data science thesis examples

Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

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

PS – This is just the start…

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

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

Data Science-Related Research Topics

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

Data Science Research Ideas (Continued)

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

Recent Data Science-Related Studies

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

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

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

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

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

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37 Research Topics In Data Science To Stay On Top Of

• February 22, 2024

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

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

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

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

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

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

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

37 Research Topics in Data Science

1.) predictive modeling.

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

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

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

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

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

2.) Big Data Analytics

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

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

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

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

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

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

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

That’s where data science comes in.

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

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

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

3.) Auto Machine Learning

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

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

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

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

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

4.) Text Mining

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

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

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

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

5.) Natural Language Processing

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

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

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

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

6.) Recommender Systems

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

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

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

Recommender systems are also used to recommend content to users.

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

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

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

7.) Deep Learning

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

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

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

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

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

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

8.) Reinforcement Learning

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

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

9.) Data Visualization

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

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

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

Data visualization is also used to communicate results to others.

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

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

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

10.) Predictive Maintenance

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

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

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

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

11.) Financial Analysis

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

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

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

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

Financial analysis is also used to predict future economic trends.

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

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

12.) Image Recognition

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

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

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

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

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

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

13.) Fraud Detection

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

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

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

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

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

14.) Web Scraping

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

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

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

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

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

15.) Social Media Analysis

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

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

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

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

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

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

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

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

16.) GPU Computing

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

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

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

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

17.) Quantum Computing

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

It also opens the door to new types of data.

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

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

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

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

You could be too.

18.) Genomics

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

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

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

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

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

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

19.) Location-based services

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

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

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

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

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

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

20.) Smart City Applications

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

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

But what exactly are smart city applications?

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

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

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

It is then analyzed to identify tendencies and habits.

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

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

21.) Internet Of Things (IoT)

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

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

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

That means that they can share data with computers.

And that’s where data science comes in.

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

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

Really, the possibilities are endless.

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

22.) Cybersecurity

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

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

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

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

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

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

23.) Blockchain

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

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

Did someone say transmitting data?

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

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

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

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

24.) Sustainability

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

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

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

Sustainability is an important issue that is relevant to everyone.

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

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

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

25.) Educational Data

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

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

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

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

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

26.) Politics

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

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

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

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

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

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

27.) Cloud Technologies

Cloud technologies are a great research topic.

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

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

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

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

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

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

28.) Robotics

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

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

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

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

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

29.) HealthCare

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

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

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

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

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

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

30.) Remote Work

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

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

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

For example, how does remote work impact employee productivity?

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

And what are the cybersecurity risks associated with working remotely?

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

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

31.) Data-Driven Journalism

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

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

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

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

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

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

32.) Data Engineering

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

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

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

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

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

33.) Data Curation

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

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

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

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

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

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

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

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

34.) Meta-Learning

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

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

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

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

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

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

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

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

35.) Data Warehousing

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

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

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

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

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

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

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

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

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

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

36.) Business Intelligence

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

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

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

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

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

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

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

37.) Crowdsourcing

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

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

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

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

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

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

That would have a huge effect.

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

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

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

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

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

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Uncertainty Quantification under Distribution Shifts Aleksandr Podkopaev, 2023

Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023

Comparing Forecasters and Abstaining Classifiers Yo Joong Choe, 2023

Using Task Driven Methods to Uncover Representations of Human Vision and Semantics Aria Yuan Wang, 2023

Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023

Applied Mathematics of the Future Kin G. Olivares, 2023

METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023

NEURAL REASONING FOR QUESTION ANSWERING Haitian Sun, 2023

Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Darby M. Losey, 2023

Calibrated Conditional Density Models and Predictive Inference via Local Diagnostics David Zhao, 2023

Towards an Application-based Pipeline for Explainability Gregory Plumb, 2022

Objective Criteria for Explainable Machine Learning Chih-Kuan Yeh, 2022

Making Scientific Peer Review Scientific Ivan Stelmakh, 2022

Facets of regularization in high-dimensional learning: Cross-validation, risk monotonization, and model complexity Pratik Patil, 2022

Active Robot Perception using Programmable Light Curtains Siddharth Ancha, 2022

Strategies for Black-Box and Multi-Objective Optimization Biswajit Paria, 2022

Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin, 2022

Sensor Fusion Frameworks for Nowcasting Maria Jahja, 2022

Equilibrium Approaches to Modern Deep Learning Shaojie Bai, 2022

Towards General Natural Language Understanding with Probabilistic Worldbuilding Abulhair Saparov, 2022

Applications of Point Process Modeling to Spiking Neurons (Unavailable) Yu Chen, 2021

