case study topics in data structures

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13: Case study - data structure selection

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Allen B. Downey
Olin College via Green Tea Press

At this point you have learned about Python’s core data structures, and you have seen some of the algorithms that use them. If you would like to know more about algorithms, this might be a good time to read Chapter B. But you don’t have to read it before you go on; you can read it whenever you are interested.

This chapter presents a case study with exercises that let you think about choosing data structures and practice using them.

13.1: Word frequency analysis
13.2: Random numbers
13.3: Word histogram
13.4: Most common words
13.5: Optional parameters
13.6: Dictionary subtraction
13.7: Random words
13.8: Markov analysis
13.9: Data structures
13.10: Debugging
13.11: Glossary
13.12: Exercises

Data Structures Case Studies

Optimizing data structures is different from optimizing algorithms as data structure problems have more dimensions: you may be optimizing for throughput , for latency , for memory usage , or any combination of those — and this complexity blows up exponentially when you need to process multiple query types and consider multiple query distributions.

This makes simply defining benchmarks much harder, let alone the actual implementations. In this chapter, we will try to navigate all this complexity and learn how to design efficient data structures with extensive case studies.

A brief review of the CPU cache system is strongly advised.

10 Real World Data Science Case Studies Projects with Example

Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.

BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses. We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.

Walmart Sales Forecasting Data Science Project

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So, without much ado, let's get started with data science business case studies !

With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.

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Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science at Walmart:

i) Personalized Customer Shopping Experience

Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.

ii) Order Sourcing and On-Time Delivery Promise

Millions of customers view items on Walmart.com, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.

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iii) Packing Optimization

Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .

Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.

Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.

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Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:

i) Recommendation Systems

Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.

Here is a Recommender System Project to help you build a recommendation system using collaborative filtering.

ii) Retail Price Optimization

Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.

Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.

iii) Fraud Detection

Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.

You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.

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Let us explore data analytics case study examples in the entertainment indusry.

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Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :

i) Personalized Recommendation System

Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.

ii) Content Development using Data Analytics

Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.

iii) Marketing Analytics for Campaigns

Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.

Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.

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In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:

i) Personalization of Content using Recommendation Systems

Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.

Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.

ii) Targetted marketing through Customer Segmentation

With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.

iii) CNN's for Classification of Songs and Audio Tracks

Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.

Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.

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Below you will find case studies for data analytics in the travel and tourism industry.

Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience.

i) Recommendation Systems and Search Ranking Algorithms

Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.

ii) Natural Language Processing for Review Analysis

Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .

Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.

iii) Smart Pricing using Predictive Analytics

The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.

Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics.

Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:

i) Dynamic Pricing for Price Surges and Demand Forecasting

Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.

ii) One-Click Chat

Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.

iii) Customer Retention

Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.

You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.

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7) LinkedIn

LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:

i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems

LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.

ii) Recommendation Systems Personalized for News Feed

The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.

iii) CNN's to Detect Inappropriate Content

To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.

Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.

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Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :

i) Identifying Patients for Clinical Trials

Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.

ii) Supply Chain and Manufacturing

Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.

iii) Drug Development

Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.

You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.

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9) Shell Data Analyst Case Study Project

Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:

i) Precision Drilling

Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used.

ii) Efficient Charging Terminals

Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

iii) Monitoring Service and Charging Stations

Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.

Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.

10) Zomato Case Study on Data Analytics

Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:

i) Personalized Recommendation System for Homepage

Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato.

You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.

ii) Analyzing Customer Sentiment

Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.

iii) Predicting Food Preparation Time (FPT)

Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time.

Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.

FAQs on Data Analysis Case Studies

A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.

To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.

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Introducing Data Structures with Java by David Cousins

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Case Study 4—A Queue Simulation

17.1 introduction.

Chapter 10 introduced the topic of queues including the priority queue, which holds items in the order of some defined criterion. This case study will build an application to simulate such a queue. It also includes the process of writing data to a disk file as described in Chapter 4 .

17.2 THE APPLICATION

Checking in for a flight includes being given a boarding card printed with a seat number for the aircraft cabin. However, many low-cost airline operators do not allocate seats at check-in, leaving passengers to pick one when they board instead. An attempt is made to avoid a free-for-all from developing as a result, as follows:

When booking a budget flight, it is often possible at extra cost ...

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4.1 Algorithms and Data Structures

The fundamental toolkit for the aspiring computer scientist or programmer.

What are Data Structures?

Collecting, organizing, accessing, and manipulating information in a computer.

Allocating Memory

Records are created on the fly! We keep track of them by holding on to their addresses, which creates the possibility of aliasing.

Records in Memory

Using addresses, the data in records can be read and also modified.

End of Unit 1

Complete all lessons above to reach this milestone.

0 of 3 lessons complete

Pointers to Pointers

One kind of data that a record might store is the address of another record.

Linked Lists

Linked lists are a technique for storing data in a sequence of records connected by pointers.

End of Unit 2

0 of 2 lessons complete

Timing Programs with a Stopwatch

In science, sometimes the best thing to do is run an experiment!

Counting Operations

Find out why computer scientists talk about "cost" instead of "time."

Best, Worst, and Average Case

Algorithms don't always have predictable costs. To prepare for the worst or hope for the best, you need to know what those are!

End of Unit 3

Comparing Algorithms

Big O notation is a simple notation that helps computer scientists compare algorithms and implementations.

Understanding Big O

The flexibility of Big O can make it a bit tricky. But it's the trickiness that makes it so useful!

The Mathematics of Big O

Dive into the details to really understand why Big O notation works the way it does.

Running Time

The tools you've learned for describing the speed of algorithms apply to the Gale-Shapley deferred acceptance algorithm.

End of Unit 4

0 of 4 lessons complete

Course description

Storing and manipulating data is a fundamental part of computer science, and every programming language provides a few simple ways of doing it. Programmers are then free to use these building blocks to design and implement data structures, special-purpose tools for storing data in particular ways.

Topics covered

Data structures
Records (or structs)
Memory allocation
Memory aliasing
Linked lists
Running time
Big-O notation
Best, worst, and average case analysis

Prerequisites and next steps

This course assumes understanding of the basics of algorithms, as well as familiarity with variables, loops, conditionals, and arrays.

Prerequisites

Introduction to Algorithms

5.1 Introduction to Neural Networks

Delve into the inner machinery of neural networks to discover how these flexible learning tools actually work.

