analysis tool as it gives them the critical insight into the people’s reactions to the dry run of a new product launch or a change in business strategy. To build a system like this, you could use R with janeaustenR’s data set along with the tidytext package .

9. Exploratory Data Analysis

• Packages: pandas, NumPy, seaborn, and matplotlib
• Source code: Exploratory data analysis in Python

Data analysis starts with exploratory data analysis (EDA). It plays a key role in the data analysis process as it helps you make sense of your data and often involves visualizing them for better exploration. For visualization , you can pick from a range of options, including histograms, scatterplots or heat maps. EDA can also expose unexpected results and outliers in your data. Once you have identified the patterns and derived the necessary insights from your data, you are good to go.

A project of this scale can easily be done with Python, and for the packages, you can use pandas, NumPy, seaborn and matplotlib.

A great source for EDA data sets is the IBM Analytics Community .

10. Gender Detection and Age Prediction

• Data set: Adience
• Packages: OpenCV
• Source code: OpenCV Age Detection with Deep Learning

Identified as a classification problem, this gender detection and age prediction project will put both your machine learning and computer vision skills to the test. The goal is to build a system that takes a person’s image and tries to identify their age and gender.

For this project, you can implement convolutional neural networks and use Python with the OpenCV package . You can grab the Adience dataset for this project. Factors such as makeup, lighting and facial expressions will make this challenging and try to throw your model off, so keep that in mind.

11. Recognizing Speech Emotions

• Data set: RAVDESS
• Packages: Librosa, Soundfile, NumPy, Sklearn, Pyaudio
• Source code: Speech Emotion Recognition with librosa

Speech is one of the most fundamental ways of expressing ourselves, and it contains a variety of emotions, such as calmness, anger, joy and excitement, to name a few. By analyzing the emotions behind speech, it’s possible to use this information to restructure our actions,  services and even products, to offer a more personalized service to specific individuals.

This project involves identifying and extracting emotions from multiple sound files containing human speech. To make something like this in Python, you can use the Librosa , SoundFile , NumPy, Scikit-learn, and PyAaudio packages. For the data set, you can use the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) , which contains over 7300 files.

12. Customer Segmentation

• Source code: Customer Segmentation using Machine Learning

Modern businesses strive by delivering highly personalized services to their customers, which would not be possible without some form of customer categorization or segmentation. In doing so, organizations can easily structure their services and products around their customers while targeting them to drive more revenue.

For this project, you will use unsupervised learning to group your customers into clusters based on individual aspects such as age, gender, region, interests, and so on. K-means clustering or hierarchical clustering are suitable here, but you can also experiment with fuzzy clustering or density-based clustering methods. You can use the Mall_Customers data set as sample data.

More Data Science Project Ideas to Build

• Visualizing climate change.
• Uber’s pickup analysis.
• Web traffic forecasting using time series.
• Impact of Climate Change On Global Food Supply.
• Detecting Parkinson’s disease.
• Pokemon data exploration.
• Earth surface temperature visualization.
• Brain tumor detection with data science.
• Predictive policing.

Throughout this article, we’ve covered 12 fun and handy data science project ideas for you to try out. Each will help you understand the basics of data science technology. As one of the hottest, in-demand professions in the industry, the future of data science holds many promises. But to make the most out of the upcoming opportunities, you need to be prepared to take on the challenges it brings.

Frequently Asked Questions

What projects can be done in data science.

• Build a chatbot using Python.
• Create a movie recommendation system using R.
• Detect credit card fraud using R or Python.

How do I start a data science project?

To start a data science project, first decide what sort of data science project you want to undertake, such as data cleaning, data analysis or data visualization. Then, find a good dataset on a website like data.world or data.gov. From there, you can analyze the data and communicate your results.

How long does a data science project take to complete?

Data science projects vary in length and depend on several variables like the data source, the complexity of the problem you’re trying to solve and your skill level. It could take a few hours or several months.

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Top 7 Data Science Trends of 2024 and Beyond

Home Blog Data Science Top 7 Data Science Trends of 2024 and Beyond

With the advent of new technologies, businesses are becoming more productive and increasing their return on investment. Today's trends include data analytics, artificial intelligence, big data, and data science. Business organizations are adopting data-driven models to simplify their processes and make decisions based on the insights derived from data analytics.  

The pandemic disrupted industries worldwide, so SMEs and large companies had to adapt quickly. Data analytics and data science investments increased as a result, and almost every organization relies heavily on data. This article discusses the  latest trends in data science, data science industry trends  and the importance of data analytics.  

You can check out the  Data Science Bootcamp fee  to have a fair idea about the fee structure and other course details if you want to learn more about data science.  

What  i s Data  Science ?  

Data science is the process of analyzing data to extract meaningful insights. The data from which these insights are extracted can come from various sources, including databases, business transactions, sensors, and more. As a result, it's a fast-growing field with close to many job options.  