Neural variability: structure, sources, control, and data augmentation Akash Umakantha, 2021

Structure and time course of neural population activity during learning Jay Hennig, 2021

Cross-view Learning with Limited Supervision Yao-Hung Hubert Tsai, 2021

Meta Reinforcement Learning through Memory Emilio Parisotto, 2021

Learning Embodied Agents with Scalably-Supervised Reinforcement Learning Lisa Lee, 2021

Learning to Predict and Make Decisions under Distribution Shift Yifan Wu, 2021

Statistical Game Theory Arun Sai Suggala, 2021

Towards Knowledge-capable AI: Agents that See, Speak, Act and Know Kenneth Marino, 2021

Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods Po-Wei Wang, 2021

Bridging Language in Machines with Language in the Brain Mariya Toneva, 2021

Curriculum Learning Otilia Stretcu, 2021

Principles of Learning in Multitask Settings: A Probabilistic Perspective Maruan Al-Shedivat, 2021

Towards Robust and Resilient Machine Learning Adarsh Prasad, 2021

Towards Training AI Agents with All Types of Experiences: A Unified ML Formalism Zhiting Hu, 2021

Building Intelligent Autonomous Navigation Agents Devendra Chaplot, 2021

Learning to See by Moving: Self-supervising 3D Scene Representations for Perception, Control, and Visual Reasoning Hsiao-Yu Fish Tung, 2021

Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe Collin Politsch, 2020

Causal Inference with Complex Data Structures and Non-Standard Effects Kwhangho Kim, 2020

Networks, Point Processes, and Networks of Point Processes Neil Spencer, 2020

Dissecting neural variability using population recordings, network models, and neurofeedback (Unavailable) Ryan Williamson, 2020

Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector Dylan Fitzpatrick, 2020

Towards a Unified Framework for Learning and Reasoning Han Zhao, 2020

Learning DAGs with Continuous Optimization Xun Zheng, 2020

Machine Learning and Multiagent Preferences Ritesh Noothigattu, 2020

Learning and Decision Making from Diverse Forms of Information Yichong Xu, 2020

Towards Data-Efficient Machine Learning Qizhe Xie, 2020

Change modeling for understanding our world and the counterfactual one(s) William Herlands, 2020

Machine Learning in High-Stakes Settings: Risks and Opportunities Maria De-Arteaga, 2020

Data Decomposition for Constrained Visual Learning Calvin Murdock, 2020

Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data Micol Marchetti-Bowick, 2020

Towards Efficient Automated Machine Learning Liam Li, 2020

LEARNING COLLECTIONS OF FUNCTIONS Emmanouil Antonios Platanios, 2020

Provable, structured, and efficient methods for robustness of deep networks to adversarial examples Eric Wong , 2020

Reconstructing and Mining Signals: Algorithms and Applications Hyun Ah Song, 2020

Probabilistic Single Cell Lineage Tracing Chieh Lin, 2020

Graphical network modeling of phase coupling in brain activity (unavailable) Josue Orellana, 2019

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees Christoph Dann, 2019 Learning Generative Models using Transformations Chun-Liang Li, 2019

Estimating Probability Distributions and their Properties Shashank Singh, 2019

Post-Inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Willie Neiswanger, 2019

Accelerating Text-as-Data Research in Computational Social Science Dallas Card, 2019

Multi-view Relationships for Analytics and Inference Eric Lei, 2019

Information flow in networks based on nonstationary multivariate neural recordings Natalie Klein, 2019

Competitive Analysis for Machine Learning & Data Science Michael Spece, 2019

The When, Where and Why of Human Memory Retrieval Qiong Zhang, 2019

Towards Effective and Efficient Learning at Scale Adams Wei Yu, 2019

Towards Literate Artificial Intelligence Mrinmaya Sachan, 2019

Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter, 2019

Unified Models for Dynamical Systems Carlton Downey, 2019

Anytime Prediction and Learning for the Balance between Computation and Accuracy Hanzhang Hu, 2019

Statistical and Computational Properties of Some "User-Friendly" Methods for High-Dimensional Estimation Alnur Ali, 2019

Nonparametric Methods with Total Variation Type Regularization Veeranjaneyulu Sadhanala, 2019

New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications Hongyang Zhang, 2019

Gradient Descent for Non-convex Problems in Modern Machine Learning Simon Shaolei Du, 2019

Selective Data Acquisition in Learning and Decision Making Problems Yining Wang, 2019

Anomaly Detection in Graphs and Time Series: Algorithms and Applications Bryan Hooi, 2019

Neural dynamics and interactions in the human ventral visual pathway Yuanning Li, 2018

Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation Kirthevasan Kandasamy, 2018

Teaching Machines to Classify from Natural Language Interactions Shashank Srivastava, 2018

Statistical Inference for Geometric Data Jisu Kim, 2018

Representation Learning @ Scale Manzil Zaheer, 2018

Diversity-promoting and Large-scale Machine Learning for Healthcare Pengtao Xie, 2018

Distribution and Histogram (DIsH) Learning Junier Oliva, 2018

Stress Detection for Keystroke Dynamics Shing-Hon Lau, 2018

Sublinear-Time Learning and Inference for High-Dimensional Models Enxu Yan, 2018

Neural population activity in the visual cortex: Statistical methods and application Benjamin Cowley, 2018

Efficient Methods for Prediction and Control in Partially Observable Environments Ahmed Hefny, 2018

Learning with Staleness Wei Dai, 2018

Statistical Approach for Functionally Validating Transcription Factor Bindings Using Population SNP and Gene Expression Data Jing Xiang, 2017

New Paradigms and Optimality Guarantees in Statistical Learning and Estimation Yu-Xiang Wang, 2017

Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden Kirstin Early, 2017

New Optimization Methods for Modern Machine Learning Sashank J. Reddi, 2017

Active Search with Complex Actions and Rewards Yifei Ma, 2017

Why Machine Learning Works George D. Montañez , 2017

Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision Ying Yang , 2017

Computational Tools for Identification and Analysis of Neuronal Population Activity Pengcheng Zhou, 2016

Expressive Collaborative Music Performance via Machine Learning Gus (Guangyu) Xia, 2016

Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch, 2016

Exploring Weakly Labeled Data Across the Noise-Bias Spectrum Robert W. H. Fisher, 2016

Optimizing Optimization: Scalable Convex Programming with Proximal Operators Matt Wytock, 2016

Combining Neural Population Recordings: Theory and Application William Bishop, 2015

Discovering Compact and Informative Structures through Data Partitioning Madalina Fiterau-Brostean, 2015

Machine Learning in Space and Time Seth R. Flaxman, 2015

The Time and Location of Natural Reading Processes in the Brain Leila Wehbe, 2015

Shape-Constrained Estimation in High Dimensions Min Xu, 2015

Spectral Probabilistic Modeling and Applications to Natural Language Processing Ankur Parikh, 2015 Computational and Statistical Advances in Testing and Learning Aaditya Kumar Ramdas, 2015

Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain Alona Fyshe, 2015

Learning Statistical Features of Scene Images Wooyoung Lee, 2014

Towards Scalable Analysis of Images and Videos Bin Zhao, 2014

Statistical Text Analysis for Social Science Brendan T. O'Connor, 2014

Modeling Large Social Networks in Context Qirong Ho, 2014

Semi-Cooperative Learning in Smart Grid Agents Prashant P. Reddy, 2013

On Learning from Collective Data Liang Xiong, 2013

Exploiting Non-sequence Data in Dynamic Model Learning Tzu-Kuo Huang, 2013

Mathematical Theories of Interaction with Oracles Liu Yang, 2013

Short-Sighted Probabilistic Planning Felipe W. Trevizan, 2013

Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms Lucia Castellanos, 2013

Approximation Algorithms and New Models for Clustering and Learning Pranjal Awasthi, 2013

Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems Mladen Kolar, 2013

Learning with Sparsity: Structures, Optimization and Applications Xi Chen, 2013

GraphLab: A Distributed Abstraction for Large Scale Machine Learning Yucheng Low, 2013

Graph Structured Normal Means Inference James Sharpnack, 2013 (Joint Statistics & ML PhD)

Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le, 2013

Learning Large-Scale Conditional Random Fields Joseph K. Bradley, 2013

New Statistical Applications for Differential Privacy Rob Hall, 2013 (Joint Statistics & ML PhD)

Parallel and Distributed Systems for Probabilistic Reasoning Joseph Gonzalez, 2012

Spectral Approaches to Learning Predictive Representations Byron Boots, 2012

Attribute Learning using Joint Human and Machine Computation Edith L. M. Law, 2012

Statistical Methods for Studying Genetic Variation in Populations Suyash Shringarpure, 2012

Data Mining Meets HCI: Making Sense of Large Graphs Duen Horng (Polo) Chau, 2012

Learning with Limited Supervision by Input and Output Coding Yi Zhang, 2012

Target Sequence Clustering Benjamin Shih, 2011

Nonparametric Learning in High Dimensions Han Liu, 2010 (Joint Statistics & ML PhD)

Structural Analysis of Large Networks: Observations and Applications Mary McGlohon, 2010

