quantitative capstone project

Quantitative Capstone

The mcah quantitative capstone reserach project.

As part of the requirement for the MPH, each MCAH student must complete a quantitative capstone research project. This project may also be referred to as the program's " Integrative Learning Experience ." The project consists of a written and an oral component and is considered to be the comprehensive examination for MCAH students. The MCAH capstone seminars (PH 210E & 210F: Practicum in MCAH Data Analysis I and II) are designed to support students in the capstone process.

The purpose of the MCAH capstone research project is to engage students in learning:

How to formulate a good research question

How to identify the appropriate methods to address the research question

How to test the research question with empirical data

How to analyze/interpret/summarize and present the findings, linking these to the existing body of scientific literature

How to present the findings in a formal paper and link them to implications for policy 

How to orally present and defend research findings in an academic setting

The goals of the MCAH capstone research project are to:

Demonstrate that the student has mastered each of the skills necessary to complete an in-depth quantitatively based research project

Ensure that the student has attained an in-depth understanding of and exposure to specific MCAH topics that are relevant to their career goals and interests

Demonstrate proficiency of the topic researched through a written paper and through oral presentation of the research findings to one’s peers and faculty

Components of the Capstone Project

Each student will have a Capstone chair, who will read the entire paper and provide detailed feedback. In some cases, the student may also have 1-2 additional members who will meet periodically with them to discuss the research question and findings and give advice on the project, but who might not read the entire paper.

The topic for the capstone project is the student’s choice, based upon their interests. Students will not be allowed to pursue a descriptive study, such as a needs assessment, a qualitative study, or a mixed-methods study. Such studies, while valuable, do not provide students with the opportunity to exercise their analytic skills to the extent necessary for a master’s degree.

Each student must identify and obtain a dataset that they can use for their research. This may be a publicly available dataset, data collected by BPH faculty or collaborators, or data from a job or summer internship, etc. All research activities for the capstone must be approved by the UC Berkeley Committee for the Protection of Human Subjects (CPHS) before activities begin.

The written portion of the capstone is structured as a standard pre-publication manuscript. Approval of the Capstone manuscript is similar to the process of having an article accepted for publication in a peer reviewed journal.

Oral Presentation

In addition to submitting a written portion of the capstone to the Committee, the student will be required to give a final oral presentation of their research study and findings. The presentation component serves as the oral examination of the comprehensive exam.

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Capstone Projects

Mqe students solve real-world client challenges.

Through the  Capstone Course , MQE students work closely with organizations to identify industry challenges, analyze data, and present effective results.

According to MQE students they accomplished:

"building a working text scraper that can potentially generate a large data frame" "working on a linear model" "being adaptable to create a final deliverable that filled all of the needs of our client"   "utilizing machine learning and ways of thinking about the problem. The exposure to matching learning models will allow me to have more tools at my disposal to solve problems" "how to quickly adjust to subject matter and see the process of tackling large projects" "always explaining the big picture"

2023 Capstone Projects

• Pittsburgh Parks Conservancy

2022 Capstone Projects

• Buffalo Sabres
• City of Pittsburgh
• Fourth Economy

2021 Capstone Projects

• Allegheny County Department of Human Services
• Pittsburgh Penguins

For organizations and professionals interested in the  MQE Partner Program  and employee recruitment, please contact [email protected]. 

AP Capstone Project: Quantitative Research

• APA Format & Paper Organization
• Quantitative Research
• How to Avoid Scientific Misconduct

Why is research so important?

Conducting Research

Even if you have written papers for english or social studies, research for science projects is  different:.

• Primary literature  has a different meaning in the sciences. Scientists refer to published articles that describe actual research as  primary literature.  This means that what humanities scholars call secondary sources  is primary literature in the sciences.
• Newer is better.  Most scientific information is time sensitive and always changing as knowledge grows. This means that for many fields (Geology, mathematics, and history of science are exceptions.), the most current articles are the most accurate and useful.
• Open access  means that some scholarly science journals, mainly in medical biology and in physics, are freely available on the web. Open access occurs when there is strong competition between publishers and/or government intervention.
• Copyright  effects access. If journal articles are not freely available due to open access, they are locked down due to copyright. This means you can only find them through full text databases such as those on EBSCO SmartSearch or at libraries that have print or electronic subscriptions.  
• Quantitative research, also called "empirical research," refers to any research based on something that can be accurately and precisely measured. Hint: think "quantity=amount"

Adapted from Geier Library LibGuide

Qualitative research is...

Considered hard science

Deductive reasoning used to synthesize data

Focused—concise and narrow

Tests theory

Basis of knowing—cause and effect relationships

Basic element of analysis—numbers and statistical analysis

Single reality that can be measured and  generalized

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• Last Updated: Apr 11, 2018 12:03 PM
• URL: https://shhs-southhadleyschools.libguides.com/apcapstone

General Catalog

Master of quantitative economics capstone project, 1 to 4 units.

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Data Literacy Capstone – Evaluating Research

This course is part of Data Literacy Specialization

Taught in English

Some content may not be translated

Instructor: Jennifer Bachner, PhD

Financial aid available

Recommended experience

Intermediate level

Completion of the first four courses in the Data Literacy Specialization

Skills you'll gain

• Research Methods
• research evaluation

Details to know

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There are 3 modules in this course

This is the final course in the Data Literacy Specialization. In this capstone course, you'll apply the skills and knowledge you have acquired in the specialization to the critical evaluation of an original quantitative analysis. The project will first require you to identify and read a piece of high-quality, original, quantitative research on a topic of your choosing. You’ll then interpret and evaluate the findings as well as the methodological approach. As part of the project, you’ll also review other students’ submissions. By the end of the project, you should be empowered to be a critical consumer and user of quantitative research.

Capstone Project Overview

Welcome to the Capstone Course for the Data Literacy Specialization! The capstone project will allow you to apply the knowledge and skills you've acquired throughout the specialization to the evaluation of a published piece of scholarly work. In this first module, I'll present an overview of the project's purpose and components.

What's included

1 video 1 reading 1 quiz

1 video • Total 3 minutes

• Capstone Project Overview • 3 minutes • Preview module

1 reading • Total 60 minutes

• Measuring Research • 60 minutes

1 quiz • Total 5 minutes

• Capstone Project Overview Practice Problems • 5 minutes

Locating Quality Scholarship

In this module, I'll present information that will help you identify an article or report that you'll evaluate for the capstone project. We'll explore sources that frequently publish high-quality, original, quantitative research. We'll also discuss the difference between primary and secondary research.

1 video 2 readings 1 quiz

1 video • Total 5 minutes

• Research Selection • 5 minutes • Preview module

2 readings • Total 30 minutes

• Think Tank Search • 15 minutes
• Evaluating Think Tanks • 15 minutes

1 quiz • Total 8 minutes

• Research Selection Practice Problems • 8 minutes

Final Paper Submission

Welcome to the final module in the Data Literacy Specialization. In this module, you'll complete and submit your capstone project via the peer review system. You'll also review three submissions from other students in the specialization. I hope this specialization has given you a set of tools and skills that empower you to be a critical consumer and user of quantitative research.

1 peer review

1 peer review • Total 240 minutes

• Data Literacy Capstone Project • 240 minutes

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

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What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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Want to participate?

Visit the qtm prep website to learn more and submit your application, potential partner inquiries, contact the outreach and career development coordinator, capstone applicants, complete your application by oct. 15th for early consideration., qtm capstone: experiential learning at its finest, spring 2024 capstone projects.

The QTM capstone course offers distinctive collaborations across college and the real world. This opportunity is intended for advanced QTM majors (i.e., seniors and highly qualified seniors).

Partners each bring unique proprietary challenges to faculty-led teams of student investigators who apply disciplinary knowledge and substantive expertise to the questions they are presented. The QTM capstone is your opportunity to synthesize everything you have learned in your classes and make a real impact.

  Over the course of the semester, student teams will...

• learn the relevant background information and business constraints in order to define the research problem
• collaborate weekly to bring theoretically sound, analytically rigorous, creative solutions and insights to specific challenges
• participate in weekly lab sections intended to integrate professional communication, presentation, and writing skills into the overall experience
• provide partners periodic updates
• make concluding formal presentations of findings/insights, applicable models/code, and recommended courses of action to partner stakeholders and university guests

All of this happens with the generous support and oversight from...

• dedicated faculty leads
• departmental staff
• partner liaisons
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Capstone Components

12 Research Design

The story continues….

“So, how do we go about answering our research questions?” asked Harry.

Physicus explained that they will have to analyze their questions to see what types of answers are required. Knowing this will guide their decisions about how to design the needs assessment to answer their questions.

“There are two basic types of answers to research questions, quantitative and qualitative. The types of answers the questions require tell us what type of research design we need,” said Physicus.

“I guess if I ask how we decide which type of research design we should choose, you will say, ‘It depends?'” uttered Harry.

Physicus’ face brightened as he blurted out, “Absolutely not! Negative!” Physicus continued, “If the research questions are stated well, there will only be two ways in which they can be answered. The research questions are king; they make all the decisions.”

“How come?” Harry appeared confused.