Data Analytics: Overview

Data analytics is the process of analyzing raw data to derive conclusions. Businesses can optimize their performance, be more efficient, maximize profits, or make more strategic decisions with the help of data analytics. Automating data analytics techniques and processes has led to the development of mechanical methods and algorithms used over raw data.   

Different approaches to analyzing data

• Descriptive Analytics : Describing what happened   
• Diagnostic Analytics: Diagnosing what happened   
• Predictive analytics: Predicting what will happen and   
• Prescriptive analytics: Prescribing what should be done  

Data analysts use spreadsheets, data mining programs, Data visualization tools, or open-source languages to manipulate the most data.  Trend analysis in data science  is a technical analysis technique that attempts to forecast future stock price movements using recently observed trend data.  

Top  data science trends in 2024  include:  

• The boom in cloud migration  
• Growth of predictive analytics  
• Cloud-native solutions will become a must-have  
• Augmented Consumer Interfaces  
• Better data regulation  
• AI as a Service  

 Read on to learn the  trends in data analytics .  

The Top Data Science Trends in 2024  and Beyond  

Listed below are some of the top  data science trends  in 2024. These are some of the  trends in data science examples:  

1.  TinyML  and Small Data  

Big Data is a term used to describe the rapid growth of digital data we create, collect, and analyze. The ML algorithms we use to process the data are also quite large; it's not just big data. It has approximately 175 billion parameters, making it the most extensive and complex system capable of simulating human language. It is one of the  data science future trends .  

It may be fine if you're working with cloud-based systems with limitless bandwidth, but that won't cover all the use cases where ML can be helpful. Hence, "small data" has evolved as a means of processing data quickly and cognitively in time-sensitive, bandwidth-constrained situations. There is a close connection between edge computing and this concept. When trying to avoid a traffic collision in an emergency, self-driving cars cannot rely on a centralized cloud server to send and receive data.   

TinyML algorithms are designed to consume the least amount of space possible and run on low-powered hardware. All kinds of embedded systems will use in 2024, from home appliances to wearables, cars, agricultural machinery, and industrial equipment, making them better and more valuable.  

Applications of TinyML:  

• object recognition and classification  
• gesture recognition  
• keyword spotting  
• machine monitoring  
• audio detection  

2.   D ata-Driven Consumer Experience  

It constitutes one of the  new trends in data science . The idea is that businesses use the data to provide increasingly valuable, worthwhile, or enjoyable experiences. The software could be more user-friendly, have less time waiting on hold, be transferred between departments when contacting customer service, and reduce friction and hassle in e-commerce.  

As the interactions with businesses become increasingly digital - from AI chatbots to Amazon's cashier-less convenience stores - this can measure and analyze every aspect of the exchanges to find ways to improve processes or make them more enjoyable. As a result, businesses have begun to offer goods and services that are more personalized. Companies began investing and innovating in online retail technology because of the pandemic, trying to replace the hands-on, tactile experiences of brick-and-mortar shopping. In 2024, many people in data science will focus on finding new ways to leverage this customer data to create better and unique customer service and experiences.  

3.   Convergence  

In today's digital world, AI, cloud computing, the internet of things (IoT), and superfast networks such as 5G are the cornerstones, and data is the fuel that drives them all. These technologies are some of the  data science latest trends . Together, these technologies enable much more than they can do separately. 

Smart homes, smart factories, and smart cities can now be created by leveraging artificial intelligence, enabling  IoT  devices to act as bright as possible without human intervention. In addition to allowing more incredible data transmission speeds, 5G and other ultra-fast networks will enable new types of data transfer (such as superfast broadband and mobile video streaming).

As data scientists use AI algorithms to ensure optimal transfer speeds, automate data center environmental controls, and route traffic, they play a significant role in ensuring optimal data transfer speeds. As these transformative technologies intersect in 2024, robust data science work will be undertaken to ensure that they complement one another.

4.  Auto ML  

It is among the  current trends in data science . In addition to democratizing data science, AutoML is a trend causing the "democratization" of machine learning. Anyone can create ML-based apps using tools and platforms developed by autoML solution developers. The training is designed to address the most pressing problems in their fields but is primarily geared towards subject matter experts lacking the coding skills required to apply AI to those challenges.

It's standard for data scientists to spend significant time cleaning and preparing data - repetitive and mundane tasks. The basic idea behind machine learning is to automate these tasks, but it has evolved to include building models, algorithms, and neural networks. Through simple, user-friendly interfaces that keep the inner workings of ML out of sight, anyone with a problem that they want to test will be able to apply machine learning.

5.  AI and Databases Based on Cloud  

It's a complex task to gather, label, clean, organize, format, and analyze this enormous volume of data in one place. Cloud-based platforms are becoming increasingly popular as a solution to this problem.  Data science and AI  industries will be transformed moving forward with a cloud computing database. As a result of cloud computing, businesses can protect their data and manage their tasks more efficiently and effectively. It is among the  future trends in data science .