Open Data Structures: An Introduction

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Publisher: Athabasca University Press

Language: English

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Reviewed by Joseph Jess, Faculty, Linn-Benton Community College on 1/14/20

Comprehensiveness rating: 5 see less

The text covers all areas I would expect to see in an introduction to data structures (lists, trees, hash tables, graphs, supporting searching and sorting algorithms for relevant structures, and plenty of complexity analysis) with a variety of variations on the structures and some reasoning as to why we might want to use these variations. The table of contents and term-based index have enough detail especially considering the organized structure of the book.

Content Accuracy rating: 4

The contents are accurate, I found no obvious errors, and at least mentions and gives brief examples of the background information needed to be most successful with the materials; though having some explanation on some of the common proof techniques used with data structures that we use could be beneficial as well (there is some mention of this in the mathematical background section but not much of the reasoning behind why we care about this analysis).

Relevance/Longevity rating: 5

The content is very relevant for an introduction to data structures, needing to cover the simpler version of these structures and then provide a few variations from the many decades of basic materials covered; this will hopefully show students that this is an area that is still being researched heavily. Updating the basics I expect to be rare, and a perfect starting point for using these structures in other related areas while allowing new developments (and far far more detailed than some of these basic structures) to be covered in a separate text or other resource.

Clarity rating: 4

The text is written in prose accessible to someone with perhaps a year of academic courses in computer science, but does lean somewhat on someone having solved problems either programmatically or having a good deal of mathematical background. The jargon is light and well explained, though their variables for the code examples could be greatly improved...

Consistency rating: 5

The book is very consistent with regards to terminology and framework, I greatly appreciate the regular use of diagrams showing the sequence of actions for an operation to complete (something I frequently draw out on a whiteboard or digital tablet for students).

Modularity rating: 4

The text is readily divisible into smaller reading sections, though you would want to be careful about jumping beyond certain sections before knowing someone had taken in some of the key concepts from a simpler set of structures (perhaps broken into the sections on lists, then hashes and trees, then graphs and sorting, then structures for integers and external memory searching), since they sometimes build on a key concept (such as a simple linked-list node, then a binary tree node, then a more general node).

Organization/Structure/Flow rating: 5

The topics are in a logical order, and so long as someone pauses to ask what the key behaviors and components of a structure are should then be able to see the next section adding either a variation on what we already have access to or roughly one new feature to some of the components we have access to.

Interface rating: 5

The contents look great!

Grammatical Errors rating: 5

The English contents follow English grammar rules well, and the simplified code examples should be easily followed by someone having worked with a C-based language or a good deal of mathematics before.

Cultural Relevance rating: 5

The text is not culturally insensitive or offensive to me; though it does not make use of virtually any examples or variety of race, ethnicity, or background.

I plan to use this as a supporting text for my own data structures course with the many explanations for why we study each structure and why we care about each section we analyze or implement ourselves; with it covering all the major topics that I do in class and having variations that I do not bring up in class due to time constraints.

Reviewed by Breeann Flesch, Associate Professor of Computer Science, Western Oregon University on 2/25/19

This book covers all of the topics typical in an introductory data structures class (complexity, sorts, stacks, queues, binary search trees, heaps, hash tables, etc). read more

This book covers all of the topics typical in an introductory data structures class (complexity, sorts, stacks, queues, binary search trees, heaps, hash tables, etc).

Content Accuracy rating: 5

The book is thorough and accurate, including relevant mathematical topics for background information.

Since this course covers the foundations of computer science, it is likely to be relevant for a long time.

Clarity rating: 5

The book has lots of examples and pictures. The code examples are based on Java, but have been simplified enough to consider them language-agnostic pseudocode.

Consistency rating: 4

The book's definitions and notation are consistent throughout.

It would be easy to assign smaller chunks for scaffolding. However, many topics do build upon previous sections, so it may be difficult to jump around or skip sections.

The book is organized well, making sure that topics build in a logical order.

The entire book seemed to display well with my pdf reader.

Grammatical Errors rating: 4

The text contains few grammatical errors.

This book is as culturally sensitive as makes sense for a data structures text.

Overall, this book is comprehensive and accurate. I will use it for my data structure classes in the future.

Reviewed by Andrew Black, Professor, Portland State University on 8/21/16

It does a very thorough job of describing data structures for stacks, queues, lists, sets, and “sortedSets” — the latter not exactly a standard mathematical structure, but quite a useful one for many algorithms. It also has chapters on Priority queues, sorting, graph representation and traversal, tries, and B-trees. The treatment of the basic data structures for lists and sets is unusually comprehensive, covering not just linear lists but also skiplists, hash tables, and various kinds of tree. The chapter on hashing covers not just hash tables, but also the construction of hash functions, a topic that is often omitted in texts.

It also contains some introductory material on asymptotic notation, randomization and probability. My feeling that this material would be a useful refresher for a student (or a professor!) who hadn’t used this kind of mathematics for a while, but that it would by itself be inadequate as an introduction. This may be a reflection on the different levels of math education expected at a university in Canada vs on in the USA.

I didn’t find any errors in the book, and feel that it is accurate. However, I did not read all of the later chapters in detail, and certainly did not check the proofs. Its treatment of Quicksort is much better than that of many algorithms books, since it assumes from the start (as did Hoare in the original papers) that the pivot must be chosen randomly. I was a little disappointed, though, to see that one other feature of Quicksort, which I consider obligatory, was omitted — the double-ended traversal in the partition algorithm,which reduces the number of swaps by roughly half compared to a single-ended traversal. This can’t be considered a bug, though, because the book does not include an analysis of the number of swaps, only of the number of comparisons, even though the latter is a much less expensive operation.

Relevance/Longevity rating: 4

This material is pretty stable. The book does not include some of the more recent development, such as dual-pivot Quicksort, but the topic of data structures moves so quickly that it would be impossible to include every new development. In any case, this would not be appropriate in a textbook.

The text is written in lucid, accessible prose. Sometimes I was left wanting a little more context. For example, in the discussion of multiplicative hashing, a couple of sentences describing the goal (mixing up the bits of the input) and the mechanism (selecting some of the “middle” bits of the product of the input and a randomly-chosen constant) would have been useful, as a prelude to the detailed discussion of the mechanism.

With a very few exceptions, the text is internally consistent in terms of terminology and framework. There is just one place where I noticed the consistency breaking down, in the section on random Binary Search Trees, where there seems to be some confusion between _element n_ and the element with _rank n_.