“Well, let us see. Think about our first question. How many mice will Pickles attack at one time? What type of answer does this question require? It requires a numeric answer, correct?” Physicus asked.

“Yes, that is correct,” Harry said.

Physicus continued, “Good. So, does our second question also require a numeric answer?”

“The second question is also answered with a number,” replied Harry

Physicus blurted, “Correct! This means we need to use a quantitative research design!”

Physicus continued, “Now if we had research questions that could not be answered with numbers, we would need to use a qualitative research design to answer our questions with words or phrases instead.”

Harry now appeared relieved, “I get it. So in designing a research project, we simply look for a way to answer the research questions. That’s easy!”

“Well, it depends,” answered Physicus smiling.

Interpreting the Story

There are qualitative, quantitative, mixed methods, and applied research designs. Based on the research questions, the research design will be obvious. Physicus led Harry in determining their investigation would need a quantitative design, because they only needed numerical data to answer their research questions. If Harry’s questions could only be answered with words or phrases, then a qualitative design would be needed. If the friends had questions needing to be answered with numbers and phrases, then either a mixed methods or an applied research design would have been the choice.

Research Design

The Research Design explains what type of research is being conducted in the needs assessment. The writing in this heading also explains why this type of research is needed to obtain the answers to the research or guiding questions for the project. The design provides a blueprint for the methodology. Articulating the nature of the research design is critical for explaining the Methodology (see the next chapter).

There are four categories of research designs used in educational research and a variety of specific research designs in each category. The first step in determining which category to use is to identify what type of data will answer the research questions. As in our story, Harry and Physicus had research questions that required quantitative answers, so the category of their research design is quantitative.

The next step in finding the specific research design is to consider the purpose (goal) of the research project. The research design must support the purpose. In our story, Harry and Physicus need a quantitative research design that supports their goal of determining the effect of the number of mice Pickles encounters at one time on his behavior.  A causal-comparative or quasi-experimental research design is the best choice for the friends because these are specific quantitative designs used to find a cause-and-effect relationship.

Quantitative Research Designs

Quantitative research designs seek results based on statistical analyses of the collected numerical data. The primary quantitative designs used in educational research include descriptive, correlational, causal-comparative, and quasi-experimental designs. Numerical data are collected and analyzed using statistical calculations appropriate for the design. For example, analyses like mean, median, mode, range, etc. are used to describe or explain a phenomenon observed in a descriptive research design. A correlational research design uses statistics, such as correlation coefficient or regression analyses to explain how two phenomena are related. Causal-comparative and quasi-experimental designs use analyses needed to establish causal relationships, such as pre-post testing, or behavior change (like in our story).

The use of numerical data guides both the methodology and the analysis protocols. The design also guides and limits how the results are interpreted. Examples of quantitative data found in educational research include test scores, grade point averages, and dropout rates.

Qualitative Research Designs

Qualitative research designs involve obtaining verbal, perspective, and/or visual results using code-based analyses of collected data. Typical qualitative designs used in educational research include the case study, phenomenological, grounded theory, and ethnography. These designs involve exploring behaviors, perceptions/feelings, and social/cultural phenomena found in educational settings.

Qualitative designs result in a written description of the findings. Data collection strategies include observations, interviews, focus groups, surveys, and documentation reviews. The data are recorded as words, phrases, sentences, and paragraphs. Data are then grouped together to form themes. The process of grouping data to form themes is called coding. The labeled themes become the “code” used to interpret the data. The coding can be determined ahead of time before data are collected, or the coding emerges from the collected data. Data collection strategies often include media such as video and audio recordings. These recordings are transcribed into words to allow for the coding analysis.

The use of qualitative data guides both the methodology and the analysis protocols. The “squishy” nature of qualitative data (words vs. numbers) and the data coding analysis limits the interpretation and conclusions made from the results. It is important to explain the coding analysis used to provide clear reasoning for the themes and how these relate to the research questions.

Mixed Method Designs

Mixed Methods research designs are used when the research questions must be answered with results that are both quantitative and qualitative. These designs integrate the data results to arrive at conclusions. A mixed method design is used when there are greater benefits to using multiple data types, sources, and analyses. Examples of typical mixed methods design approaches in education include convergent, explanatory, exploratory, and embedded designs. Using mixed methods approaches in educational research allows the researcher to triangulate, complement, or expand understanding using multiple types of data.

The use of mixed methods data guides the methodology, analysis, and interpretation of the results. Using both qualitative (quant) and quantitative (qual) data analyses provides a clearer or more balanced picture of the results. Data are analyzed sequentially or concurrently depending on the design. While the quantitative and qualitative data are analyzed independently, the results are interpreted integratively. The findings are a synthesis of the quantitative and qualitative analyses.

Applied Research Designs

Applied research designs seek both quantitative and qualitative results to address issues of educational practice. Applied research designs include evaluation, design and development, and action research. The purposes of applied research are to identify best practices, to innovate or improve current practices or policies, to test pedagogy, and to evaluate effectiveness. The results of applied research designs provide practical solutions to problems in educational practice.

Applied designs use both theoretical and empirical data. Theoretical data are collected from published theories or other research. Empirical data are obtained by conducting a needs assessment or other data collection methods. Data analyses include both quantitative and qualitative procedures. The findings are interpreted integratively as in mixed methods approaches, and then “applied” to the problem to form a solution.

Telling the research story

The Research Design in a research project tells the story of what direction the plot of the story will take.  The writing in this heading sets the stage for the rising action of the plot in the research story. The Research Design describes the journey that is about to take place. It functions to guide the reader in understanding the type of path the story will follow. The Research Design is the overall direction of the research story and is determined before deciding on the specific steps to take in obtaining and analyzing the data.

The Research Design heading appears in Chapter 2 of a capstone project. In the capstone project, the Research Design explains the type of design used for conducting the needs assessment.

Capstone Projects in Education: Learning the Research Story Copyright © 2023 by Kimberly Chappell and Greg I. Voykhansky is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

200+ Experimental Quantitative Research Topics For STEM Students In 2023

STEM means Science, Technology, Engineering, and Math, which is not the only stuff we learn in school. It is like a treasure chest of skills that help students become great problem solvers, ready to tackle the real world’s challenges.

In this blog, we are here to explore the world of Research Topics for STEM Students. We will break down what STEM really means and why it is so important for students. In addition, we will give you the lowdown on how to pick a fascinating research topic. We will explain a list of 200+ Experimental Quantitative Research Topics For STEM Students.

And when it comes to writing a research title, we will guide you step by step. So, stay with us as we unlock the exciting world of STEM research – it is not just about grades; it is about growing smarter, more confident, and happier along the way.

What Is STEM?

Table of Contents

STEM is Science, Technology, Engineering, and Mathematics. It is a way of talking about things like learning, jobs, and activities related to these four important subjects. Science is about understanding the world around us, technology is about using tools and machines to solve problems, engineering is about designing and building things, and mathematics is about numbers and solving problems with them. STEM helps us explore, discover, and create cool stuff that makes our world better and more exciting.

Why STEM Research Is Important?

STEM research is important because it helps us learn new things about the world and solve problems. When scientists, engineers, and mathematicians study these subjects, they can discover cures for diseases, create new technology that makes life easier, and build things that help us live better. It is like a big puzzle where we put together pieces of knowledge to make our world safer, healthier, and more fun.

• STEM research leads to new discoveries and solutions.
• It helps find cures for diseases.
• STEM technology makes life easier.
• Engineers build things that improve our lives.
• Mathematics helps us understand and solve complex problems.

How to Choose a Topic for STEM Research Paper

Here are some steps to choose a topic for STEM Research Paper:

Step 1: Identify Your Interests

Think about what you like and what excites you in science, technology, engineering, or math. It could be something you learned in school, saw in the news, or experienced in your daily life. Choosing a topic you’re passionate about makes the research process more enjoyable.

Step 2: Research Existing Topics

Look up different STEM research areas online, in books, or at your library. See what scientists and experts are studying. This can give you ideas and help you understand what’s already known in your chosen field.

Step 3: Consider Real-World Problems

Think about the problems you see around you. Are there issues in your community or the world that STEM can help solve? Choosing a topic that addresses a real-world problem can make your research impactful.

Step 4: Talk to Teachers and Mentors

Discuss your interests with your teachers, professors, or mentors. They can offer guidance and suggest topics that align with your skills and goals. They may also provide resources and support for your research.

Step 5: Narrow Down Your Topic

Once you have some ideas, narrow them down to a specific research question or project. Make sure it’s not too broad or too narrow. You want a topic that you can explore in depth within the scope of your research paper.

Here we will discuss 200+ Experimental Quantitative Research Topics For STEM Students: 

Qualitative Research Topics for STEM Students:

Qualitative research focuses on exploring and understanding phenomena through non-numerical data and subjective experiences. Here are 10 qualitative research topics for STEM students:

• Exploring the experiences of female STEM students in overcoming gender bias in academia.
• Understanding the perceptions of teachers regarding the integration of technology in STEM education.
• Investigating the motivations and challenges of STEM educators in underprivileged schools.
• Exploring the attitudes and beliefs of parents towards STEM education for their children.
• Analyzing the impact of collaborative learning on student engagement in STEM subjects.
• Investigating the experiences of STEM professionals in bridging the gap between academia and industry.
• Understanding the cultural factors influencing STEM career choices among minority students.
• Exploring the role of mentorship in the career development of STEM graduates.
• Analyzing the perceptions of students towards the ethics of emerging STEM technologies like AI and CRISPR.
• Investigating the emotional well-being and stress levels of STEM students during their academic journey.