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy Brian D. Ziebart, 2010

Tractable Algorithms for Proximity Search on Large Graphs Purnamrita Sarkar, 2010

Rare Category Analysis Jingrui He, 2010

Coupled Semi-Supervised Learning Andrew Carlson, 2010

Fast Algorithms for Querying and Mining Large Graphs Hanghang Tong, 2009

Efficient Matrix Models for Relational Learning Ajit Paul Singh, 2009

Exploiting Domain and Task Regularities for Robust Named Entity Recognition Andrew O. Arnold, 2009

Theoretical Foundations of Active Learning Steve Hanneke, 2009

Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning Hao Cen, 2009

Detecting Patterns of Anomalies Kaustav Das, 2009

Dynamics of Large Networks Jurij Leskovec, 2008

Computational Methods for Analyzing and Modeling Gene Regulation Dynamics Jason Ernst, 2008

Stacked Graphical Learning Zhenzhen Kou, 2007

Actively Learning Specific Function Properties with Applications to Statistical Inference Brent Bryan, 2007

Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields Pradeep Ravikumar, 2007

Scalable Graphical Models for Social Networks Anna Goldenberg, 2007

Measure Concentration of Strongly Mixing Processes with Applications Leonid Kontorovich, 2007

Tools for Graph Mining Deepayan Chakrabarti, 2005

Automatic Discovery of Latent Variable Models Ricardo Silva, 2005

LIBRARIES | ARCH

Data science masters theses.

The Master of Science in Data Science program requires the successful completion of 12 courses to obtain a degree. These requirements cover six core courses, a leadership or project management course, two required courses corresponding to a declared specialization, two electives, and a capstone project or thesis. This collection contains a selection of masters theses or capstone projects by MSDS graduates.

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17 Compelling Machine Learning Ph.D. Dissertations

Machine Learning Modeling Research posted by Daniel Gutierrez, ODSC August 12, 2021 Daniel Gutierrez, ODSC

Working in the field of data science, I’m always seeking ways to keep current in the field and there are a number of important resources available for this purpose: new book titles, blog articles, conference sessions, Meetups, webinars/podcasts, not to mention the gems floating around in social media. But to dig even deeper, I routinely look at what’s coming out of the world’s research labs. And one great way to keep a pulse for what the research community is working on is to monitor the flow of new machine learning Ph.D. dissertations. Admittedly, many such theses are laser-focused and narrow, but from previous experience reading these documents, you can learn an awful lot about new ways to solve difficult problems over a vast range of problem domains. 

In this article, I present a number of hand-picked machine learning dissertations that I found compelling in terms of my own areas of interest and aligned with problems that I’m working on. I hope you’ll find a number of them that match your own interests. Each dissertation may be challenging to consume but the process will result in hours of satisfying summer reading. Enjoy!

Please check out my previous data science dissertation round-up article . 

1. Fitting Convex Sets to Data: Algorithms and Applications

This machine learning dissertation concerns the geometric problem of finding a convex set that best fits a given data set. The overarching question serves as an abstraction for data-analytical tasks arising in a range of scientific and engineering applications with a focus on two specific instances: (i) a key challenge that arises in solving inverse problems is ill-posedness due to a lack of measurements. A prominent family of methods for addressing such issues is based on augmenting optimization-based approaches with a convex penalty function so as to induce a desired structure in the solution. These functions are typically chosen using prior knowledge about the data. The thesis also studies the problem of learning convex penalty functions directly from data for settings in which we lack the domain expertise to choose a penalty function. The solution relies on suitably transforming the problem of learning a penalty function into a fitting task; and (ii) the problem of fitting tractably-described convex sets given the optimal value of linear functionals evaluated in different directions.

2. Structured Tensors and the Geometry of Data

This machine learning dissertation analyzes data to build a quantitative understanding of the world. Linear algebra is the foundation of algorithms, dating back one hundred years, for extracting structure from data. Modern technologies provide an abundance of multi-dimensional data, in which multiple variables or factors can be compared simultaneously. To organize and analyze such data sets we can use a tensor , the higher-order analogue of a matrix. However, many theoretical and practical challenges arise in extending linear algebra to the setting of tensors. The first part of the thesis studies and develops the algebraic theory of tensors. The second part of the thesis presents three algorithms for tensor data. The algorithms use algebraic and geometric structure to give guarantees of optimality.