All the chapters depend the interfaces, computational model and analysis techniques defined in Chapter 1. Apart from that, the other chapters are largely self-contained. This is about as good as could be expected. However, two of the names given to the interfaces in chapter 1 are obscure. Everyone knows what a Queue and a Stack are, but what's an SSet? Every time I came back to the book after working on something else, I had to remind myself what a USet and and SSet were. This is so unnecessary. It turns out that a USet is just a mutable Set, while an SSet is a mutable Sorted Set. Using the slightly longer names would make this book much more useful as a reference.

Generally, the book is very well organized, working form simple and straightforward (not no necessarily fast) data structures to complex one that have better amortized performance. By only objection is to chapter 13, which is titled Data Structures for Integers, but which deals with Tries. I tend to think of Tries as a data structure for variable-length strings, although they can certainly be used for integers too.

It's a very nice looking text.

Excellent! And I'm a picky reviewer.

The text is not culturally insensitive or offensive in any way that I observed.

I enjoyed reading it — this is important.

1 Introduction
2 Array-Based Lists
3 Linked Lists
4 Skiplists
5 Hash Tables
6 Binary Trees
7 Random Binary Search Trees
8 Scapegoat Trees
9 Red-Black Trees
11 Sorting Algorithms
13 Data Structures for Integers
14 External Memory Searching

Ancillary Material

About the book.

Offered as an introduction to the field of data structures and algorithms, Open Data Structures covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs. Focusing on a mathematically rigorous approach that is fast, practical, and efficient, Morin clearly and briskly presents instruction along with source code.

Analyzed and implemented in Java, the data structures presented in the book include stacks, queues, deques, and lists implemented as arrays and linked-lists; space-efficient implementations of lists; skip lists; hash tables and hash codes; binary search trees including treaps, scapegoat trees, and red-black trees; integer searching structures including binary tries, x-fast tries, and y-fast tries; heaps, including implicit binary heaps and randomized meldable heaps; graphs, including adjacency matrix and adjacency list representations; and B-trees.

A modern treatment of an essential computer science topic, Open Data Structures is a measured balance between classical topics and state-of-the art structures that will serve the needs of all undergraduate students or self-directed learners.

About the Contributors

Pat Morin is Professor in the School of Computer Science at Carleton University as well as founder and managing editor of the open access Journal of Computational Geometry. He is the author of numerous conference papers and journal publications on the topics of computational geometry, algorithms, and data structures.

Contribute to this Page

How Search Works Under the Hood – Data Structures and Search Algorithms Explained

Searching is something people do every day. Whether they're word searching in documents and databases, or pattern matching in bioinformatics to detect anomalies in DNA, the applications for search are pretty much endless.

And these applications require a lot of computation. Imagine searching for a particular DNA sequence out of millions, or searching for a user in Google's database.

That's why we need an algorithm that runs very fast without consuming lots of memory. And that's what you'll learn about here.

In this tutorial, we will be diving deep into two famous search algorithms: Rabin-Karp's algorithm and the Knuth-Morris-Pratt algorithm. We'll also discuss their time and space complexity, which is equivalent to the time and space an algorithm consumes, depending on its input size. You will also learn about common data structures for searching.

A very basic knowledge of programming is required to follow along. We'll be using Python examples in this tutorial.

Data structures for bioinformatics and searching
Naïve search algorithm
Rabin-Karp's algorithm
Knuth-Morris-Pratt algorithm

Data Structures for Bioinformatics and Searching

Let's first discuss some data structures that you should know even if we weren't diving too deeply into the topic.

The Trie Data Structure

A trie is a tree-like data structure where each nodes stores a letter of an alphabet. You can structure the nodes in a certain way so that words and strings can be retrieved from the structure by traversing down a branch (path) of the tree.

Let's take the words ANA , AND , and BOT for example – their prefix tree or trie will look like this:

Tries are widely used for:

Auto complete
Spell Checkers
Longest prefix matching
Browser history

Suffix trees

Suffix trees are tree-based data structures, but instead of taking multiple words to build the tree we will be using the suffixes of a single world.

If we consider the string AABA , we will first add a $ sign to the end of the string. That $ will make string comparison easier. Now the string is AABA$ .

We will consider all of it's suffixes and the tree will look like this:

Burrows Wheeler transform

Now we will discuss the burrows wheeler transform which is very useful data structure fort string compression.

This data structure is widely used in bioinformatics for a very specific reason. I want you to imagine a string representing a genome. It maybe very long, like 20 million characters.

But there is actually a way to store genomes efficiently. Imagine the string AAAAABBBAAA . This string has many repeats and we can actually compress this string to be something like this: A5B3A3 . This is very useful for genomes as well which have many repeated characters.

Now let's take the string BANANA . We will first add a $ sign to the start of the string then compute all the cyclic rotations of $BANANA . Then will sort the cyclic rotations – and here is the importance of the dollar sign: that value, when sorting, is less than all alphabet values, so it will make sorting way easier.

After we sort the cyclic rotations, the string formed by the last characters of the sorted cyclic rotations is the burrows wheeler transform. It will look like this:

Now as you can see, the burrows wheeler transform tends to have lots of repeats. You might also ask how to reverse the operation. To do that, look at the first characters of the sorted cyclic rotations. They are the same as the characters in the burrows wheeler transform but in sorted order.

So by sorting the burrows wheeler transform, we will get the first and the last letters and compute the original string.

Naïve Search Algorithm

Now let's look at a brute force solution for finding a pattern in a string. This approach is not optimal, as it runs with O(n*m) time complexity (where m is the length of the pattern and n is the length of the string).

We will consider two pointers, I and J . First we'll initialize I and J to be 0. Then we will run a while loop that will keep running while I is less than the length of the string.

Every time the loop runs we will compare the characters at the index I+J in the string and the character at the index J in the pattern. If they are equal, we will increment J , but of mpt. we will increment I and reset J to be 0 . If j ever exceeds the length of the pattern, then there's a match.

The code for this will look like this:

Rabin-Karp's Algorithm

The problem with the naïve approach is that it runs in O(n**2) time complexity which is horrible when it comes to large inputs like genomes. This means we need something better.

Comparing two strings is linear time, because we need to compare each character for each index. But what if we don't need to do that – what if we transformed the strings into numbers?

Here is the main idea of this algorithm. Let's say there is a function that returns a number associated with each string in the alphabet. We will use the ASCII value of the strings for that.