Easy Experimental Research Topics for STEM Students:

These experimental research topics are relatively straightforward and suitable for STEM students who are new to research:

•  Measuring the effect of different light wavelengths on plant growth.
•  Investigating the relationship between exercise and heart rate in various age groups.
•  Testing the effectiveness of different insulating materials in conserving heat.
•  Examining the impact of pH levels on the rate of chemical reactions.
•  Studying the behavior of magnets in different temperature conditions.
•  Investigating the effect of different concentrations of a substance on bacterial growth.
•  Testing the efficiency of various sunscreen brands in blocking UV radiation.
•  Measuring the impact of music genres on concentration and productivity.
•  Examining the correlation between the angle of a ramp and the speed of a rolling object.
•  Investigating the relationship between the number of blades on a wind turbine and energy output.

Research Topics for STEM Students in the Philippines:

These research topics are tailored for STEM students in the Philippines:

•  Assessing the impact of climate change on the biodiversity of coral reefs in the Philippines.
•  Studying the potential of indigenous plants in the Philippines for medicinal purposes.
•  Investigating the feasibility of harnessing renewable energy sources like solar and wind in rural Filipino communities.
•  Analyzing the water quality and pollution levels in major rivers and lakes in the Philippines.
•  Exploring sustainable agricultural practices for small-scale farmers in the Philippines.
•  Assessing the prevalence and impact of dengue fever outbreaks in urban areas of the Philippines.
•  Investigating the challenges and opportunities of STEM education in remote Filipino islands.
•  Studying the impact of typhoons and natural disasters on infrastructure resilience in the Philippines.
•  Analyzing the genetic diversity of endemic species in the Philippine rainforests.
•  Assessing the effectiveness of disaster preparedness programs in Philippine communities.

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Good Research Topics for STEM Students:

These research topics are considered good because they offer interesting avenues for investigation and learning:

•  Developing a low-cost and efficient water purification system for rural communities.
•  Investigating the potential use of CRISPR-Cas9 for gene therapy in genetic disorders.
•  Studying the applications of blockchain technology in securing medical records.
•  Analyzing the impact of 3D printing on customized prosthetics for amputees.
•  Exploring the use of artificial intelligence in predicting and preventing forest fires.
•  Investigating the effects of microplastic pollution on aquatic ecosystems.
•  Analyzing the use of drones in monitoring and managing agricultural crops.
•  Studying the potential of quantum computing in solving complex optimization problems.
•  Investigating the development of biodegradable materials for sustainable packaging.
•  Exploring the ethical implications of gene editing in humans.

Unique Research Topics for STEM Students:

Unique research topics can provide STEM students with the opportunity to explore unconventional and innovative ideas. Here are 10 unique research topics for STEM students:

•  Investigating the use of bioluminescent organisms for sustainable lighting solutions.
•  Studying the potential of using spider silk proteins for advanced materials in engineering.
•  Exploring the application of quantum entanglement for secure communication in the field of cryptography.
•  Analyzing the feasibility of harnessing geothermal energy from underwater volcanoes.
•  Investigating the use of CRISPR-Cas12 for rapid and cost-effective disease diagnostics.
•  Studying the interaction between artificial intelligence and human creativity in art and music generation.
•  Exploring the development of edible packaging materials to reduce plastic waste.
•  Investigating the impact of microgravity on cellular behavior and tissue regeneration in space.
•  Analyzing the potential of using sound waves to detect and combat invasive species in aquatic ecosystems.
•  Studying the use of biotechnology in reviving extinct species, such as the woolly mammoth.

Experimental Research Topics for STEM Students in the Philippines

Research topics for STEM students in the Philippines can address specific regional challenges and opportunities. Here are 10 experimental research topics for STEM students in the Philippines:

•  Assessing the effectiveness of locally sourced materials for disaster-resilient housing construction in typhoon-prone areas.
•  Investigating the utilization of indigenous plants for natural remedies in Filipino traditional medicine.
•  Studying the impact of volcanic soil on crop growth and agriculture in volcanic regions of the Philippines.
•  Analyzing the water quality and purification methods in remote island communities.
•  Exploring the feasibility of using bamboo as a sustainable construction material in the Philippines.
•  Investigating the potential of using solar stills for freshwater production in water-scarce regions.
•  Studying the effects of climate change on the migration patterns of bird species in the Philippines.
•  Analyzing the growth and sustainability of coral reefs in marine protected areas.
•  Investigating the utilization of coconut waste for biofuel production.
•  Studying the biodiversity and conservation efforts in the Tubbataha Reefs Natural Park.

Capstone Research Topics for STEM Students in the Philippines:

Capstone research projects are often more comprehensive and can address real-world issues. Here are 10 capstone research topics for STEM students in the Philippines:

•  Designing a low-cost and sustainable sanitation system for informal settlements in urban Manila.
•  Developing a mobile app for monitoring and reporting natural disasters in the Philippines.
•  Assessing the impact of climate change on the availability and quality of drinking water in Philippine cities.
•  Designing an efficient traffic management system to address congestion in major Filipino cities.
•  Analyzing the health implications of air pollution in densely populated urban areas of the Philippines.
•  Developing a renewable energy microgrid for off-grid communities in the archipelago.
•  Assessing the feasibility of using unmanned aerial vehicles (drones) for agricultural monitoring in rural Philippines.
•  Designing a low-cost and sustainable aquaponics system for urban agriculture.
•  Investigating the potential of vertical farming to address food security in densely populated urban areas.
•  Developing a disaster-resilient housing prototype suitable for typhoon-prone regions.

Experimental Quantitative Research Topics for STEM Students:

Experimental quantitative research involves the collection and analysis of numerical data to conclude. Here are 10 Experimental Quantitative Research Topics For STEM Students interested in experimental quantitative research:

•  Examining the impact of different fertilizers on crop yield in agriculture.
•  Investigating the relationship between exercise and heart rate among different age groups.
•  Analyzing the effect of varying light intensities on photosynthesis in plants.
•  Studying the efficiency of various insulation materials in reducing building heat loss.
•  Investigating the relationship between pH levels and the rate of corrosion in metals.
•  Analyzing the impact of different concentrations of pollutants on aquatic ecosystems.
•  Examining the effectiveness of different antibiotics on bacterial growth.
•  Trying to figure out how temperature affects how thick liquids are.
•  Finding out if there is a link between the amount of pollution in the air and lung illnesses in cities.
•  Analyzing the efficiency of solar panels in converting sunlight into electricity under varying conditions.

Descriptive Research Topics for STEM Students

Descriptive research aims to provide a detailed account or description of a phenomenon. Here are 10 topics for STEM students interested in descriptive research:

•  Describing the physical characteristics and behavior of a newly discovered species of marine life.
•  Documenting the geological features and formations of a particular region.
•  Creating a detailed inventory of plant species in a specific ecosystem.
•  Describing the properties and behavior of a new synthetic polymer.
•  Documenting the daily weather patterns and climate trends in a particular area.
•  Providing a comprehensive analysis of the energy consumption patterns in a city.
•  Describing the structural components and functions of a newly developed medical device.
•  Documenting the characteristics and usage of traditional construction materials in a region.
•  Providing a detailed account of the microbiome in a specific environmental niche.
•  Describing the life cycle and behavior of a rare insect species.

Research Topics for STEM Students in the Pandemic:

The COVID-19 pandemic has raised many research opportunities for STEM students. Here are 10 research topics related to pandemics:

•  Analyzing the effectiveness of various personal protective equipment (PPE) in preventing the spread of respiratory viruses.
•  Studying the impact of lockdown measures on air quality and pollution levels in urban areas.
•  Investigating the psychological effects of quarantine and social isolation on mental health.
•  Analyzing the genomic variation of the SARS-CoV-2 virus and its implications for vaccine development.
•  Studying the efficacy of different disinfection methods on various surfaces.
•  Investigating the role of contact tracing apps in tracking & controlling the spread of infectious diseases.
•  Analyzing the economic impact of the pandemic on different industries and sectors.
•  Studying the effectiveness of remote learning in STEM education during lockdowns.
•  Investigating the social disparities in healthcare access during a pandemic.
• Analyzing the ethical considerations surrounding vaccine distribution and prioritization.

Research Topics for STEM Students Middle School

Research topics for middle school STEM students should be engaging and suitable for their age group. Here are 10 research topics:

• Investigating the growth patterns of different types of mold on various food items.
• Studying the negative effects of music on plant growth and development.
• Analyzing the relationship between the shape of a paper airplane and its flight distance.
• Investigating the properties of different materials in making effective insulators for hot and cold beverages.
• Studying the effect of salt on the buoyancy of different objects in water.
• Analyzing the behavior of magnets when exposed to different temperatures.
• Investigating the factors that affect the rate of ice melting in different environments.
• Studying the impact of color on the absorption of heat by various surfaces.
• Analyzing the growth of crystals in different types of solutions.
• Investigating the effectiveness of different natural repellents against common pests like mosquitoes.