6.  Data Visualization  

Visualization of data is the process of displaying information in a graphical format. Data visualization tools allow you to see patterns, trends, and outliers in data by using visual elements such as charts, graphs, and maps. It also allows employees or business owners to present data without confusing non-technical audiences. It is one of the  trending topics in data science . Analyzing massive amounts of data and making data-driven decisions requires data visualization tools and technologies.  

The advantages of data visualization tools are:  

• Visualize relationships and patterns  
• Explore interactive opportunities  
• Able to share information easily  

Tableau, Microsoft Power BI, and Google data studio are some data visualization tools.

7.   Scalability in Artificial Intelligence  

Today's businesses have a confluence of statistics, systems architecture, machine learning deployments, and data mining. For coherence, these components must be combined into flexible, scalable models that handle large amounts of data. It would help if you learned or know about Scalable AI for the following reasons.

The concept of scalable AI refers to algorithms, data models, and infrastructure capable of operating at the speed, size, and complexity required for the task. By reusing and recombining capabilities to scale across business problem statements, scalability contributes to solving scarcity and collection issues of quality data.

The development of ML and AI for scalability requires setting up data pipelines, creating extensible system architectures, developing modern acquisition practices, taking advantage of rapid innovations in AI technologies, and creating and deploying data pipelines. To use cloud-enabled and network-enabled edge devices and centralized data center capabilities to apply artificial intelligence to critical missions.

These are some of the  recent trends   in data science  and  future data science trends  that will bring more innovations in the domain. You can check out the  Data Science course duration  to know how long it'll take you to learn the concepts and trends in data science. Prepare accordingly for the course to advance your career.

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Importance of Data Analytics

Let's check the importance of data analytics:  

1.  Product Development  

Information can be estimated and explored through data analytics. One can confidently forecast future results by understanding the market's or process's current state. Companies use data analysis to comprehend the existing business situation and create new products that meet the market's needs.  

2.  Efficient Operations  

Marketing data analytics can streamline operations or increase benefits by finding more viable ways to streamline processes. Identifying possible issues early, avoiding the waiting period, and taking action are all system advantages.  

3.  Consumer-Centric Content  

We have all experienced how consumers' expectations have increased over the years. Customer service, product offerings, and convenience should be on their list. Therefore, companies are trying harder to anticipate customer needs, their needs, and how they want them. Of course, not all consumers are the same. Regardless of what they value, what they need, and how they behave, they all have different needs. Organizations can use data analytics to deliver personalized experiences and spur action and engagement by targeting consumers and customizing their experiences to their needs.   

Data science market trends  show that the  data science platforms  market was valued at USD  96.3 billion  in 2022, and it is expected to reach around USD 378.7 billion by 2030, growing at a compound annual growth rate (CAGR) of 16.43% from 2023 to 2030.   The field of data science involves theoretical and practical applications of data and technology and  emerging trends in data science , such as big data, predictive analytics, and artificial intelligence. This article discusses the top  data science trends  for 2024 and their importance. Organizations are embracing data science wholeheartedly to stay in the competition and not miss any opportunities.   

To improve your business and career, you can learn all the  new trends in data analytics  with the help of data science courses and boot camps. The  KnowledgeHut’s Data Science Bootcamp fee  is also priced reasonably and helps you build analytical skills and programming knowledge to help you land your dream job in the data science domain.  

Frequently Asked Questions (FAQs)

The emerging trends in data science are data analytics, artificial intelligence, big data, and data science. Businesses want to streamline their business processes by adopting data-driven models. 

By 2023, the demand for data scientists will increase dramatically. By 2020, a survey by IBM predicts that 364,000 to 2,720,000 new data science positions will be created.  

Some of the new trends in data analytics are: 

• Enterprises moving to the cloud 
• Automating the analysis of data 
• Using DataOps to improve data analytics 
• Data visualization advancements in real-time 
• The Data-as-a-Service model will become strategic. 

Ashish Gulati

Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

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The Division of Human Resources and the Penn Center for Public Health host the monthly two-mile wellness walk for April, which is the first outdoor walk of the year. 

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Using data to inform a safer, more supportive campus environment

Penn is one of 10 universities participating in the higher education sexual misconduct and awareness survey this spring, building upon similar undertakings in 2015 and 2019..

In an email on Tuesday, Interim President J. Larry Jameson and Provost John L. Jackson Jr. invited undergraduate, graduate, and professional students across Penn’s 12 schools to participate in the Higher Education Sexual Misconduct and Awareness survey. It is the third survey since 2015 of confidential data-gathering on the topic, meant to inform policies, services, and interventions at Penn and other institutions.

Penn is one of 10 universities and colleges this year that will take part in the survey . Coordinated by Westat, an independent social science research firm, students have received a unique survey link via email, and, upon authentication with their PennKey and password, can access the survey with a single link.

“The safety and wellbeing of our students is our top priority, and we hope students will take time to engage in this important, voluntary survey,” said Jameson. “It is a useful tool to help us understand, prevent, and respond to sexual misconduct on our campus and assess the effectiveness of our education and prevention strategies. We’d really like to see 100% participation.”