And then to transform a string into a number we will compute the sum of the ASCII values of all its characters. This a simple hashing function:

Now let's say we want to find an in banana . Then we will first compare hash(an) to hash(ba) then to hash(an) and so on till we reach the end of the string. When the hash values match, we can then compare the pattern and the substring because hash(na) and hash(an) are equal so we need an extra check.

You might be thinking this is silly – why do we need to do all of this? Computing the hash function needs to iterate over the string so we didn't achieve any better time complexity. And you're completely right.

But what if we can compute the hash value of a substring from its previous substring? The difference is in the starting and ending characters – and that's called a rolling hash. That's what makes this algorithm run at O(n*m) worst case and O(n+m) average case: because of collisions.

For example, hash(na) is equal to hash(an) , so here we need to compare na and an. The hash function I showed you is a very simple one. The better the hash function, the fewer collisions we will have. But I don't want to make this too fancy, so I will be using this hash function.

Knuth-Morris-Pratt Algorithm

Now we will discuss the last algorithm for today. This one is very complicated but I will try to make it as simple as possible.

The idea behind this algorithm is that the naïve approach is great unless the string and the pattern have many repeats – like for example searching for AAAB in AAAXAAA . In this case do we really need to reset the j pointer to 0 after the first mismatch? Actually no we don't, and I will show you why.

When we analyze this pattern, we can see that it's quite special. Let's say we started at i=0 and we reached j=3. Then the next iteration we got a mismatch.

We don't really need to reset j, since for this given pattern we have the two first characters equal to the second and third characters. And since j already was 3, then the first 3 characters in the string are equal the first three characters in the pattern. And the second and third characters in the string are equal to the first and second characters in the pattern. So we can skip two positions by resetting j to 2 instead of 0.

Now you might ask how we'll know the value of j after the mismatch. Well, the answer depends on the pattern. For example abac if we got a mismatch when we reached j=3 (when we reached c) and since ab is equal to ba , we can actually skip b for the next iteration.

As you can see this works when we have a common suffix and prefix in a substring of a pattern, so we need to preprocess the string. And for each index of that substring, we can compute the number of skips and store it in an array known as the longest suffix prefix array.

Now we'll write the function for computing the LSP array. But first I will explain what I will be doing:

the first element in the array is always 0
we will create an array LSP with the same length as the pattern
we will have twp pointers i and prevlps. i is an iteration variable initially set to 1, since the first element in the array is always 0. prevlps keeps track of the previous longest prefix suffix.
we will run a while loop which will keep running while i is less then the length of the pattern. It will compare pattern[i] and pattern[prevLps]. If they match, we will set LCP[i] to prevlps +1, and we'll increment i and prevlps by 1.
if they don't match and prevlps was 0 already, we will set LCP[i] to 0 and increment i.
if prevlps was not 0, we will set prevlps to LCP[prevlcp-1].

Let's take an example using the string ANAN. First i was 1 and prevlps was 0. The first iteration happens, and A is not equal n and prevlps is 0 – so the LCP is now [0,0,0,0].

Now for the second iteration we will compare pattern[prevlps] which is A and A which evaluates to true. So now i is 3, prevlps is 1, and LCP is [0,0,1,0].

And finally, in the final iteration we will compare N and N. The same thing happens as in the past iteration, and LCP is [0,0,1,2].

The code for LCP is the following:

Now since we have the LCP array we can run the KMP algorithm. It's similar to the naïve algorithm but with some improvements. It will look like this:

This algorithm runs with O(n+m) time complexity which is great – but it's little bit complicated. If you noticed, almost nothing changed (just in computing the LCP and a single line changed). This single line is what makes the algorithm so efficient.

In the end I hope you learned something new from this article. We learned about data structures for searching as well as some famous algorithms for searching.

This tutorial was the result of two weeks of research. There are a lot of things that I wanted to cover but I wanted to keep this manageable.

If you found this useful and want to get more awesome content, follow me on LinkedIn. It will help a lot.

A computer and communications engineer, specialized in React, JavaScript, typescript and python. Follow me for awesome content.

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Distributed data structures: A case study

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Data Structures Tutorial

Introduction to Data Structures
Data Structure Types, Classifications and Applications

Overview of Data Structures

Introduction to Linear Data Structures
Introduction to Hierarchical Data Structure
Overview of Graph, Trie, Segment Tree and Suffix Tree Data Structures

Different Types of Data Structures

Array Data Structure
String in Data Structure
Linked List Data Structure
Stack Data Structure
Queue Data Structure
Introduction to Tree - Data Structure and Algorithm Tutorials
Heap Data Structure
Hashing in Data Structure
Graph Data Structure And Algorithms
Matrix Data Structure
Advanced Data Structures
Data Structure Alignment : How data is arranged and accessed in Computer Memory?
Static Data Structure vs Dynamic Data Structure
Static and Dynamic data structures in Java with Examples
Common operations on various Data Structures
Real-life Applications of Data Structures and Algorithms (DSA)

Different Types of Advanced Data Structures

Data structures are essential components that help organize and store data efficiently in computer memory. They provide a way to manage and manipulate data effectively, enabling faster access, insertion, and deletion operations.

Common data structures include arrays, linked lists, stacks, queues, trees, and graphs , each serving specific purposes based on the requirements of the problem. Understanding data structures is fundamental for designing efficient algorithms and optimizing software performance.

Data Structure

Table of Content

What is a Data Structure?

Why are data structures important, classification of data structures, types of data structures, applications of data structures.

Learn Basics of Data Structure
Most Popular Data Structures
Advanced Data Structure

A data structure is a way of organizing and storing data in a computer so that it can be accessed and used efficiently. It defines the relationship between the data and the operations that can be performed on the data

Data structures are essential for the following reasons:

Efficient Data Management: They enable efficient storage and retrieval of data, reducing processing time and improving performance.
Data Organization: They organize data in a logical manner, making it easier to understand and access.
Data Abstraction: They hide the implementation details of data storage, allowing programmers to focus on the logical aspects of data manipulation.
Reusability: Common data structures can be reused in multiple applications, saving time and effort in development.
Algorithm Optimization: The choice of the appropriate data structure can significantly impact the efficiency of algorithms that operate on the data.

Data structures can be classified into two main categories:

Linear Data Structures: These structures store data in a sequential order this allowing for easy insertion and deletion operations. Examples include arrays, linked lists, and queues.
Non-Linear Data Structures: These structures store data in a hierarchical or interconnected manner this allowing for more complex relationships between data elements. Examples include trees, graphs, and hash tables.