Technology Research Topics for STEM Students

Technology is at the forefront of STEM fields. Here are 10 research topics for STEM students interested in technology:

• Developing and optimizing algorithms for autonomous drone navigation in complex environments.
• Exploring the use of blockchain technology for enhancing the security and transparency of supply chains.
• Investigating the applications of virtual reality (VR) and augmented reality (AR) in medical training and surgery simulations.
• Studying the potential of 3D printing for creating personalized prosthetics and orthopedic implants.
• Analyzing the ethical and privacy implications of facial recognition technology in public spaces.
• Investigating the development of quantum computing algorithms for solving complex optimization problems.
• Explaining the use of machine learning and AI in predicting and mitigating the impact of natural disasters.
• Studying the advancement of brain-computer interfaces for assisting individuals with
• disabilities.
• Analyzing the role of wearable technology in monitoring and improving personal health and wellness.
• Investigating the use of robotics in disaster response and search and rescue operations.

Scientific Research Topics for STEM Students

Scientific research encompasses a wide range of topics. Here are 10 research topics for STEM students focusing on scientific exploration:

• Investigating the behavior of subatomic particles in high-energy particle accelerators.
• Studying the ecological impact of invasive species on native ecosystems.
• Analyzing the genetics of antibiotic resistance in bacteria and its implications for healthcare.
• Exploring the physics of gravitational waves and their detection through advanced interferometry.
• Investigating the neurobiology of memory formation and retention in the human brain.
• Studying the biodiversity and adaptation of extremophiles in harsh environments.
• Analyzing the chemistry of deep-sea hydrothermal vents and their potential for life beyond Earth.
• Exploring the properties of superconductors and their applications in technology.
• Investigating the mechanisms of stem cell differentiation for regenerative medicine.
• Studying the dynamics of climate change and its impact on global ecosystems.

Interesting Research Topics for STEM Students:

Engaging and intriguing research topics can foster a passion for STEM. Here are 10 interesting research topics for STEM students:

• Exploring the science behind the formation of auroras and their cultural significance.
• Investigating the mysteries of dark matter and dark energy in the universe.
• Studying the psychology of decision-making in high-pressure situations, such as sports or
• emergencies.
• Analyzing the impact of social media on interpersonal relationships and mental health.
• Exploring the potential for using genetic modification to create disease-resistant crops.
• Investigating the cognitive processes involved in solving complex puzzles and riddles.
• Studying the history and evolution of cryptography and encryption methods.
• Analyzing the physics of time travel and its theoretical possibilities.
• Exploring the role of Artificial Intelligence  in creating art and music.
• Investigating the science of happiness and well-being, including factors contributing to life satisfaction.

Practical Research Topics for STEM Students

Practical research often leads to real-world solutions. Here are 10 practical research topics for STEM students:

• Developing an affordable and sustainable water purification system for rural communities.
• Designing a low-cost, energy-efficient home heating and cooling system.
• Investigating strategies for reducing food waste in the supply chain and households.
• Studying the effectiveness of eco-friendly pest control methods in agriculture.
• Analyzing the impact of renewable energy integration on the stability of power grids.
• Developing a smartphone app for early detection of common medical conditions.
• Investigating the feasibility of vertical farming for urban food production.
• Designing a system for recycling and upcycling electronic waste.
• Studying the environmental benefits of green roofs and their potential for urban heat island mitigation.
• Analyzing the efficiency of alternative transportation methods in reducing carbon emissions.

Experimental Research Topics for STEM Students About Plants

Plants offer a rich field for experimental research. Here are 10 experimental research topics about plants for STEM students:

• Investigating the effect of different light wavelengths on plant growth and photosynthesis.
• Studying the impact of various fertilizers and nutrient solutions on crop yield.
• Analyzing the response of plants to different types and concentrations of plant hormones.
• Investigating the role of mycorrhizal in enhancing nutrient uptake in plants.
• Studying the effects of drought stress and water scarcity on plant physiology and adaptation mechanisms.
• Analyzing the influence of soil pH on plant nutrient availability and growth.
• Investigating the chemical signaling and defense mechanisms of plants against herbivores.
• Studying the impact of environmental pollutants on plant health and genetic diversity.
• Analyzing the role of plant secondary metabolites in pharmaceutical and agricultural applications.
• Investigating the interactions between plants and beneficial microorganisms in the rhizosphere.

Qualitative Research Topics for STEM Students in the Philippines

Qualitative research in the Philippines can address local issues and cultural contexts. Here are 10 qualitative research topics for STEM students in the Philippines:

• Exploring indigenous knowledge and practices in sustainable agriculture in Filipino communities.
• Studying the perceptions and experiences of Filipino fishermen in coping with climate change impacts.
• Analyzing the cultural significance and traditional uses of medicinal plants in indigenous Filipino communities.
• Investigating the barriers and facilitators of STEM education access in remote Philippine islands.
• Exploring the role of traditional Filipino architecture in natural disaster resilience.
• Studying the impact of indigenous farming methods on soil conservation and fertility.
• Analyzing the cultural and environmental significance of mangroves in coastal Filipino regions.
• Investigating the knowledge and practices of Filipino healers in treating common ailments.
• Exploring the cultural heritage and conservation efforts of the Ifugao rice terraces.
• Studying the perceptions and practices of Filipino communities in preserving marine biodiversity.

Science Research Topics for STEM Students

Science offers a diverse range of research avenues. Here are 10 science research topics for STEM students:

• Investigating the potential of gene editing techniques like CRISPR-Cas9 in curing genetic diseases.
• Studying the ecological impacts of species reintroduction programs on local ecosystems.
• Analyzing the effects of microplastic pollution on aquatic food webs and ecosystems.
• Investigating the link between air pollution and respiratory health in urban populations.
• Studying the role of epigenetics in the inheritance of acquired traits in organisms.
• Analyzing the physiology and adaptations of extremophiles in extreme environments on Earth.
• Investigating the genetics of longevity and factors influencing human lifespan.
• Studying the behavioral ecology and communication strategies of social insects.
• Analyzing the effects of deforestation on global climate patterns and biodiversity loss.
• Investigating the potential of synthetic biology in creating bioengineered organisms for beneficial applications.

Correlational Research Topics for STEM Students

Correlational research focuses on relationships between variables. Here are 10 correlational research topics for STEM students:

• Analyzing the correlation between dietary habits and the incidence of chronic diseases.
• Studying the relationship between exercise frequency and mental health outcomes.
• Investigating the correlation between socioeconomic status and access to quality healthcare.
• Analyzing the link between social media usage and self-esteem in adolescents.
• Studying the correlation between academic performance and sleep duration among students.
• Investigating the relationship between environmental factors and the prevalence of allergies.
• Analyzing the correlation between technology use and attention span in children.
• Studying how environmental factors are related to the frequency of allergies.
• Investigating the link between parental involvement in education and student achievement.
• Analyzing the correlation between temperature fluctuations and wildlife migration patterns.

Quantitative Research Topics for STEM Students in the Philippines

Quantitative research in the Philippines can address specific regional issues. Here are 10 quantitative research topics for STEM students in the Philippines

• Analyzing the impact of typhoons on coastal erosion rates in the Philippines.
• Studying the quantitative effects of land use change on watershed hydrology in Filipino regions.
• Investigating the quantitative relationship between deforestation and habitat loss for endangered species.
• Analyzing the quantitative patterns of marine biodiversity in Philippine coral reef ecosystems.
• Studying the quantitative assessment of water quality in major Philippine rivers and lakes.
• Investigating the quantitative analysis of renewable energy potential in specific Philippine provinces.
• Analyzing the quantitative impacts of agricultural practices on soil health and fertility.
• Studying the quantitative effectiveness of mangrove restoration in coastal protection in the Philippines.
• Investigating the quantitative evaluation of indigenous agricultural practices for sustainability.
• Analyzing the quantitative patterns of air pollution and its health impacts in urban Filipino areas.

Things That Must Keep In Mind While Writing Quantitative Research Title 

Here are few things that must be keep in mind while writing quantitative research tile:

1. Be Clear and Precise

Make sure your research title is clear and says exactly what your study is about. People should easily understand the topic and goals of your research by reading the title.

2. Use Important Words

Include words that are crucial to your research, like the main subjects, who you’re studying, and how you’re doing your research. This helps others find your work and understand what it’s about.

3. Avoid Confusing Words

Stay away from words that might confuse people. Your title should be easy to grasp, even if someone isn’t an expert in your field.

4. Show Your Research Approach

Tell readers what kind of research you did, like experiments or surveys. This gives them a hint about how you conducted your study.

5. Match Your Title with Your Research Questions

Make sure your title matches the questions you’re trying to answer in your research. It should give a sneak peek into what your study is all about and keep you on the right track as you work on it.