The survey is designed to take about 20 minutes to complete, and students will have an opportunity to stop and pick up where they left off if needed. Penn participants will receive a $10 gift card for Amazon, CVS, or Visa (students choose which gift card they’d prefer, and Westat handles the distribution to protect respondents’ privacy.)

Past survey results as well as many other factors have influenced updates on campus, strengthening education, outreach, and response initiatives in recent years. The University has, for example, increased staffing at Penn Violence Prevention , and built engagement around the Penn Anti-Violence Educators peer education program, the Sexual Trauma Treatment Outreach and Prevention (STTOP) team at Penn Wellness, and Restorative Practices at Penn in the Center for Community Standards and Accountability . Other efforts have included events specifically at New Student Orientation , where all undergraduates this academic year participated in small group discussions about consent, led by students and staff.

The 2024 survey for Penn students will be available for four weeks. The survey is anonymous, and because the University will not know who has responded, all students will receive regular reminders. Due to privacy laws, particularly those in EU countries and other states or jurisdictions, the survey is only open to enrolled students in residence in Philadelphia.

Westat is scheduled to deliver the aggregate results across the participating schools this fall. At that time, Westat will also provide Penn with the University’s data to allow for comparisons to the aggregate results. As they were in 2015 and 2019, the results will be posted on the Office of Institutional Research & Analysis website .

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25 years of ‘LOVE’

The iconic sculpture by pop artist Robert Indiana arrived on campus in 1999 and soon became a natural place to come together.

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As the country’s life expectancy has risen, the Malawi Longitudinal Study of Families and Health has shifted its current and future research to aging.

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In hot water: Coral resilience in the face of climate change

Over a decade, researchers from Penn studied coral species in Hawaii to better understand their adaptability to the effects of climate change.

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Hot Topics in Chemistry at ACS Spring 2024: Part 1

• Mar 18, 2024

From edible bugs to exercise in pill form, we bring you a roundup of hot topics and breakthrough chemistry research presented at ACS Spring 2024.

The spring meeting of the American Chemical Society (ACS), held virtually and in person March 17-21, 2024, features more than 12,000 presentations on a diverse range of science topics. Read on to discover some of the hot topics and research highlights* presented at the meeting—and check back throughout the week for more updates!

1. Seasonal Secrets Unveiled: How Wild Animals' Hair Changes to Beat the Cold

Learn how wild animals have evolved to survive extreme temperatures in the groundbreaking research led by Taylor Millett at Utah Tech University. This study explores the fascinating world of animal hair, revealing for the first time that the inner structure of animals' coats undergoes significant changes with the seasons. Unlike common beliefs that only the color of the hair changes, Millett's research shows microscopic alterations within the hair that are crucial for survival in different weather conditions.

Millett and her colleagues explored the complex hair structures of wild, big-game animals including the pronghorn antelope, mule deer, and Rocky Mountain elk. Using advanced scanning electron microscopy, they discovered the unique 'honeycomb' structure within the hairs, which changes in density and size between seasons to provide essential insulation. Their findings not only shed light on nature's ingenious ways of protecting these animals but also suggest possibilities for other applications, such as synthetic insulation for homes and consumer products.

2. Bugging Out: The Surprisingly Tasty World of Edible Ants

When it comes to keeping with ACS Spring 2024's theme of " Many Flavors of Chemistry ," Changqi Liu and his colleagues understood the assignment. Their latest research uncovers the diverse and rich flavor profiles of edible ants , diving deep into the unique aroma profiles of four distinct ant species. From the acidic and vinegary taste of common black ants to the nutty and woody nuances of chicatana ants, their findings reveal that each species brings its own unique set of tastes and smells to the table, advancing our understanding of these insects' culinary potential.

The study also touches on the potential for incorporating these flavors into new food products, particularly as the world seeks more sustainable and eco-friendly protein alternatives.

Watch the Headline Science video surrounding this research, created by the ACS Science Communications team:

Explore More Bug Science on ACS Axial

Worm Slime: The Key to More Eco-Friendly Plastics? The Secret of Spinning A Bug’s Eye View of Road Safety

3. A Chemist's Guide to Brewing Perfect Kombucha

A team of chemists at Shippensburg University are revolutionizing kombucha brewing , tackling the challenges of inconsistent alcohol levels and flavor profiles in the fermented beverage. Their research reports on innovative ways to control alcohol content, enhance taste, and speed up fermentation, offering new insights for both home and commercial kombucha producers.

First, the team analyzed the fermentation process in different containers, where silicone bags showed superior performance over glass jars in both fermentation speed and increased acid production. They also looked into how different sugars affect kombucha's taste and alcohol content. Using glucose as a starter resulted in higher gluconic acid and lower ethanol levels, while fructose led to sweeter brews with more acetic acid and ethanol. These findings are crucial for brewers aiming to tailor their kombucha to specific flavor profiles and alcohol content.