Basically, data structures are divided into two categories:

Linear Data Structures:

Array: A collection of elements of the same type stored in contiguous memory locations.
Linked List: A collection of elements linked together by pointers, allowing for dynamic insertion and deletion.
Queue: A First-In-First-Out (FIFO) structure where elements are added at the end and removed from the beginning.
Stack: A Last-In-First-Out (LIFO) structure where elements are added and removed from the top.

Non-Linear Data Structures:

Tree: A hierarchical structure where each node can have multiple child nodes.
Graph: A collection of nodes connected by edges, representing relationships between data elements.
Hash Table: A data structure that uses a hash function to map keys to values, allowing for fast lookup and insertion.

Data structures are widely used in various applications, including:

Database Management Systems: To store and manage large amounts of structured data.
Operating Systems: To manage memory, processes, and files.
Compiler Design: To represent source code and intermediate code.
Artificial Intelligence: To represent knowledge and perform reasoning.
Graphics and Multimedia: To store and process images, videos, and audio data.

Learn Basics of Data Structure:

Overview of Data Structures | Set 3 (Graph, Trie, Segment Tree and Suffix Tree)
Abstract Data Types

Most Popular Data Structures :

Below are some most popular Data Structure:

Array is a linear data structure that stores a collection of elements of the same data type. Elements are allocated contiguous memory, allowing for constant-time access. Each element has a unique index number.

Important articles on Array:

Search, insert and delete in an unsorted array
Search, insert and delete in a sorted array
Write a program to reverse an array
Leaders in an array
Given an array A[] and a number x, check for pair in A[] with sum as x
Majority Element
Find the Number Occurring Odd Number of Times
Largest Sum Contiguous Subarray
Find the Missing Number
Search an element in a sorted and pivoted array
Merge an array of size n into another array of size m+n
Median of two sorted arrays
Program for array rotation
Reversal algorithm for array rotation
Block swap algorithm for array rotation
Maximum sum such that no two elements are adjacent
Sort elements by frequency | Set 1
Count Inversions in an array

3. Linked List:

A linear data structure where elements are stored in nodes linked together by pointers. Each node contains the data and a pointer to the next node in the list. Linked lists are efficient for inserting and deleting elements, but they can be slower for accessing elements than arrays.

Types of Linked List:

a) Singly Linked List: Each node points to the next node in the list.

Important articles on Singly Linked Lis:

Introduction to Linked List
Linked List vs Array
Linked List Insertion
Linked List Deletion (Deleting a given key)
Linked List Deletion (Deleting a key at given position)
A Programmer’s approach of looking at Array vs. Linked List
Find Length of a Linked List (Iterative and Recursive)
How to write C functions that modify head pointer of a Linked List?
Swap nodes in a linked list without swapping data
Reverse a linked list
Merge two sorted linked lists
Merge Sort for Linked Lists
Reverse a Linked List in groups of given size
Detect and Remove Loop in a Linked List
Add two numbers represented by linked lists | Set 1
Rotate a Linked List
Generic Linked List in C

b) Circular Linked List: The last node points back to the first node, forming a circular loop.

Important articles on Circular Linked List:

Circular Linked List Introduction and Applications,
Circular Singly Linked List Insertion
Circular Linked List Traversal
Split a Circular Linked List into two halves
Sorted insert for circular linked list

c) Doubly Linked List: Each node points to both the next and previous nodes in the list.

Important articles on Doubly Linked List:

Doubly Linked List Introduction and Insertion
Delete a node in a Doubly Linked List
Reverse a Doubly Linked List
The Great Tree-List Recursion Problem.
QuickSort on Doubly Linked List
Merge Sort for Doubly Linked List

6. Binary Tree:

Binary Tree is a hierarchical data structure where each node has at most two child nodes, referred to as the left child and the right child. Binary trees are mostly used to represent hierarchical data, such as file systems or family trees.

Important articles on Binary Tree:

Binary Tree Introduction
Binary Tree Properties
Types of Binary Tree
Handshaking Lemma and Interesting Tree Properties
Enumeration of Binary Tree
Applications of tree data structure
Tree Traversals
BFS vs DFS for Binary Tree
Level Order Tree Traversal
Diameter of a Binary Tree
Inorder Tree Traversal without Recursion
Inorder Tree Traversal without recursion and without stack!
Threaded Binary Tree
Maximum Depth or Height of a Tree
If you are given two traversal sequences, can you construct the binary tree?
Clone a Binary Tree with Random Pointers
Construct Tree from given Inorder and Preorder traversals
Maximum width of a binary tree
Print nodes at k distance from root
Print Ancestors of a given node in Binary Tree
Check if a binary tree is subtree of another binary tree
Connect nodes at same level

7. Binary Search Tree:

A Binary Search Tree is a data structure used for storing data in a sorted manner. Each node in a Binary Search Tree has at most two children, a left child and a right child, with the left child containing values less than the parent node and the right child containing values greater than the parent node. This hierarchical structure allows for efficient searching, insertion, and deletion operations on the data stored in the tree.

Important articles on Binary Search Tree:

Search and Insert in BST
Deletion from BST
Minimum value in a Binary Search Tree
Inorder predecessor and successor for a given key in BST
Check if a binary tree is BST or not
Lowest Common Ancestor in a Binary Search Tree.
Inorder Successor in Binary Search Tree
Find k-th smallest element in BST (Order Statistics in BST)
Merge two BSTs with limited extra space
Two nodes of a BST are swapped, correct the BST
Floor and Ceil from a BST
In-place conversion of Sorted DLL to Balanced BST
Find a pair with given sum in a Balanced BST
Total number of possible Binary Search Trees with n keys
Merge Two Balanced Binary Search Trees
Binary Tree to Binary Search Tree Conversion

9. Hashing:

Hashing is a technique that generates a fixed-size output (hash value) from an input of variable size using mathematical formulas called hash functions . Hashing is used to determine an index or location for storing an item in a data structure, allowing for efficient retrieval and insertion.