STEM students, addressing what STEM is and why research matters in this field. It offered an extensive list of research topics , including experimental, qualitative, and regional options, catering to various academic levels and interests. Whether you’re a middle school student or pursuing advanced studies, these topics offer a wealth of ideas. The key takeaway is to choose a topic that resonates with your passion and aligns with your goals, ensuring a successful journey in STEM research. Choose the best Experimental Quantitative Research Topics For Stem Students today!

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Course Offerings

• Applied Projects
• Distinguished Speaker Series

Explore Our Rigorous Curriculum

UCLA’s MQE is a 48-unit program that features a flexible timeline, so you complete your degree in 9 to 18 months. Throughout the program, you’ll gain exposure to R, Python, SQL, Excel and numerous financial tools and platforms. 

Create Your Course Schedule

Once you have completed required foundational and subject-area courses, you can create your own course schedule to mirror your interests. If you choose, you can complete a concentration in data analytics, finance, or international & monetary economics.

Courses for Career Success

Alumnus and faculty, Nathan Kunz ( Assistant Director, MQE Quant Lab and Lecturer ), shares how MQE courses can be tailored to diverse career goals and prepare students to be career ready.

Fall Course Offerings: Theoretical Foundations

Introduction to main topics of graduate macroeconomics, including macroeconomic data, models of economic growth, supply and demand of factors of production, business cycle models, unemployment, monetary policy and inflation, and fiscal policy and deficits.

Topics Covered:  

Introduction to the Macroeconomy

Production, Distribution, and the Markets for the Factors of Production (general equilibrium model focusing on labor and product markets, human capital accumulation, the determination of the distribution of income between labor and  

Consumption, Investment, and Savings (general equilibrium models focusing on capital markets, and the determinants of consumption and savings)

Financial Markets (process of financial intermediation and how it brings the suppliers and users of capital together; impact of technological change on financial intermediation; how venture capital promotes new firm creation)

Forecasting Methods (introducing simple and accurate methods, including smoothing, modeling trends, seasonal adjustment, ARIMA models)

Economic Growth (contributions of capital accumulation and technological change in the process of growth. Understanding how frontier growth and catch-up growth are fundamentally different. The process of innovation and technology adoption)

Global economy (link between economic openness, trade, and capital flows, and debtor and creditor nations. The role of money, inflation, and exchange rates. International policy coordination)

Through Econ 410, students will have the opportunity to attend several guest lectures and/or ‘mini courses’ hosted by the MQE. These distinguished speakers include noted academics, Nobel Laureates, government officials, and industry leaders. All have contributed greatly to their field and will impart knowledge and perspective making this a unique opportunity for students to learn about the key issues facing the world today. In addition, professional development seminars will teach students how to effectively translate their academic training for career opportunities.  Throughout the term students will have an opportunity to reflect on how their MQE coursework is helping to prepare them for future careers in applied and quantitative economics.

Designed to help students develop professional skills essential for success in professional business settings. Aids students in translating topics covered in other courses into language and format that is accessible to industry/non-academic settings. Students conduct labor market research, identify and analyze industry trends, and develop targeted plan to achieve professional success. Exploration of skills identification, goal setting, researching employment market, and resume writing.

Topics covered:

Career goals , finding fit, discovering opportunities; targeting applications

Job search strategies: employment research, job search techniques, analyzing labor market trends, skill development

Personal Branding:  marketing yourself for the job market; personal pitch; managing your brand

Professional writing ; Resume and cover letter writing

Introduction to probability, statistics, econometrics, and time series methods used in economics, business, and government using R and Python. Topics include estimation, simple and multiple regression, cross-sectional and panel data, instrumental variables, and estimation with stationary/non-stationary processes

Statistical Inference

Likelihood Inference

Exploring and Transforming Data

Linear Regression

Generalized Linear Models

Heteroskedasticity & Robust Estimation

IV – Two-Stage Least Squares; System Estimation

Cross-sectional & Panel Data

Time Series Introduction

Modeling and Forecasting Trend

Modeling and Forecasting Seasonality

ARMA & ARIMA

Regression with Time Series (VAR & VMA)

Volatility Modeling (ARCH & GARCH)  

Introduction to core principles of asset valuations. Emphasis on common economic reasoning used in valuation problems. Derivations and study of valuation formulas for three broad asset classes: fixed income securities, equity, and derivatives. Practical applications to investment problems, and relation to current financial news.

Topics Covered:

Interest rate analysis and fixed income markets (Time value of money: simple and compound interest rate, present value formulas, internal rate of return; Fixed income security pricing, Interest risk, duration and immunization; The term structure of interest rates: expectations hypothesis, liquidity preference theory, segmented market theory)

Portfolio choice and equilibrium asset pricing (Static portfolio choice for mean-variance investors: efficiency frontier, optimal portfolio, two-fund separation theorem; Static market equilibrium with mean-variance investors: market portfolio, security market line, idiosyncratic and systematic risk; Net present value for risky projects

Factor models (Equity valuation: the Gordon Growth Model and valuation ratios; Dynamic portfolio choice, hedging demand, macroeconomic determinant of interest rates and risk premia)

Derivatives (Arbitrage: the no-arbitrage condition, state prices, risk-neutral probabilities; Forward and future contracts: pricing, equivalence; Option basics: payoff, option portfolios, put-call parity; Binomial option pricing: binomial tree, dynamic hedging portfolio, delta; Merton-Black-Scholes option pricing: Itoˆ’s Lemma, Black-Scholes Formula, implied volatility, greeks; Options are everywhere: real options, embedded options, equity as an option, distance to default)

Market efficiency

Financial accounting is concerned with the preparation and public dissemination of financial reports designed to reflect corporate performance and financial condition. By providing timely, relevant, and reliable information, these reports facilitate the decision-making of investors, creditors, and other interested parties. Financial markets depend on the information contained in these reports to evaluate executives, estimate future stock returns, assess firms’ riskiness, and allocate society’s resources to their most productive uses.   This course provides a base level of knowledge needed by corporate executives to understand and discuss corporate financial statements. The process of learning how various business activities impact financial statements will also give you opportunities to learn and think about the business activities themselves. In addition, accounting provides a foundation for courses in other areas.

Balance Sheet; Income Statement

Transaction Analysis

Statement of cash flows

Current assets and Non-current assets

Liabilities

Shareholders’ Equity

Introduction to modern practices in data gathering, cleaning, and warehousing. Topics include Web scraping using API’s, engineering of R packages, and data manipulation in SQL. This course emphasizes applications of the data pipeline expected of an entry-level analyst. This instruction taught is offered as a supplement to the MAE coursework by providing solutions to expedite R coding techniques and the dissemination of analytic findings.

Survey of Relevant R packages for Data Import, Wrangling and Visualization

Creation of sharable R packages

GitHub Tutorial

API’s with R

Examples of API’s

SQL Commands

Uploading and editing SQL databases and Multitable commands

Data Visualizations With Excel, MySQL and NodeJS

Interactive data visualizations with R

Introduction to most requested data management tools in industry. Students gain hands-on experience with SQL database queries and database management through integrations with database management systems, query editors, and Python and R programming languages. Students practice saving advanced commands as stored procedures on collective database, simulating tasks seen in real world. Use of Excel and Visual Basic for Applications to make data cleaning, visualization, and data management processes more efficient.

This course explores fundamental analysis, a method of measuring a security’s value by assessing economic and financial factors.  Through lectures, readings, and interactive discussions, the course will explore macroeconomic and microeconomic factors that affect the intrinsic value of a security.  This experiential course is designed to deepen student exposure to the world of fundamental equity research through the research and development of an investment memorandum.  Students will also gain exposure to options through a series of lectures and applied activities. This course requires a basic understanding of finance and financial markets concepts and theories.

Designed to help students develop social-emotional learning skills through interactive activities and lessons to improve their abilities to succeed in variety of team settings. Lessons and activities are designed to be highly interactive, expressive, and creative and aid students in stress reduction, emotion management, and team building. Students are aided in translating topics covered in other courses into language and format that is accessible to industry/non-academic settings.

* Required Course

Winter Course Offerings: Applied Economics

• Phases of project management (Initiating, Planning, Executing, Monitoring & Controlling, Closing)
• Project management frameworks (Agile, Waterfall, P3.Express, etc.)
• Use and application of various industry tools utilized in a project (Jira, monday.com, Asana, Trello, etc.)

Introduction to recent developments in international finance. Coverage of lending booms and financial crises both theoretically and empirically, as well as foreign exchange market anomalies and different approaches to forecasting exchange rates.