This is not the team's first foray into the art and science of kombucha brewing. In 2023, they published a study in the Journal of Chemical Education exploring the use of a cost-effective sensor to accurately measure alcohol concentrations in kombucha across a variety of undergraduate chem lab courses. Read the article here .

Watch the Headline Science short surrounding this research, created by the ACS Science Communications team:

Read More Booze & Beverage Chemistry on Axial:

What Gives Red Bordeaux Wine a “Meaty” Aroma? Beyond the Bean: The Science Behind Lab-Grown Coffee Staying Fresh: The Chemistry of Beer Packaging Like a Fine (Sparkling) Wine: How to Age Champagne Without Losing the Bubbles

4. A New Decking Material That Fights Global Warming

In a major stride towards environmentally sustainable construction, David Heldebrant and his team have developed a new composite decking material that is both cheaper than standard options and carbon-negative. It's designed to store more carbon dioxide (CO 2 ) than its manufacturing process emits, providing a viable solution to one of the construction sector's most significant challenges: high carbon emissions.

The new material incorporates low-quality brown coal and lignin as fillers, which are then treated with CO 2 . Not only does this composite meet international building codes for decking materials in terms of strength and durability, but it also offers a significant cost advantage, being 18% cheaper than its conventional counterparts.

This type of composite decking could play a pivotal role in reducing the carbon footprint of the building industry: replacing all U.S. decking with this material could sequester the CO 2 equivalent of the annual emissions from 54,000 cars. As the team works towards commercialization, this carbon-negative decking presents a hopeful glimpse into a more sustainable solution for the construction industry.

Read more of David Hildebrant's research on CO 2 capture published in ACS journals:

Water-Lean Solvents for Post-Combustion CO 2 Capture: Fundamentals, Uncertainties, Opportunities, and Outlook David J. Heldebrant*Orcid, Phillip K. KoechOrcid, Vassiliki-Alexandra GlezakouOrcid, Roger RousseauOrcid, Deepika Malhotra, and David C. Cantu DOI : 10.1021/acs.chemrev.6b00768

In Situ Raman Methodology for Online Analysis of CO 2 and H 2 O Loadings in a Water-Lean Solvent for CO 2 Capture Amanda M. Lines, Dushyant Barpaga*, Richard F. Zheng, James R. Collett, David J. Heldebrant, and Samuel A. Bryan* DOI : 10.1021/acs.analchem.3c02281

Directed Hydrogen Bond Placement: Low Viscosity Amine Solvents for CO2 Capture Deepika Malhotra, David C. Cantu, Phillip K. Koech*, David J. Heldebrant, Abhijeet Karkamkar, Feng Zheng, Mark D. Bearden, Roger Rousseau, and Vassiliki-Alexandra Glezakou* DOI : 10.1021/acssuschemeng.8b05481

5. A Pill to Replace the Gym?

While this may seem too good to be true, researchers have recently identified new compounds that can mimic the physical benefits of exercise—a significant advancement particularly for those unable to engage in regular physical activity.

The team, led by Bahaa Elgendy at Washington University School of Medicine, initially developed SLU-PP-332 , a compound that activates estrogen-related receptors (ERRs), which are crucial in muscle adaptation to exercise. While SLU-PP-332 was a pioneering discovery, it had its limitations—particularly in its inability to cross into the brain.

To improve upon SLU-PP-332, the team engineered new molecules that not only demonstrated a stronger activation of the ERRs but also exhibited attributes like enhanced stability and lower toxicity potential. Their effectiveness was measured through an increase in RNA presence in rat heart muscle cells, suggesting a more robust simulation of exercise effects compared to SLU-PP-332.

A crucial advancement with the new compounds is their ability to penetrate the brain, opening possibilities for treating neurodegenerative disorders like Alzheimer's disease. Elgendy and his team hope to further test these compounds in animal models, potentially leading to new treatments for various medical conditions where exercise-mimicking drugs could be beneficial.

Read more of Bahaa Elgendy's research published in ACS journals:

Synthetic ERRα/β/γ Agonist Induces an ERRα-Dependent Acute Aerobic Exercise Response and Enhances Exercise Capacity Cyrielle Billon, Sadichha Sitaula, Subhashis Banerjee, Ryan Welch, Bahaa Elgendy, Lamees Hegazy, Tae Gyu Oh, Melissa Kazantzis, Arindam Chatterjee, John Chrivia, Matthew E. Hayes, Weiyi Xu, Angelica Hamilton, Janice M. Huss, Lilei Zhang, John K. Walker, Michael Downes, Ronald M. Evans, and Thomas P. Burris* DOI : 10.1021/acschembio.2c00720

Synthesis of 3-Aminoquinazolinones via a SnCl 2 -Mediated ANRORC-like Reductive Rearrangement of 1,3,4-Oxadiazoles Mohamed Elagawany, Lingaiah Maram, and Bahaa Elgendy* DOI : 10.1021/acs.joc.3c01973

Recent Advances in the Medicinal Chemistry of Farnesoid X Receptor Yuanying Fang, Lamees Hegazy, Brian N. Finck, and Bahaa Elgendy* DOI : 10.1021/acs.jmedchem.1c01017

6. Revolutionizing Cancer Research with Artificial Mucus

Led by Jessica Kramer at the University of Utah, this study marks a significant advancement in understanding the role of mucus in tumor formation. By synthesizing mucins, the sugar-coated proteins that mucus primarily consists of, the team discovered that altering the mucins in healthy cells to resemble those in cancer cells can induce cancer-like behaviors in these cells.