Important articles on Hashing:

Hashing Introduction
Separate Chaining for Collision Handling
Open Addressing for Collision Handling
Print a Binary Tree in Vertical Order
Find whether an array is subset of another array
Union and Intersection of two Linked Lists
Find a pair with given sum
Check if a given array contains duplicate elements within k distance from each other
Find Itinerary from a given list of tickets
Find number of Employees Under every Employee

Advanced Data Structure:

Below are some advance Data Structure:

1. Advanced Lists:

Advanced Lists is a data structure that extends the functionality of a standard list. Advanced lists may support additional operations, such as finding the minimum or maximum element in the list, or rotating the list.

Important articles on Advanced Lists:

Memory efficient doubly linked list
XOR Linked List – A Memory Efficient Doubly Linked List | Set 1
XOR Linked List – A Memory Efficient Doubly Linked List | Set 2
Skip List | Set 1 (Introduction)
Self Organizing List | Set 1 (Introduction)
Unrolled Linked List | Set 1 (Introduction)

2. Segment Tree:

Segment Tree is a tree data structure that allows for efficient range queries on an array. Each node in the segment tree represents a range of elements in the array, and the value stored in the node is some aggregate value of the elements in that range.

Important articles on Segment Tree:

Segment Tree | Set 1 (Sum of given range)
Segment Tree | Set 2 (Range Minimum Query)
Lazy Propagation in Segment Tree
Persistent Segment Tree | Set 1 (Introduction)

4. Binary Indexed Tree:

Binary Indexed Tree is a data structure that allows for efficient range queries and updates on an array. Binary indexed trees are often used to compute prefix sums or to solve range query problems.

Important articles on Binary Indexed Tree:

Binary Indexed Tree
Two Dimensional Binary Indexed Tree or Fenwick Tree
Binary Indexed Tree : Range Updates and Point Queries
Binary Indexed Tree : Range Update and Range Queries

Related articles on Binary Indexed Tree :

All Articles on Binary Indexed Tree

5. Suffix Array and Suffix Tree :

Suffix Array and Suffix Tree is a data structures that are used to efficiently search for patterns within a string. Suffix arrays and suffix trees are mostly used in bioinformatics and text processing applications.

Important articles on Suffix Array and Suffix Tree:

Suffix Array Introduction
Suffix Array nLogn Algorithm
kasai’s Algorithm for Construction of LCP array from Suffix Array
Suffix Tree Introduction
Ukkonen’s Suffix Tree Construction – Part 1
Ukkonen’s Suffix Tree Construction – Part 2
Ukkonen’s Suffix Tree Construction – Part 3
Ukkonen’s Suffix Tree Construction – Part 4,
Ukkonen’s Suffix Tree Construction – Part 5
Ukkonen’s Suffix Tree Construction – Part 6
Generalized Suffix Tree
Build Linear Time Suffix Array using Suffix Tree
Substring Check
Searching All Patterns
Longest Repeated Substring,
Longest Common Substring, Longest Palindromic Substring

Related articles on Suffix Array and Suffix Tree:

All Articles on Suffix Tree

6. AVL Tree:

AVL tree is a self-balancing binary search tree that maintains a balanced height. AVL trees are mostly used when it is important to have efficient search and insertion operations.

Important articles on AVL Tree:

AVL Tree | Set 1 (Insertion)
AVL Tree | Set 2 (Deletion)
AVL with duplicate keys

7. Splay Tree:

Splay Tree is a self-balancing binary search tree that moves frequently accessed nodes to the root of the tree. Splay trees are mostly used when it is important to have fast access to recently accessed data.

Important articles on Splay Tree:

Splay Tree | Set 1 (Search)
Splay Tree | Set 2 (Insert)

B Tree is a balanced tree data structure that is used to store data on disk. B trees are mostly used in database systems to efficiently store and retrieve large amounts of data.

Important articles on B Tree:

B-Tree | Set 1 (Introduction)
B-Tree | Set 2 (Insert)
B-Tree | Set 3 (Delete)

9. Red-Black Tree:

Red-Black Tree is a self-balancing binary search tree that maintains a balance between the number of black and red nodes. Red-black trees are mostly used when it is important to have efficient search and insertion operations.

Important articles on Red-Black Tree:

Red-Black Tree Introduction
Red Black Tree Insertion.
Red-Black Tree Deletion
Program for Red Black Tree Insertion

10. K Dimensional Tree:

K Dimensional Tree is a tree data structure that is used to store data in a multidimensional space. K dimensional trees are mostly used for efficient range queries and nearest neighbor searches.

Important articles on K Dimensional Tree:

KD Tree (Search and Insert)
K D Tree (Find Minimum)
K D Tree (Delete)

Others Data Structure:

Treap (A Randomized Binary Search Tree)
Ternary Search Tree
Interval Tree
Implement LRU Cache
Sort numbers stored on different machines
Find the k most frequent words from a file
Given a sequence of words, print all anagrams together
Decision Trees – Fake (Counterfeit) Coin Puzzle (12 Coin Puzzle)
Spaghetti Stack
Data Structure for Dictionary and Spell Checker?
Cartesian Tree
Cartesian Tree Sorting
Centroid Decomposition of Tree
Gomory-Hu Tree
Recent Articles on Advanced Data Structures.
Commonly Asked Data Structure Interview Questions | Set 1
A data structure for n elements and O(1) operations
Expression Tree

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I learned all data structures in a week. This is what it did to my brain.

Over the last week, I studied seven commonly used data structures in great depth. In the last 3 years since I first studied about them during my undergraduate studies, I felt no glimmer of temptation to study any one of them again; it wasn’t the complex concepts which kept me away, but their lack of usage in my day to day coding. Every data structure I’ve ever used was built into the language. And I’ve forgotten how they worked under the hood.

They were inescapable now. There are seven data structure in the series to be studied.

Let us go back to where it all began. Like every invention has a necessity, and having data structures also had one.

Say you’ve to find a specific book in an unorganized library. You’ve put in numerous hours in shuffling, organizing and searching for the book. The next day the librarian walks in, looks at the mess you’ve created and organizes the library in a different way.

Problem? Just like you can organize books in a library in 100 different ways, you can structure data in 100 different ways. So, we need to find a way to organize our books(read: data) so that we can find our book(read: data) in a very efficient and fastest way.

Luckily for us, some uber smart people have built great structures that have stood the test of time and help us solve our problem. All we need to know how they work and use them. We call them data structures . Accessing, inserting, deleting, finding, and sorting the data are some of the well-known operations that one can perform using data structures.

The first entry in the series ‘ Array’ leaves no need to have multiple data structures. And yet there will be so many more. I do not have the energy to describe why one data structure triumph over another one. But, I’ll be honest with you: it does matter knowing multiple data structures.