Models of Exchange Rate Determination : Uncovered Interest Parity and exchange rates over the short-run; Monetary Policy, the Taylor Rule and interest rates; Forecasting a time series and the Kalman Filter; Foreign Exchange Rate Anomalies (Forward Premium Puzzle; Delayed Overshooting; The Carry Trade); Rationalizing the Anomalies (Rational expectations equilibria; Behavioral Finance and Cognitive biases); Purchasing Power Parity and exchange rates; Foreign Currency Futures and Options  

Forecasting Exchange rates and Strategy Design : Basic Structure of strategy coding; Entry and exit Order types; Filters; Creating Indicators and trading setups; Fundamentals-based forecasts; Sentiment-based forecasts; Forecasts based on Big-data

Evaluation of Forecasts and of Portfolio Strategies : The Sharpe Ratio and alternative statistics; Degree of Robustness of a strategy; The Diebold-Mariano and Clark-West tests against the random-walk; Binomial test of directional forecasts

Introduction to concepts of information economics that lie at heart of modern economics and application of them to understand incentives within firms, as well as competition between them. Study of theoretical models and functioning of real-life markets, such as insurance, labor, and consumer markets. Consideration of whether we can design policies that improve market outcomes. Role of models in economics, and how to tie data and theory together.

Introduction, Performance-Pay (welfare theorems, externalities and market power; information economics)

Performance Pay – Applications (Informativeness Principle; how to approach a Case Study)

Identification (Identification strategies)

Multi-tasking (how to incentivize agent who can manipulate performance measures, and how to motivate an agent to take multiple actions)

Teamwork and Team Incentives (use tournaments incentivize workers, collusion, free-ride

Long-term Incentives – Investment and Efficiency Wages (incentivized to work via relational contracts. We test this using car plants (Capelli, Chauvin) and fast-food restaurants (Krueger))

Asymmetric Information (examines how a firm overcame the ratchet effect; asymmetric info and derive the best way to price discriminate)

Pricing and Auctions (optimal way to price discriminate; examine the performance of auction vs posted-price on eBay)

Adverse Selection

Covers set of fundamental machine learning algorithms, models, and theories, and introduces advanced engineering practices for implementing data-intensive intelligent systems. Topics involve both supervised methods (e.g., support vector machine, neural network, etc.) and unsupervised methods (e.g., clustering, dimensionality reduction, etc.), and their applications in classification, regression, data analysis, and visualization.

Python Programming: List, Array, Data Processing and Visualization  

Introduction to Machine Learning  

Linear Models for Regression  

Implementation of Linear Regression and Its Variants (Lasso, Ridge)  

Linear Models for Classification Gradient Descent and its Variants  

Polynomial Expansions and Filtering Spline Models and Regularizations  

Feedforward Neural Networks LSTM and Time-Series Data  

Max-margin Learning and Line Support Model  

Kernel Support Vector Machine  

Bagging and Boosting Adaboost Algorithms  

Clustering and K-means methods Fuzzy C-means and Constrained Clustering  

PCA and Robust PCA ICA and Multidimensional Scaling  

Subspace Learning: NMF and LLE Visualization of High-Dimensional Data

Designed to help students develop professional skills essential for success in professional business settings. Aids students in translating topics covered in other courses into language and format that is accessible to industry/non-academic settings. Students  develop a targeted plan to achieve professional success.

Networking and the hidden job market

Leveraging social media: LinkedIn; online portfolios; GitHub

Interview strategies : preparation, behavioral interviews, industry-specific/technical interviews; mock interview lab

Presentation design and delivery:  audience targeting; persuasive presentations

Job and salary offer negotiation ; professional workplace skills

Data science provides many useful tools for modeling financial data and testing hypotheses on how markets work, and prices are formed. Study of these important tools. Focus on econometric models and methods to understand financial market dynamics. Topics include returns of financial assets, statistical tests on financial market efficiency, linear time series models, time-varying expected return models, heteroscedastic volatility models, optimal portfolio choice problem, capital asset pricing models, factor models, portfolio allocation, tracking and risk management.

Stylized Facts of Financial Data (Compounding and future value of asset; simple return and continuous compounding return; returns of portfolio)

Concepts in Probability and Risk Measure (Characteristics of distributions; Quantiles of a distribution; Value-at-Risk (VaR) and Expected Shortfall (ES); Bivariate distributions; Covariance, correlation, autocorrelation)

Statistical Methods and Risk Management (constant expected return model; descriptive statistics: histograms, sample means, sample variances/covariances; Standard errors of estimates; Confidence intervals; Estimation and inference of VaR and ES; Testing the efficient market hypothesis

Linear Time Series Models (Strictly stationarity and covariance stationarity; information set and conditional expectation; Martingale and martingale difference; ARMA process; Nonstationary time series; Random walk tests)

Heteroscedastic Volatility Models (Volatility clustering of asset returns; ARCH(1) model; GARCH(1,1) model; Estimation and inference of GARCH model

Portfolio Theory (Financial risk of portfolio; Portfolio frontier and efficient portfolios; Statistical analysis of efficient portfolios; Portfolio theory with matrix algebra)

Efficient Portfolios and the CAPM (CAPM Review; Sharpe-Lintner Version; Black Version; Validating the CAPM based on the statistical tests; Cross-sectional regression

Factor Pricing Model (Multifactor pricing model; Applications of multifactor models; Model validation with tradable factors; Selection of factors)

A course that will develop the data visualization toolkit of students using Tableau and Python packages. Focusses will be on techniques to simplistically communicate data, Excel functions/dashboards, Tableau dashboards, Matplot library, and interactive visualizations with Plotly.  

Topics covered:  

Python reusable functions and modules  

Python Plotting libraries (Matplotlib, Plotly; Exporting interactive plots to sharable formats)

Python APIs and API wrappers

Excel Functions (Vlookup, Pivot tables, Concatenate,…)

Building an Excel Dashboard and Publishing to HTML (build interactive filters and sliders into dashboards; appropriate use of graph types; online sharing of interactive data visualizations)   Building Tableau Dashboards  

Ways to Share and Integrate Tableau Dashboards (Connect to browser, use starters, .twb files)

Automating Tableau Data Integration for Live Dashboards  

Presentation Skills for Graphs and Charts  

This course broadens exposure to tasks seen in a financial analyst role. Coding tasks will be centered around options order book, depth chart, volume profile, cointegrated assets, and commodities data. Theory covered will consist of behavioral finance relating to technical analysis, applications of portfolio optimization and hedging techniques.  

Python Packages for Financial Forecasting (Pandas, Quandl, Pyfolio, Numpy, Scipy, Statmodels)

Cointegration vs. Correlation (Calculate a hedge ratio; Mean reversion)

Monte Carlo simulation, Grid search, and optimization (Use optimization of parameters to target a desired metric)

Technical analysis / Behavioral Finance (Behavioral Finance Theory behind TA; Fibonacci retracement; How to build momentum indicators, MACD, RSI, 9OC)

Sentiment Data / Combining Sentiment Data with TA (Jake Bernstein’s DSI; sentiment data as a contrarian indicator)  

Level II market data (Reading order book data; How to deal with large financial data; Hedging with options)

Identifying Smart/Dumb Money Indicators (Parsing large order book data to create indicators)

Building an Automated trading Algorithm in Python (Walkthrough of an automated trading bot form data collection to signal)

Deploying an Automated trading Algorithm with Python (Using Alpaca API, a timestamped Python trading bot that can be used to demonstrate skill in trading )

Introduction to cloud services software relevant for big data analytics and data scientists. Survey of Amazon Web Services. Study of automated solutions to data gathering, storage, and machine learning. Students acquire specific skill sets in application programming interfaces and web scraping with Python through hands-on problem solving. Use of blockchain and smart contracts to make business processes more efficient through technical and theoretical application.

Outlook of economy is of vital importance for many key decisions. Introduction to theory and application of cutting-edge tools used by economists and business leaders to inform their views of economy. These tools are applied to forecast or nowcast key economic indicators such as inflation, unemployment, and gross domestic product. Examination of how forecasts of fundamentals can be used to inform our views on asset prices.

Example course list. Subject to change. 

Spring Course Offerings: Applied Electives

Investigation of rise of earning inequality (with emphasis on U.S.), focusing on learning how to use models and data to quantify impact of range of forces on inequality. Overview of broad empirical trends, with emphasis on understanding how to document these facts ourselves. Consideration of three classes of potential explanations for these patterns: international connections (e.g., trade and immigration), institutional change (e.g., minimum wage and unionization), and technical change (e.g., computerization and spread of robots). Focus on quantifying these forces ourselves. Study of top income inequality: why have extremely rich become much richer than very rich? Focus on CEO compensation.

National Income Accounting and the Balance of Payment, Global Imbalances: Current account sustainability; sudden stops; international capital flows and development; home bias in international portfolios

International relative prices and exchange-rate pass-through: real exchange rates and economic activity; currency unions and economic adjustment, the dollar and international prices

International production and business cycles

International trade

Introduction to models and data used to understand connection between asset prices, health of financial sector, and macroeconomy, including review of recent papers to gain introduction to questions being addressed on research frontier.

Interest Rates and Term Structure ; Macro variables and Interest Rates

Solow Growth Model (balanced growth and transition dynamics; applying model to stock market benchmarks)

Growth accounting : determinants of economic growth

NIPA Accounting using Growth Model

Measuring and projecting potential output

Benchmarks for Stock Price

Efficient Portfolio Theory & CAPM

Equity Risk Premium

Aggregate Asset Pricing Anomalies

Introduction to basic concepts, uses, and challenges of big data, with emphasis on pragmatic hands-on applications using real-world data for current and future big data practitioners — consumers of big data insights for economic applications.