Kramer's approach, involving synthetic chemistry and bacterial enzymes, allows for the precise alteration of mucins and reveals how specific changes in their sugar or protein sequences can impact cellular behavior. This methodology led to the observation that healthy epithelial cells with modified, cancer-like mucins cease normal cell extrusion and begin to pile up, a process resembling early tumor formation. While it's still unclear if these cells transform into cancer cells, the findings open new avenues for developing cancer treatments targeting mucins, particularly the sugar groups on these molecules. Beyond cancer, this research could lead to the development of anti-infectives, probiotics, and other health-supporting therapies.

*Press Release content and videos in this post are brought to you by the ACS Science Communications team. Learn more below.

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What the Data Says About Pandemic School Closures, Four Years Later

The more time students spent in remote instruction, the further they fell behind. And, experts say, extended closures did little to stop the spread of Covid.

By Sarah Mervosh ,  Claire Cain Miller and Francesca Paris

Four years ago this month, schools nationwide began to shut down, igniting one of the most polarizing and partisan debates of the pandemic.

Some schools, often in Republican-led states and rural areas, reopened by fall 2020. Others, typically in large cities and states led by Democrats, would not fully reopen for another year.

A variety of data — about children’s academic outcomes and about the spread of Covid-19 — has accumulated in the time since. Today, there is broad acknowledgment among many public health and education experts that extended school closures did not significantly stop the spread of Covid, while the academic harms for children have been large and long-lasting.

While poverty and other factors also played a role, remote learning was a key driver of academic declines during the pandemic, research shows — a finding that held true across income levels.

Source: Fahle, Kane, Patterson, Reardon, Staiger and Stuart, “ School District and Community Factors Associated With Learning Loss During the COVID-19 Pandemic .” Score changes are measured from 2019 to 2022. In-person means a district offered traditional in-person learning, even if not all students were in-person.

“There’s fairly good consensus that, in general, as a society, we probably kept kids out of school longer than we should have,” said Dr. Sean O’Leary, a pediatric infectious disease specialist who helped write guidance for the American Academy of Pediatrics, which recommended in June 2020 that schools reopen with safety measures in place.

There were no easy decisions at the time. Officials had to weigh the risks of an emerging virus against the academic and mental health consequences of closing schools. And even schools that reopened quickly, by the fall of 2020, have seen lasting effects.

But as experts plan for the next public health emergency, whatever it may be, a growing body of research shows that pandemic school closures came at a steep cost to students.

The longer schools were closed, the more students fell behind.

At the state level, more time spent in remote or hybrid instruction in the 2020-21 school year was associated with larger drops in test scores, according to a New York Times analysis of school closure data and results from the National Assessment of Educational Progress , an authoritative exam administered to a national sample of fourth- and eighth-grade students.

At the school district level, that finding also holds, according to an analysis of test scores from third through eighth grade in thousands of U.S. districts, led by researchers at Stanford and Harvard. In districts where students spent most of the 2020-21 school year learning remotely, they fell more than half a grade behind in math on average, while in districts that spent most of the year in person they lost just over a third of a grade.

( A separate study of nearly 10,000 schools found similar results.)

Such losses can be hard to overcome, without significant interventions. The most recent test scores, from spring 2023, show that students, overall, are not caught up from their pandemic losses , with larger gaps remaining among students that lost the most ground to begin with. Students in districts that were remote or hybrid the longest — at least 90 percent of the 2020-21 school year — still had almost double the ground to make up compared with students in districts that allowed students back for most of the year.

Some time in person was better than no time.

As districts shifted toward in-person learning as the year went on, students that were offered a hybrid schedule (a few hours or days a week in person, with the rest online) did better, on average, than those in places where school was fully remote, but worse than those in places that had school fully in person.

Students in hybrid or remote learning, 2020-21

80% of students

Some schools return online, as Covid-19 cases surge. Vaccinations start for high-priority groups.

Teachers are eligible for the Covid vaccine in more than half of states.

Most districts end the year in-person or hybrid.

Source: Burbio audit of more than 1,200 school districts representing 47 percent of U.S. K-12 enrollment. Note: Learning mode was defined based on the most in-person option available to students.

Income and family background also made a big difference.

A second factor associated with academic declines during the pandemic was a community’s poverty level. Comparing districts with similar remote learning policies, poorer districts had steeper losses.

But in-person learning still mattered: Looking at districts with similar poverty levels, remote learning was associated with greater declines.