Still not convinced?

Let’s try to solve few operations with our beloved array. You want to find something in an array, just check every slot. Want to insert anything in middle? You can move every element to make room.

Easy-peasy, right?

The thing is “all of these are slow”. We want to find/sort/insert data efficiently and in the fastest possible way. An algorithm may want to perform these operations a million of times. If you can’t do them efficiently, many other algorithms are inefficient . As it turns out, you can do lots of things faster if you arrange the data differently.

You may think, “Ah, but what if they ask me trivia questions about which data structure is most important or rank them”

At which point I must answer: At any rate, should that happen, just offer them this — the ranking of the data structures will be at least partially tied to problem context. And never ever forget to analyze time and space performance of the operations.

But if you want a ranking of learning different data structures, below is the list from most tolerable to “oh dear god”

Linked List
Hash Tables

You will need to keep the graph and trees somewhere near the end, for, I must confess: it is huge and deals with zillions of concepts and different algorithms.

Maps or arrays are easy. You’ll have a difficult time finding a real-world application that doesn’t use them. They are ubiquitous.

As I worked my way through other structures, I realized one does not simply eat the chips from the Pringles tube, you pop them. The last chip to go in the tube is the first one to go in my stomach(LIFO) . The pearl necklace you gifted your Valentine is nothing but a circular linked list with each pearl containing a bit of data. You just follow the string to the next pearl of data, and eventually, you end up at the beginning again.

Our Brain somehow makes the leap from being the most important organ to one of the world’s best example of a linked list . Consider the thinking process when you placed your car keys somewhere and couldn’t remember.Our brain follows association and tries to link one memory with another and so on and we finally recall the lost memory.

We are connected on Medium. Thank you, Graphs. When a data structure called trees goes against nature’s tradition of having roots at the bottom, we accept it handily. Such is the magic of data structures. There is something ineffable about them — perhaps all our software are destined for greatness. We just haven’t picked the right data structure.

Here, in the midst of theoretical concepts is one of the most nuanced and beautiful real-time examples of the stacks and queues data structure I’ve seen in real life.

Browser back/forward button and browsing history

As we navigate from one web page to another, those pages are placed on a stack. The current page that we are viewing is on the top and the first page we looked at is at the base. If we click on the Back button, we begin to move in reverse order through the pages. A queue is used for Browsing history. New pages are added to history. Old pages removed such as in 30 days

Now pause for a moment and imagine how many times we, as both a user and developer, use stacks and queues. That is amazing, right?

But, my happiness was short-lived. As I progressed with the series, I realized we have a new data structure based on a doubly-linked list that handles browser back and forward functionality more efficiently in O(1) time.

That is the problem with the data structures. I am touched and impressed by a use case, and then everyone starts talking about why one should be preferred over other based on time complexities and I feel my brain cells atrophying.

In the end, I am left not knowing what to do. I can’t look at the things the same way ever again. Maps are graphs. Trees look upside down. I pushed my article in Codeburst’s queue to be published. I wish they introduced something like prime/priority writers, which might help me jump the queue. These data structures look absolutely asinine yet I cannot stop talking/thinking about them. Please help.

Thanks for reading! If you liked this post, you can check out my other writings here or please consider entering your email here if you’d like to be added to my once-weekly email list.You can also follow me on Twitter , Linkedin & Github

If this post was helpful, please click the clap button below a few times to show your support!

✉️ Subscribe to CodeBurst’s once-weekly Email Blast , 🐦 Follow CodeBurst on Twitter , view 🗺️ The 2018 Web Developer Roadmap , and 🕸️ Learn Full Stack Web Development .

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Teaching Data Structures and Algorithms Through Games

Conference paper
First Online: 19 November 2022
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Davide Carneiro ORCID: orcid.org/0000-0002-6650-0388 15 &
Mariana Carvalho ORCID: orcid.org/0000-0003-2190-4319 15

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 538))

Included in the following conference series:

International Conference in Methodologies and intelligent Systems for Techhnology Enhanced Learning

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Computer Science degrees are often seen as challenging by students, especially in what concerns subjects such as programming, data structures or algorithms. Many reasons can be pointed out for this, some of which related to the abstract nature of these subjects and the lack of previous related knowledge by the students. In this paper we tackle this challenge using gamification in the teaching/learning process, with two main goals in mind. The first is to increase the intrinsic motivation of students to learn, by making the whole process more fun, enjoyable and competitive. The second is to facilitate the learning process by providing intuitive tools for the visualization of data structures and algorithmic output, together with a tool for automated assessment that decreases the dependence on the teacher and allows them to work more autonomously. We validated this approach over the course of three academic years in a Computer Science degree of the Polytechnic of Porto, Portugal, through the use of a questionnaire. Results show that the effects of using games and game elements have a generally positive effect on motivation and on the overall learning process.

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Ahmad, A., Zeshan, F., Khan, M.S., Marriam, R., Ali, A., Samreen, A.: The impact of gamification on learning outcomes of computer science majors. ACM Trans. Comput. Educ. (TOCE) 20 (2), 1–25 (2020)

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Acknowledgments

This work has been supported by national funds through FCT - Fundação para a Ciência e Tecnologia through project UIDB/04728/2020.

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CIICESI, ESTG, Politécnico do Porto, 4610-156, Felgueiras, Portugal

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Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia

Zuzana Kubincová

Ca' Foscari University of Venice, Venice, Italy

Alessandra Melonio

University of Minho, Braga, Portugal

Dalila Durães

Polytechnic Institute of Porto, Porto, Portugal

Davide Rua Carneiro

Politecnico di Milano, Milan, Italy

Mehdi Rizvi

Department of Life, Health and Environmental Sciences, University of L'Aquila, Coppito, Italy

Loreto Lancia

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Carneiro, D., Carvalho, M. (2023). Teaching Data Structures and Algorithms Through Games. In: Kubincová, Z., Melonio, A., Durães, D., Rua Carneiro, D., Rizvi, M., Lancia, L. (eds) Methodologies and Intelligent Systems for Technology Enhanced Learning, Workshops, 12th International Conference. MIS4TEL 2022. Lecture Notes in Networks and Systems, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-031-20257-5_1

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Examining the role of criminal profiling in solving complex cases.
Exploring the impact of illegal substance policies on criminal justice outcomes.
Case study on juvenile justice and rehabilitation programs.
Analyzing the effects of witness testimony on criminal trials.
Examining the role of mental health considerations in criminal sentencing.
Case study on the use of forensic science in criminal investigations.
Exploring the ethical considerations in criminal justice policymaking.
Case study on the effectiveness of community policing initiatives.
Analyzing the impact of racial disparities in sentencing and incarceration.
Examining the role of private prisons in the criminal justice system.
Case study on the challenges and opportunities in criminal justice reform.
Exploring the intersection of technology and privacy rights in criminal cases.
Case study on the implications of criminal justice policies on recidivism.