Big Data (volume, velocity, variety, voracity)

Bayesian Analysis

Machine Learning & Data Mining (assessing model accuracy, logistic regression, Bayesian Decision Theory, Naïve Bayes, regression, gradient descent, LDA, QDA, K-Means, kNN, resampling methods)

Regularization methods (LASSO, Ridge, Elastic Nets), PCA, Partial Least squares, Non-linear methods, association rules, collaborative filtering  

Decision trees ; Classification Trees; Bagging; Random Forests; SVM; Neural Networks

Economic Methods and applications

Study covers asset pricing and portfolio theory, critical areas for deeper understanding of financial markets and investments. Building from theory, incorporation of empirical analysis and real-world issues to bridge theory with practice through case studies.

Applied Quantitative Methods (Probability, Regression, Optimization, Simulation) Valuation and Efficient Market Theory (Valuation methodologies and the DCF framework; Efficient market theory)  

Empirical Asset Pricing (Capital asset pricing model; Empirical risk factors; “Smart-beta” strategies)

Performance in Competitive Markets (Index vs. active; Performance analysis; Bond-market risk factors and manager performance; Stock-market risk factors and manager performance)

Alternative Investments (Hedge funds, Private equity, Real Estate)

Portfolio Theory and Practice (Modern portfolio theory; CAPM and I-CAPM; Liability-relative and goals-based asset allocation; Factor-based asset allocation)

The Optimal Portfolio (Return and risk forecasts; Mean-variance portfolio optimization and the efficient frontier; I-CAPM portfolio optimization)

Portfolio Management (selection, construction, monitoring)

Investigation of several theoretical frameworks in international economics followed by applications to empirical questions. Neoclassical trade models, analysis of firms and heterogeneous producers, and economic geography topics. Case studies and empirical papers focus on understanding determinants of trade patterns and on measurement of aggregate and distributional effects of international trade. Discussion of recent research on effects of NAFTA and Brexit, effect of trade on inequality in developed and developing countries, and impact of infrastructure investments on trade and development.

Twenty first century is the century of big data, with large datasets now appearing in many scientific fields. These datasets cannot be analyzed using classical econometric techniques. Instead, to extract useful information from these datasets, we have to rely on modern machine learning techniques. Some of these machine learning techniques, including lasso, regression trees, random forests, principle component regression, and neural networks, will be discussed in the first part of the class. In the second part, we will cover cutting edge developments at the intersection of machine learning and econometrics. In particular, we will study double machine learning in detail and discuss how to apply it to enhance the analysis of classical econometric problems, such as program evaluation, demand estimation, and asset pricing. Throughout the course, theoretical concepts will be illustrated via applications.  

Asymptotic Theory : Convergence in probability and in distribution; Law of large numbers and central limit theorem; Continuous mapping theorem, Slutsky lemma, and delta method; Consistency and asymptotic normality

Intermediate-level econometrics : Linear regression; Logistic regression; Instrumental variable regression; Maximum likelihood estimation

High-dimensional linear regression : Regularized estimation; Lasso; Variable Selection; Cross-validation; Related methods

Nonparametric estimation and machine learning : Kernel methods; Nearest neighbor methods; Regression trees; Random forests; Bagging

Neural networks : Constructing neural networks; Training neural networks; Stochastic gradient decent; Back-propagation  

Double machine learning : Estimating equations; Neyman orthogonality; Double robustness; Cross-fitting

Program evaluation : Potential outcomes; Conditional unconfoundedness; Average treatment effects; Quantile treatment effects; Local average treatment effects; Diff-in-diff estimation  

Machine learning in finance : Asset pricing via machine learning; Text data and asset pricing

Demand estimation with big data : Hedonic model; BLP model

This course focuses on modern data management systems that are used in data analytics. It exposes the students to cutting-edge data management concepts and systems and provides the students the working knowledge needed to manage large-scale data. Cloud storage systems, NoSQL databases, and the map-reduce computing paradigm are among modern data management techniques that are covered in this course.  

Storage Systems

File Formats, Network File Systems

Semi-structured date: JSON, XML

Data modeling: Entity-relationship model; relational data

Constrains and views indexing

Query execution

Hadoop map-reduce; Big data management

Cloud storage; NoSQL; MongoDB

Data stewardship

Introduction to Business Intelligence software relevant for Big Data and Financial Services companies. This course will survey Amazon AWS, PowerBI, and Hadoop, then selectively teach deployment of automated solutions on these platforms. Development of presentation skills necessary for industry.  

AWS Intro (EC2 instances, Subnets; Databases, RDS, setting up PostgreSQL and other databases)

AWS lambda Functions (Deploying a Python/R lambda function; Cloud computing architecture and logic) AWS ML / AI services (Intelligent search, Demand Forecasting, Demand projection, Text analytics)

Power BI Reports (Import data; Creating maps; DAX and M)

Power BI dashboards (Creating interactive visualizations with filters)  

Microsoft Azure services overview (Hadoop; Parallel processing; when to use Azure; Potentials of Big Data Analytics software)

Apache Hive (Sending data to Apache databases; HiveQL commands)

Data-mining with Hadoop (Using map reduce for data mining efficiency)  

Advanced SQL (Difference in SQL commands across Database services; Subqueries, declarations, null handling)

Alternate programming languages (SPSS, Julia)

This course broadens exposure to tasks seen in a quantitative analyst role. Coding tasks will be centered around options order book, depth chart, volume profile, and commodities data to create and deploy algorithmic trading bots. Theory covered will consist of behavioral finance, automation of portfolio rebalancing, and hedging techniques.  

Momentum Indicators and Behavioral Finance (Constructing MACD, RSI, Divergence, VWAP; Elliott Wave theory; Dutch Tulip Mania Study)

Index Fund rebalancing (automatically adjusting allocations of Indexes based on price movements)

Contrarian Strategy / Combining Sentiment Data (Jake Bernstein’s DSI; How to use sentiment data as a contrarian indicator)

Working with live data (Warehousing and updating live financial data; High frequency data sources)

How to deploy an algorithmic trading bot (Timestamped Python trading bot;  Pine script)  

Bloomberg Terminal (Plotting multiple series and exporting data; Integration with Excel)

Smart/Dumb Money Indicators With Options (Parsing high frequency order book data to create indicators)  

Level II market data (Reading order book data; Dealing with large financial data)

Alternate Data Sources (Volume Profile; Futures deliveries; Central Bank assets)

Frequently Asked Questions

The MQE is a 48-unit degree program.   Students can complete the degree in as few as 9 months (or 3 quarters – Fall, Winter and Spring). However, students may choose to extend the time of the program up to 18 months (4, 5, or 6 quarters). Students are only admitted in the Fall quarter.

All students are required to take a foundational course in applied statistics and econometrics (Econ 430 and 441A) during their first term and enroll in Economists in Action (Econ 410) each term. Beyond this, students select from elective courses offered by the MQE to reach their 48-unit degree requirement.

Visit this page to review the current program requirements for the MQE degree.

Yes, students can elect to complete the program in 18-months, which allows students to complete 2 courses per quarter over the course of six quarters. Please note that some courses may take place during the workday so students will need to ensure they have flexibility with their employer to accommodate live classes. Students who choose to complete the program in 9 months (three quarters) must be full-time students.

Yes, the MQE program is STEM Certified (CIP Code 45.0603: Econometrics and Quantitative Economics). To learn more about STEM OPT, please visit USCIS.GOV or UCLA’s Dashew Center.

The MQE program is focused on training students in data analytics, econometrics, machine learning, applied statistics, quantitative methods, forecasting, data mining and finance through hands-on courses, applied business projects, research activities, group work, and assignments. Students will gain exposure to R, Python, SQL, Excel and numerous financial platforms and tools throughout the program.   Our unique hands-on curriculum and approach equips graduates with the applied concepts, technical tools, and analytical skills necessary to solve complex business problems facing government agencies, financial institutions, and global corporations.  

The capstone for the Master of Quantitative Economics degree is a required course which entails the completion of either a final project or a final exam. The capstone may consist of one of the following: 1) complete a final project under the supervision of an MQE advisor and submit the results in the form of a research paper; 2) with the permission of three instructors, take a final exam; 3) complete an instructor-supervised applied project and produce a summary paper; or 4) complete an MQE-approved internship and produce a summary paper.

The final (capstone) project is designed by the student in concert with a faculty advisor and will center on the student’s interest in a particular field of economics.   MQE students select their faculty advisor and capstone topic.   Many students choose to focus on a topic related to their professional goals.  

Here are a few of the past MQE students have explored as part of their Capstone Project.

• Bridging Venture Capital to the Public Market
• Utilizing High Dimensional Data to Analyze the Effect of Uber on Public Transportation Use
• Green vs. Grown: An Analysis of How Nigeria’s Electric Power Sector Reform Act Impacted Greenhouse Gas Emissions

MQE students may take select PhD courses within The Department of Economics. Details are provided to students during the registration period each term. Courses offered by other units at UCLA are not permitted.