3. Statistical approaches for spatial prediction and anomaly detection

This machine learning dissertation is primarily a description of three projects. It starts with a method for spatial prediction and parameter estimation for irregularly spaced, and non-Gaussian data. It is shown that by judiciously replacing the likelihood with an empirical likelihood in the Bayesian hierarchical model, approximate posterior distributions for the mean and covariance parameters can be obtained. Due to the complex nature of the hierarchical model, standard Markov chain Monte Carlo methods cannot be applied to sample from the posterior distributions. To overcome this issue, a generalized sequential Monte Carlo algorithm is used. Finally, this method is applied to iron concentrations in California. The second project focuses on anomaly detection for functional data; specifically for functional data where the observed functions may lie over different domains. By approximating each function as a low-rank sum of spline basis functions the coefficients will be compared for each basis across each function. The idea being, if two functions are similar then their respective coefficients should not be significantly different. This project concludes with an application of the proposed method to detect anomalous behavior of users of a supercomputer at NREL. The final project is an extension of the second project to two-dimensional data. This project aims to detect location and temporal anomalies from ground motion data from a fiber-optic cable using distributed acoustic sensing (DAS). 

4. Sampling for Streaming Data

Advances in data acquisition technology pose challenges in analyzing large volumes of streaming data. Sampling is a natural yet powerful tool for analyzing such data sets due to their competent estimation accuracy and low computational cost. Unfortunately, sampling methods and their statistical properties for streaming data, especially streaming time series data, are not well studied in the literature. Meanwhile, estimating the dependence structure of multidimensional streaming time-series data in real-time is challenging. With large volumes of streaming data, the problem becomes more difficult when the multidimensional data are collected asynchronously across distributed nodes, which motivates us to sample representative data points from streams. This machine learning dissertation proposes a series of leverage score-based sampling methods for streaming time series data. The simulation studies and real data analysis are conducted to validate the proposed methods. The theoretical analysis of the asymptotic behaviors of the least-squares estimator is developed based on the subsamples.

5.  Statistical Machine Learning Methods for Complex, Heterogeneous Data

This machine learning dissertation develops statistical machine learning methodology for three distinct tasks. Each method blends classical statistical approaches with machine learning methods to provide principled solutions to problems with complex, heterogeneous data sets. The first framework proposes two methods for high-dimensional shape-constrained regression and classification. These methods reshape pre-trained prediction rules to satisfy shape constraints like monotonicity and convexity. The second method provides a nonparametric approach to the econometric analysis of discrete choice. This method provides a scalable algorithm for estimating utility functions with random forests, and combines this with random effects to properly model preference heterogeneity. The final method draws inspiration from early work in statistical machine translation to construct embeddings for variable-length objects like mathematical equations

6. Topics in Multivariate Statistics with Dependent Data

This machine learning dissertation comprises four chapters. The first is an introduction to the topics of the dissertation and the remaining chapters contain the main results. Chapter 2 gives new results for consistency of maximum likelihood estimators with a focus on multivariate mixed models. The presented theory builds on the idea of using subsets of the full data to establish consistency of estimators based on the full data. The theory is applied to two multivariate mixed models for which it was unknown whether maximum likelihood estimators are consistent. In Chapter 3 an algorithm is proposed for maximum likelihood estimation of a covariance matrix when the corresponding correlation matrix can be written as the Kronecker product of two lower-dimensional correlation matrices. The proposed method is fully likelihood-based. Some desirable properties of separable correlation in comparison to separable covariance are also discussed. Chapter 4 is concerned with Bayesian vector auto-regressions (VARs). A collapsed Gibbs sampler is proposed for Bayesian VARs with predictors and the convergence properties of the algorithm are studied. 

7.  Model Selection and Estimation for High-dimensional Data Analysis

In the era of big data, uncovering useful information and hidden patterns in the data is prevalent in different fields. However, it is challenging to effectively select input variables in data and estimate their effects. The goal of this machine learning dissertation is to develop reproducible statistical approaches that provide mechanistic explanations of the phenomenon observed in big data analysis. The research contains two parts: variable selection and model estimation. The first part investigates how to measure and interpret the usefulness of an input variable using an approach called “variable importance learning” and builds tools (methodology and software) that can be widely applied. Two variable importance measures are proposed, a parametric measure SOIL and a non-parametric measure CVIL, using the idea of a model combining and cross-validation respectively. The SOIL method is theoretically shown to have the inclusion/exclusion property: When the model weights are properly around the true model, the SOIL importance can well separate the variables in the true model from the rest. The CVIL method possesses desirable theoretical properties and enhances the interpretability of many mysterious but effective machine learning methods. The second part focuses on how to estimate the effect of a useful input variable in the case where the interaction of two input variables exists. Investigated is the minimax rate of convergence for regression estimation in high-dimensional sparse linear models with two-way interactions, and construct an adaptive estimator that achieves the minimax rate of convergence regardless of the true heredity condition and the sparsity indices.