A community’s poverty rate and the length of school closures had a “roughly equal” effect on student outcomes, said Sean F. Reardon, a professor of poverty and inequality in education at Stanford, who led a district-level analysis with Thomas J. Kane, an economist at Harvard.

Score changes are measured from 2019 to 2022. Poorest and richest are the top and bottom 20% of districts by percent of students on free/reduced lunch. Mostly in-person and mostly remote are districts that offered traditional in-person learning for more than 90 percent or less than 10 percent of the 2020-21 year.

But the combination — poverty and remote learning — was particularly harmful. For each week spent remote, students in poor districts experienced steeper losses in math than peers in richer districts.

That is notable, because poor districts were also more likely to stay remote for longer .

Some of the country’s largest poor districts are in Democratic-leaning cities that took a more cautious approach to the virus. Poor areas, and Black and Hispanic communities , also suffered higher Covid death rates, making many families and teachers in those districts hesitant to return.

“We wanted to survive,” said Sarah Carpenter, the executive director of Memphis Lift, a parent advocacy group in Memphis, where schools were closed until spring 2021 .

“But I also think, man, looking back, I wish our kids could have gone back to school much quicker,” she added, citing the academic effects.

Other things were also associated with worse student outcomes, including increased anxiety and depression among adults in children’s lives, and the overall restriction of social activity in a community, according to the Stanford and Harvard research .

Even short closures had long-term consequences for children.

While being in school was on average better for academic outcomes, it wasn’t a guarantee. Some districts that opened early, like those in Cherokee County, Ga., a suburb of Atlanta, and Hanover County, Va., lost significant learning and remain behind.

At the same time, many schools are seeing more anxiety and behavioral outbursts among students. And chronic absenteeism from school has surged across demographic groups .

These are signs, experts say, that even short-term closures, and the pandemic more broadly, had lasting effects on the culture of education.

“There was almost, in the Covid era, a sense of, ‘We give up, we’re just trying to keep body and soul together,’ and I think that was corrosive to the higher expectations of schools,” said Margaret Spellings, an education secretary under President George W. Bush who is now chief executive of the Bipartisan Policy Center.

Closing schools did not appear to significantly slow Covid’s spread.

Perhaps the biggest question that hung over school reopenings: Was it safe?

That was largely unknown in the spring of 2020, when schools first shut down. But several experts said that had changed by the fall of 2020, when there were initial signs that children were less likely to become seriously ill, and growing evidence from Europe and parts of the United States that opening schools, with safety measures, did not lead to significantly more transmission.

“Infectious disease leaders have generally agreed that school closures were not an important strategy in stemming the spread of Covid,” said Dr. Jeanne Noble, who directed the Covid response at the U.C.S.F. Parnassus emergency department.

Politically, though, there remains some disagreement about when, exactly, it was safe to reopen school.

Republican governors who pushed to open schools sooner have claimed credit for their approach, while Democrats and teachers’ unions have emphasized their commitment to safety and their investment in helping students recover.

“I do believe it was the right decision,” said Jerry T. Jordan, president of the Philadelphia Federation of Teachers, which resisted returning to school in person over concerns about the availability of vaccines and poor ventilation in school buildings. Philadelphia schools waited to partially reopen until the spring of 2021 , a decision Mr. Jordan believes saved lives.

“It doesn’t matter what is going on in the building and how much people are learning if people are getting the virus and running the potential of dying,” he said.

Pandemic school closures offer lessons for the future.

Though the next health crisis may have different particulars, with different risk calculations, the consequences of closing schools are now well established, experts say.

In the future, infectious disease experts said, they hoped decisions would be guided more by epidemiological data as it emerged, taking into account the trade-offs.

“Could we have used data to better guide our decision making? Yes,” said Dr. Uzma N. Hasan, division chief of pediatric infectious diseases at RWJBarnabas Health in Livingston, N.J. “Fear should not guide our decision making.”

Source: Fahle, Kane, Patterson, Reardon, Staiger and Stuart, “ School District and Community Factors Associated With Learning Loss During the Covid-19 Pandemic. ”

The study used estimates of learning loss from the Stanford Education Data Archive . For closure lengths, the study averaged district-level estimates of time spent in remote and hybrid learning compiled by the Covid-19 School Data Hub (C.S.D.H.) and American Enterprise Institute (A.E.I.) . The A.E.I. data defines remote status by whether there was an in-person or hybrid option, even if some students chose to remain virtual. In the C.S.D.H. data set, districts are defined as remote if “all or most” students were virtual.

An earlier version of this article misstated a job description of Dr. Jeanne Noble. She directed the Covid response at the U.C.S.F. Parnassus emergency department. She did not direct the Covid response for the University of California, San Francisco health system.