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Analyzing the influence of social media on political discourse.
Case study on the effects of fake news on public perception.
Examining the role of media convergence in shaping content delivery.
Exploring the impact of user-generated content on traditional media.
Case study on the ethical considerations of media representation.
Analyzing the effects of media ownership concentration on diversity.
Examining the role of media literacy in the digital age.
Case study on the evolving landscape of broadcast journalism.
Exploring the impact of streaming services on traditional television.
Case study on media coverage of global events and its implications.
Analyzing the role of media in shaping cultural identities.
Examining the effects of advertising and product placement in media.
Case study on the intersection of entertainment and social issues in media.
Exploring the role of media regulations in protecting consumer interests.
Case study on the influence of media on public health perceptions.

Human Rights Case Studies

Navigate the complexities of human rights with these compelling case study ideas. From the examination of global human rights challenges to the role of international organizations, these topics offer a nuanced perspective on the pursuit of justice.

Analyzing the impact of human rights violations in conflict zones.
Case study on the role of international courts in addressing human rights abuses.
Examining the challenges and opportunities in promoting gender equality.
Exploring the effects of migration policies on refugee rights.
Case study on the intersection of technology and human rights advocacy.
Analyzing the role of non-governmental organizations in protecting human rights.
Examining the implications of surveillance practices on privacy rights.
Case study on the challenges of upholding indigenous rights.
Exploring the role of education in promoting human rights awareness.
Case study on the effects of censorship on freedom of expression.
Analyzing the impact of discriminatory laws on LGBTQ+ rights.
Examining the role of economic policies in addressing socio-economic rights.
Case study on the challenges of enforcing human rights in authoritarian regimes.
Exploring the intersection of environmental protection and human rights.
Case study on the role of media in raising awareness of human rights issues.

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Case Study: How Aggressively Should a Bank Pursue AI?

Thomas H. Davenport
George Westerman

A Malaysia-based CEO weighs the risks and potential benefits of turning a traditional bank into an AI-first institution.

Siti Rahman, the CEO of Malaysia-based NVF Bank, faces a pivotal decision. Her head of AI innovation, a recent recruit from Google, has a bold plan. It requires a substantial investment but aims to transform the traditional bank into an AI-first institution, substantially reducing head count and the number of branches. The bank’s CFO worries they are chasing the next hype cycle and cautions against valuing efficiency above all else. Siti must weigh the bank’s mixed history with AI, the resistance to losing the human touch in banking services, and the risks of falling behind in technology against the need for a prudent, incremental approach to innovation.

Two experts offer advice: Noemie Ellezam-Danielo, the chief digital and AI strategy at Société Générale, and Sastry Durvasula, the chief information and client services officer at TIAA.

Siti Rahman, the CEO of Malaysia-headquartered NVF Bank, hurried through the corridors of the university’s computer engineering department. She had directed her driver to the wrong building—thinking of her usual talent-recruitment appearances in the finance department—and now she was running late. As she approached the room, she could hear her head of AI innovation, Michael Lim, who had joined NVF from Google 18 months earlier, breaking the ice with the students. “You know, NVF used to stand for Never Very Fast,” he said to a few giggles. “But the bank is crawling into the 21st century.”

Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, a visiting scholar at the MIT Initiative on the Digital Economy, and a senior adviser to Deloitte’s AI practice. He is a coauthor of All-in on AI: How Smart Companies Win Big with Artificial Intelligence (Harvard Business Review Press, 2023).
George Westerman is a senior lecturer at MIT Sloan School of Management and a coauthor of Leading Digital (HBR Press, 2014).

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Crime, justice and law

Machine learning helps flag issues with police forces sooner

New proof-of-concept tool uses crime data to provide an early warning of potential problems, enabling quicker action from inspectors.

His Majesty’s Inspectorate of Constabulary and Fire & Rescue Services ( HMICFRS ) regularly inspects and monitors police forces using the PEEL (police effectiveness, efficiency and legitimacy) assessment.

A police force is escalated into enhanced monitoring, also known as Engage, when a serious problem is found, and the response is not deemed sufficient. However, by this stage, service to the public may already have been impacted.

HMICFRS approached the Accelerated Capability Environment ( ACE ) to see if there was a way to create an early-warning predictor tool which could estimate PEEL assessment grades before inspections. This would enable HMICFRS ’s inspection programme to be prioritised to more urgently visit forces which have been flagged, with the aim of helping to improve any issues – and making communities safer – sooner.

Applying machine learning to crime data

A decision was taken to focus on one of the PEEL assessment questions: how well forces investigate crime. Working with supplier The London Data Company , a proof of concept for a machine-learning algorithm was pulled together in just eight weeks.

This used publicly available data from sources such as 999 calls, the Home Office and Office for National Statistics on levels of crime and crime outcomes. It was able to correctly predict the PEEL grade for a force in around 60% of cases and was one grade either higher or lower in around 90% of cases.

Jacquie Hayes, HMICFRS insight portfolio director, said: “Our inspection process looks at a massive amount of data from a force, and broadly this tool is coming to a very similar conclusion.

“We are now exploring what more we can do with the data that we collect, as well as what other PEEL questions we could expand this to.”

HMICFRS , which has an ambition to be more data-driven, is now working with The London Data Company on how it can deploy the initial demonstrator tool into its live systems and overall inspection process over the next 18 months.

In terms of potential next applications, Hayes said: “Fire and rescue is also on the list – but it’s a very long list, because we would like to do a lot of things with it!

“You can’t replace our inspection teams with artificial intelligence, but we can certainly think about what this means for how we inspect, and I think this will have an implication on that.”

She added: “This tells us where there might be a problem, but not why there might be a problem, which is why we’d then want to follow up with inspection activity to check if that is right, what the source of the problem is and what the force is doing about it.

“We want to make communities safer, so it’s not just about pointing out problems, it’s trying to support forces to improve those as well.”

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