8.  High-Dimensional Structured Regression Using Convex Optimization

While the term “Big Data” can have multiple meanings, this dissertation considers the type of data in which the number of features can be much greater than the number of observations (also known as high-dimensional data). High-dimensional data is abundant in contemporary scientific research due to the rapid advances in new data-measurement technologies and computing power. Recent advances in statistics have witnessed great development in the field of high-dimensional data analysis. This machine learning dissertation proposes three methods that study three different components of a general framework of the high-dimensional structured regression problem. A general theme of the proposed methods is that they cast a certain structured regression as a convex optimization problem. In so doing, the theoretical properties of each method can be well studied, and efficient computation is facilitated. Each method is accompanied by a thorough theoretical analysis of its performance, and also by an R package containing its practical implementation. It is shown that the proposed methods perform favorably (both theoretically and practically) compared with pre-existing methods.

9. Asymptotics and Interpretability of Decision Trees and Decision Tree Ensembles

Decision trees and decision tree ensembles are widely used nonparametric statistical models. A decision tree is a binary tree that recursively segments the covariate space along the coordinate directions to create hyper rectangles as basic prediction units for fitting constant values within each of them. A decision tree ensemble combines multiple decision trees, either in parallel or in sequence, in order to increase model flexibility and accuracy, as well as to reduce prediction variance. Despite the fact that tree models have been extensively used in practice, results on their asymptotic behaviors are scarce. This machine learning dissertation presents analyses on tree asymptotics in the perspectives of tree terminal nodes, tree ensembles, and models incorporating tree ensembles respectively. The study introduces a few new tree-related learning frameworks which provides provable statistical guarantees and interpretations. A study on the Gini index used in the greedy tree building algorithm reveals its limiting distribution, leading to the development of a test of better splitting that helps to measure the uncertain optimality of a decision tree split. This test is combined with the concept of decision tree distillation, which implements a decision tree to mimic the behavior of a block box model, to generate stable interpretations by guaranteeing a unique distillation tree structure as long as there are sufficiently many random sample points. Also applied is mild modification and regularization to the standard tree boosting to create a new boosting framework named Boulevard. Also included is an integration of two new mechanisms: honest trees , which isolate the tree terminal values from the tree structure, and adaptive shrinkage , which scales the boosting history to create an equally weighted ensemble. This theoretical development provides the prerequisite for the practice of statistical inference with boosted trees. Lastly, the thesis investigates the feasibility of incorporating existing semi-parametric models with tree boosting. 

10. Bayesian Models for Imputing Missing Data and Editing Erroneous Responses in Surveys

This dissertation develops Bayesian methods for handling unit nonresponse, item nonresponse, and erroneous responses in large-scale surveys and censuses containing categorical data. The focus is on applications of nested household data where individuals are nested within households and certain combinations of the variables are not allowed, such as the U.S. Decennial Census, as well as surveys subject to both unit and item nonresponse, such as the Current Population Survey.

11. Localized Variable Selection with Random Forest  

Due to recent advances in computer technology, the cost of collecting and storing data has dropped drastically. This makes it feasible to collect large amounts of information for each data point. This increasing trend in feature dimensionality justifies the need for research on variable selection. Random forest (RF) has demonstrated the ability to select important variables and model complex data. However, simulations confirm that it fails in detecting less influential features in presence of variables with large impacts in some cases. This dissertation proposes two algorithms for localized variable selection: clustering-based feature selection (CBFS) and locally adjusted feature importance (LAFI). Both methods aim to find regions where the effects of weaker features can be isolated and measured. CBFS combines RF variable selection with a two-stage clustering method to detect variables where their effect can be detected only in certain regions. LAFI, on the other hand, uses a binary tree approach to split data into bins based on response variable rankings, and implements RF to find important variables in each bin. Larger LAFI is assigned to variables that get selected in more bins. Simulations and real data sets are used to evaluate these variable selection methods. 

12. Functional Principal Component Analysis and Sparse Functional Regression

The focus of this dissertation is on functional data which are sparsely and irregularly observed. Such data require special consideration, as classical functional data methods and theory were developed for densely observed data. As is the case in much of functional data analysis, the functional principal components (FPCs) play a key role in current sparse functional data methods via the Karhunen-Loéve expansion. Thus, after a review of relevant background material, this dissertation is divided roughly into two parts, the first focusing specifically on theoretical properties of FPCs, and the second on regression for sparsely observed functional data.