How we handle corrections

Sarah Mervosh covers education for The Times, focusing on K-12 schools. More about Sarah Mervosh

Claire Cain Miller writes about gender, families and the future of work for The Upshot. She joined The Times in 2008 and was part of a team that won a Pulitzer Prize in 2018 for public service for reporting on workplace sexual harassment issues. More about Claire Cain Miller

Francesca Paris is a Times reporter working with data and graphics for The Upshot. More about Francesca Paris

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Amendment 6: New Horizons data now in scope for B.4 Heliophysics Guest Investigator Open

The Heliophysics Guest Investigator Open (HGIO) program is intended to maximize the scientific return from operating missions by providing support for research that is beyond the scope of work of the mission science teams. All HGIO investigations must be intensive data analysis efforts. The HGIO program is for investigations with a primary emphasis on the analysis of data from currently operating missions in the HSO or allowable CubeSat missions (e.g., ELFIN, DAILI, RAD (Curiosity), or MinXSS). The list of operating HSO missions is found at: https://science.nasa.gov/missions-page?field_division_tid=5&field_phase_tid=29 .

ROSES-2024 Amendment 6 announces that New Horizons datasets are now in scope for this program element. Science goals or objectives addressed by New Horizons mission data must conform to the relevant Heliophysics science scope outlined in Section 1 of B.1 The Heliophysics Research Program Overview , and data must be publicly available 30 days prior to the Step-2 deadline, see Section 1.2 of B.4 HGIO . The due dates remain unchanged: Step-1 proposals are due May 23, 2024, and Step-2 proposals are due August 1, 2024.

Questions concerning B.4 HGIO may be directed to Galen Fowler at [email protected] .

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Satellite data assimilation improves forecasts of severe weather

In 2020, a line of severe thunderstorms unleashed powerful winds that caused billions in damages across the Midwest United States. A technique developed by Penn State scientists that incorporates satellite data could improve forecasts -- including where the most powerful winds will occur -- for similar severe weather events.

The researchers reported in the journal Geophysical Research Letters that adding microwave data collected by low-Earth-orbiting satellites to existing computer weather forecast models produced more accurate forecasts of surface gusts in a case study of the 2020 Midwest Derecho. Derechos are lines of intense thunderstorms notorious for their damaging winds.

"The computer model is able to produce a series of forecasts that consistently emphasize the most powerful storms and strongest wind damage at where it happened," said Yunji Zhang, assistant professor in the Department of Meteorology and Atmospheric Science at Penn State and lead author. "If we have this kind of information in real time, before the events occur, forecasters might be able to pinpoint where the strongest damage is going to happen."

The technique could be especially useful, the scientists said, in areas that lack ground-based weather monitoring infrastructure -- like radars traditionally used in weather forecasting. In the study, the researchers only used data available from satellite observations.

"In regions where there are no surface observations, or basically no radar, we show that this combination of satellite observations can generate a decent forecast of severe weather events," Zhang said. "We can probably apply this technique to more regions where there are no radar or dense surface observations. That's the fundamental motivation behind this study."

The research builds on the team's prior work using data assimilation, a statistical method that aims to paint the most accurate picture of current weather conditions. This includes even small changes in the atmosphere as they can lead to large discrepancies in forecasts over time.

In prior work, scientists with Penn State's Center for Advanced Data Assimilation and Predictability Techniques assimilated infrared brightness temperature data from the U.S. Geostationary Operational Environmental Satellite, GOES-16. Brightness temperatures show how much radiation is emitted by objects on Earth and in the atmosphere, and the scientists used infrared brightness temperatures at different frequencies to paint a better picture of atmospheric water vapor and cloud formation.

But infrared sensors only capture what is happening at the cloud tops.

Microwave sensors view an entire vertical column, offering new insight into what is happening underneath clouds after storms have formed, the scientists said.

"Just based on the cloud tops, it's more difficult to infer what the convection of these storms looks like underneath," Zhang said. "So that's one of the benefits of adding in the microwave observations -- they can provide information on where the strongest convections are."

By combining assimilated infrared and microwave data in the study of the derecho, the researchers were able to predict surface gust locations and maximum wind values more accurately.

In future work, Zhang said he plans to apply the method to regions that lack the resources and infrastructure to support high-spatiotemporal-resolution weather observations.

"We know that there have been several times in the past several years in West Africa where very strong torrential rainfall events have brought on a lot of precipitation to those countries," Zhang said. "And one thing about these countries is that they are also the places that will likely be impacted most by global warming. So I think if we can use these available satellite observations to provide better forecast for those regions, it will be really beneficial for the people there as well."

Also contributing from Penn State were David Stensrud and Eugene Clothiaux, professors, and Xingchao Chen, assistant professor, all in the Department of Meteorology and Atmospheric Science.

NASA and the U.S. Department of Energy provided funding for this work.

• Severe Weather
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Story Source:

Materials provided by Penn State . Original written by Matthew Carroll. Note: Content may be edited for style and length.

Journal Reference :

• Yunji Zhang, Xingchao Chen, David J. Stensrud, Eugene E. Clothiaux. Enhancing Severe Weather Prediction With Microwave All‐Sky Radiance Assimilation: The 10 August 2020 Midwest Derecho . Geophysical Research Letters , 2024; 51 (2) DOI: 10.1029/2023GL106602

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