Throughout the year, the MQE partners with companies to provide students with opportunities to apply their MQE coursework and training to solve business problems faced by corporations of all sizes.   Students work in small teams under the guidance of a faculty coach to analyze data and present solutions to the corporate partner. Applied projects range in length and focus based on the needs of the business partner. Past projects have served clients in technology, ecommerce, entertainment, cybersecurity, financial services, digital marketing, analytics, sustainability, healthcare, and non-profit fields.

The UCLA Department of Economics offers undergraduate majors in Economics and Business Economics, a PhD program, and the Master of Quantitative Economics program. PhD students earn a Master of Arts along the way, but students cannot be directly admitted into that Master of Arts program.

The MQE program is focused on providing students with extensive coursework focused on quantitative economics, data analytics and finance, based on the skills desired by industry. Most PhD programs are focused on advanced economics and research training.

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A legend in the baking: ASU alum finds success with capstone project

2022 fulton schools grad designs robotic solution to baking company's palletization challenge.

Tyler Jiemback, a manufacturing engineer at Lallemand Baking and an Arizona State University alumnus, poses in front of Lallemand Baking’s newly installed robot he designed as part of his ASU senior capstone project. Photo courtesy Tyler Jiemback/Lallemand Baking

It's not every day a college student has the opportunity to participate in a $1.5 million funded capstone project.

For Tyler Jiemback, an Arizona State University alumni who graduated from  The Polytechnic School , part of the  Ira A. Fulton Schools of Engineering , in May 2022 with a Bachelor of Science in engineering, this daydream of an opportunity became a reality during his senior year of college when he and a team of other students participated in a capstone project in partnership with Lallemand Baking .

The capstone project is a culminating experience for students that can vary from writing a thesis to performances and exhibits. With many of the ASU undergraduate degree recipients participating, these projects explore skills in writing, oral communication, project management and hands-on research.

Senior capstones are recognized as transformative learning experiences, serving as high-impact projects that enable students to develop innovative solutions to challenges that may occur in professional settings. The completion of senior capstones showcases students' proficiency in the knowledge acquired at ASU and the practical implementation of their chosen area of study.

At the beginning of the capstone process, industry partners showcase their project proposals, offering students a glimpse into a real-world problem in need of a solution.

One presentation stood out to Jiemback above all others.

“Lallemand was a bit unique in that they went in knowing that they were looking for someone to hire full time, which was, admittedly, one of the main reasons I joined the project,” Jiemback says. “At the beginning of your senior year of engineering, the biggest hurdle you face isn't the class workload, it's securing your first post-graduation job.”

As a supplier of yeast and baking ingredient solutions, Lallemand Baking emphasizes an evolutionary approach by delivering value through its innovative baking solutions.

“We have large facilities in production all day long, 365 days a year, with a strong workforce embedded inside of them,” says Audrey St. Onge, president and general manager of Lallemand Baking, North America. “We don't like to call ourselves manufacturers; we’re more producers of a special ingredient.”

Lallemand Baking’s primary concern revolved around the manual palletizing process for both 50-pound bags and 40-pound boxes of dry yeast on the production line. The manual handling of these products posed risks of injury, operational inconsistencies and often led to high turnover. 

“They wanted us to come in and evaluate their production line, see how we could optimize the entire product flow and then implement a new design for a new system,” Jiemback says.

Jiemback realized that the introduction of an automated palletizing system had the potential to comprehensively address these issues.

“One of my favorite parts of their proposal was how open-ended they left it," he says. "They left us a lot of room for autonomy, creativity and design, which really drew me to the project.”

During a capstone project, each student ranks their preferred projects and the program coordinators make matches based on skill set alignment. Together, Jiemback and his team worked through the project's design phase.

“Pretty much all of the work was done as a group,” he says. “It's really hard to do independent work since the capstone is such an interdisciplinary process.”

Throughout the project, Jiemback collaborated with Lallemand Baking to redesign the yeast production lines in the company’s factory. This included designing the layout, integrating a robotic palletizer and implementing real-time data systems.

“They put no restrictions on us," Jiemback says. "We were able to draw from a lot of the new technology we were exposed to during our degree at ASU and discover new ways to apply that technology without limitations, which was so fun.”

The primary objective was to enhance production output while minimizing the physical strain on factory workers.

“In simplest terms, we developed a new production line layout that improved and streamlined this process, allowing for an efficient product flow, consistent output levels and a lower labor cost,” Jiemback says.

This solution, however, was not without its challenges.

“Designing a system such as this can be easy, but making it work with all of the restrictions is difficult,” says Chuck Dulisse, vice president of engineering and asset management at Lallemand Baking. “With such limited space and the sheer speed of the equipment, finding a way to make one piece of equipment palletize three different assembly lines was not an easy feat.”

Jiemback adds, “I think one of the biggest challenges was maintaining consistent communication between Lallemand and the capstone team. We were very fortunate in that we got to collaborate with the upper management, but it was often difficult to carve out sufficient time to discuss our design.”

The one piece of advice Jiemback would give to students beginning their capstone project is to ask as many questions as possible. The more inquisitive you are, the better your project will turn out, he says.

After the completion of his capstone, Jiemback was offered a full-time position at Lallemand Baking as a manufacturing engineer.

“What I liked about Tyler is he wasn't afraid to ask questions,” St. Onge says. “He has leadership skills built within him. He truly shined out of the pack, and I knew he was a member of our team.”

In January 2024, a year and a half after the completion of the capstone project, Lallemand Baking installed Jiemback's finished robot on the factory floor. Seeing the process unfold from start to finish was an incredibly rewarding experience for Jiemback.

“I was so proud of Tyler when I saw him standing beside the finished robot,” St. Onge says.

Today, Jiemback enjoys the diverse nature of his work at Lallemand Baking and he values having the freedom to explore and grow without fear of scrutiny.

“The value that capstones bring to companies is incredible,” St. Onge says. “I will continue to develop these kinds of partnerships and take opportunities to work hand-in-hand with students to help fuel new energy. It's a win-win partnership.” 

With appreciation for where his capstone project has landed him, Jiemback looks forward to his bright future.

“As my career continues to unfold,” he says, “I hope to use my unique perspective to bring new technology and ideas into the manufacturing space.”

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Battery Ventures

Venture capital summer 2024 intern.

• Share This: Share Venture Capital Summer 2024 Intern on Facebook Share Venture Capital Summer 2024 Intern on LinkedIn Share Venture Capital Summer 2024 Intern on X

Description of internship :

Summer interns will take on the day-to-day responsibilities of an investment analyst, helping source and diligence potential investment opportunities. They will spend time collaborating with and supporting partner Marcus Ryu and his team evaluating early and growth stage vertical and application layer software companies. The program will end with a summer-long capstone project in a particular sector, presenting to the broader investment team.

The candidate must be based in San Francisco for the 2024 summer, coming to Battery’s 260 Townsend St, SF, office four days/week. High performing candidates will be considered for transition to a full-time analyst position.

Qualifications :

• Excellent academic record at a leading college/university; business or finance-related background preferred
• GPA 3.5+; detail orientation in both written and quantitative work
• Strong interest in technology-related investing and a long-term investing role with room for growth
• Extroverted personality and ability to cold-call and build relationships with entrepreneurs
• References attesting to candidate as a self-starter, team player, and hard worker with self-awareness and willingness to learn

Program on the Environment

• College of the Environment
• University of Washington

Trans Day of Visibility CoEnv Panel & Social

Trans Day of Visibility (TDOV) is March 31st every year. The College of the Environment is excited to announce an event tailored specifically to our College community on Tuesday, April 2nd, at 5pm in the Forest Club Room in Anderson Hall. In order to foster understanding, dialogue, and solidarity, we are organizing a panel discussion featuring transgender and non-binary speakers from across the CoEnv. We believe that sharing experiences, insights, and expertise will not only enhance our understanding of transgender issues but also strengthen our commitment to inclusivity and allyship. Following the panel discussion, we will have a social period for attendees to connect, reflect, and further engage with one another. We anticipate the panel and social to be relevant for both trans folks and allies. This event is open to all!

If you are unable to attend in person, you can watch the panel live via Zoom:  https://washington.zoom.us/s/98811459310

If you have questions, you can email Michael Martinez (they/them) at [email protected]  or Miro Stuke (they/them) at  [email protected] .

A note about accessibility: The Forest Club Room is on the second floor of Anderson Hall, which does not have an elevator. We are deeply sorry if this is a barrier to your attendance, and we will plan future events far enough in advance to book accessible rooms on campus.

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AUSA 2024- Warriors Corner - Day 3- Command and Control (C2) and Network Transformation at Project Convergence Capstone 4

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AUSA 2024- Warriors Corner - Day 3- Command and Control (C2) and Network Transformation at Project Convergence Capstone 4 Speaker MG Jeth B. Rey Director, Network Cross Functional Team United States Army Futures Command Speaker Mark C. Kitz Program Executive Officer, Command, Control and Communications-Tactical United States Army Speaker COL Heather Fisk Director, Network Integration Division, G-6 United States Army Joint Modernization Command Speaker COL Michael R. Kaloostian Director, Networks, Transport and Security Next Generation Command and Control (C2) Warfighting Capabilities United States Army Futures Command

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