13. Essays In Causal Inference: Addressing Bias In Observational And Randomized Studies Through Analysis And Design

In observational studies, identifying assumptions may fail, often quietly and without notice, leading to biased causal estimates. Although less of a concern in randomized trials where treatment is assigned at random, bias may still enter the equation through other means. This dissertation has three parts, each developing new methods to address a particular pattern or source of bias in the setting being studied. The first part extends the conventional sensitivity analysis methods for observational studies to better address patterns of heterogeneous confounding in matched-pair designs. The second part develops a modified difference-in-difference design for comparative interrupted time-series studies. The method permits partial identification of causal effects when the parallel trends assumption is violated by an interaction between group and history. The method is applied to a study of the repeal of Missouri’s permit-to-purchase handgun law and its effect on firearm homicide rates. The final part presents a study design to identify vaccine efficacy in randomized control trials when there is no gold standard case definition. The approach augments a two-arm randomized trial with natural variation of a genetic trait to produce a factorial experiment. 

14. Bayesian Shrinkage: Computation, Methods, and Theory

Sparsity is a standard structural assumption that is made while modeling high-dimensional statistical parameters. This assumption essentially entails a lower-dimensional embedding of the high-dimensional parameter thus enabling sound statistical inference. Apart from this obvious statistical motivation, in many modern applications of statistics such as Genomics, Neuroscience, etc. parameters of interest are indeed of this nature. For over almost two decades, spike and slab type priors have been the Bayesian gold standard for modeling of sparsity. However, due to their computational bottlenecks, shrinkage priors have emerged as a powerful alternative. This family of priors can almost exclusively be represented as a scale mixture of Gaussian distribution and posterior Markov chain Monte Carlo (MCMC) updates of related parameters are then relatively easy to design. Although shrinkage priors were tipped as having computational scalability in high-dimensions, when the number of parameters is in thousands or more, they do come with their own computational challenges. Standard MCMC algorithms implementing shrinkage priors generally scale cubic in the dimension of the parameter making real-life application of these priors severely limited. 

The first chapter of this dissertation addresses this computational issue and proposes an alternative exact posterior sampling algorithm complexity of which that linearly in the ambient dimension. The algorithm developed in the first chapter is specifically designed for regression problems. The second chapter develops a Bayesian method based on shrinkage priors for high-dimensional multiple response regression. Chapter three chooses a specific member of the shrinkage family known as the horseshoe prior and studies its convergence rates in several high-dimensional models. 

15.  Topics in Measurement Error Analysis and High-Dimensional Binary Classification

This dissertation proposes novel methods to tackle two problems: the misspecified model with measurement error and high-dimensional binary classification, both have a crucial impact on applications in public health. The first problem exists in the epidemiology practice. Epidemiologists often categorize a continuous risk predictor since categorization is thought to be more robust and interpretable, even when the true risk model is not a categorical one. Thus, their goal is to fit the categorical model and interpret the categorical parameters. The second project considers the problem of high-dimensional classification between the two groups with unequal covariance matrices. Rather than estimating the full quadratic discriminant rule, it is proposed to perform simultaneous variable selection and linear dimension reduction on original data, with the subsequent application of quadratic discriminant analysis on the reduced space. Further, in order to support the proposed methodology, two R packages were developed, CCP and DAP, along with two vignettes as long-format illustrations for their usage.

16. Model-Based Penalized Regression

This dissertation contains three chapters that consider penalized regression from a model-based perspective, interpreting penalties as assumed prior distributions for unknown regression coefficients. The first chapter shows that treating a lasso penalty as a prior can facilitate the choice of tuning parameters when standard methods for choosing the tuning parameters are not available, and when it is necessary to choose multiple tuning parameters simultaneously. The second chapter considers a possible drawback of treating penalties as models, specifically possible misspecification. The third chapter introduces structured shrinkage priors for dependent regression coefficients which generalize popular independent shrinkage priors. These can be useful in various applied settings where many regression coefficients are not only expected to be nearly or exactly equal to zero, but also structured.

17. Topics on Least Squares Estimation

This dissertation revisits and makes progress on some old but challenging problems concerning least squares estimation, the work-horse of supervised machine learning. Two major problems are addressed: (i) least squares estimation with heavy-tailed errors, and (ii) least squares estimation in non-Donsker classes. For (i), this problem is studied both from a worst-case perspective, and a more refined envelope perspective. For (ii), two case studies are performed in the context of (a) estimation involving sets and (b) estimation of multivariate isotonic functions. Understanding these particular aspects of least squares estimation problems requires several new tools in the empirical process theory, including a sharp multiplier inequality controlling the size of the multiplier empirical process, and matching upper and lower bounds for empirical processes indexed by non-Donsker classes.

How to Learn More about Machine Learning

At our upcoming event this November 16th-18th in San Francisco,  ODSC West 2021  will feature a plethora of talks, workshops, and training sessions on machine learning and machine learning research. You can  register now for 50% off all ticket types  before the discount drops to 40% in a few weeks. Some  highlighted sessions on machine learning  include:

• Towards More Energy-Efficient Neural Networks? Use Your Brain!: Olaf de Leeuw | Data Scientist | Dataworkz
• Practical MLOps: Automation Journey: Evgenii Vinogradov, PhD | Head of DHW Development | YooMoney
• Applications of Modern Survival Modeling with Python: Brian Kent, PhD | Data Scientist | Founder The Crosstab Kite
• Using Change Detection Algorithms for Detecting Anomalous Behavior in Large Systems: Veena Mendiratta, PhD | Adjunct Faculty, Network Reliability and Analytics Researcher | Northwestern University

Sessions on MLOps:

• Tuning Hyperparameters with Reproducible Experiments: Milecia McGregor | Senior Software Engineer | Iterative
• MLOps… From Model to Production: Filipa Peleja, PhD | Lead Data Scientist | Levi Strauss & Co
• Operationalization of Models Developed and Deployed in Heterogeneous Platforms: Sourav Mazumder | Data Scientist, Thought Leader, AI & ML Operationalization Leader | IBM
• Develop and Deploy a Machine Learning Pipeline in 45 Minutes with Ploomber: Eduardo Blancas | Data Scientist | Fidelity Investments

Sessions on Deep Learning:

• GANs: Theory and Practice, Image Synthesis With GANs Using TensorFlow: Ajay Baranwal | Center Director | Center for Deep Learning in Electronic Manufacturing, Inc
• Machine Learning With Graphs: Going Beyond Tabular Data: Dr. Clair J. Sullivan | Data Science Advocate | Neo4j
• Deep Dive into Reinforcement Learning with PPO using TF-Agents & TensorFlow 2.0: Oliver Zeigermann | Software Developer | embarc Software Consulting GmbH
• Get Started with Time-Series Forecasting using the Google Cloud AI Platform: Karl Weinmeister | Developer Relations Engineering Manager | Google

Daniel Gutierrez, ODSC

Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.

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Thesis/Capstone for Master's in Data Science | Northwestern SPS - Northwestern School of Professional Studies

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Data Science

Capstone and thesis overview.

Capstone and thesis are similar in that they both represent a culminating, scholarly effort of high quality. Both should clearly state a problem or issue to be addressed. Both will allow students to complete a larger project and produce a product or publication that can be highlighted on their resumes. Students should consider the factors below when deciding whether a capstone or thesis may be more appropriate to pursue.

A capstone is a practical or real-world project that can emphasize preparation for professional practice. A capstone is more appropriate if:

• you don't necessarily need or want the experience of the research process or writing a big publication
• you want more input on your project, from fellow students and instructors
• you want more structure to your project, including assignment deadlines and due dates
• you want to complete the project or graduate in a timely manner

A student can enroll in MSDS 498 Capstone in any term. However, capstone specialization courses can provide a unique student experience and may be offered only twice a year. 

A thesis is an academic-focused research project with broader applicability. A thesis is more appropriate if:

• you want to get a PhD or other advanced degree and want the experience of the research process and writing for publication
• you want to work individually with a specific faculty member who serves as your thesis adviser
• you are more self-directed, are good at managing your own projects with very little supervision, and have a clear direction for your work
• you have a project that requires more time to pursue

Students can enroll in MSDS 590 Thesis as long as there is an approved thesis project proposal, identified thesis adviser, and all other required documentation at least two weeks before the start of any term.

From Faculty Director, Thomas W. Miller, PhD

Capstone projects and thesis research give students a chance to study topics of special interest to them. Students can highlight analytical skills developed in the program. Work on capstone and thesis research projects often leads to publications that students can highlight on their resumes.”

A thesis is an individual research project that usually takes two to four terms to complete. Capstone course sections, on the other hand, represent a one-term commitment.

Students need to evaluate their options prior to choosing a capstone course section because capstones vary widely from one instructor to the next. There are both general and specialization-focused capstone sections. Some capstone sections offer in individual research projects, others offer team research projects, and a few give students a choice of individual or team projects.

Students should refer to the SPS Graduate Student Handbook for more information regarding registration for either MSDS 590 Thesis or MSDS 498 Capstone.

Capstone Experience

If students wish to engage with an outside organization to work on a project for capstone, they can refer to this checklist and lessons learned for some helpful tips.

Capstone Checklist

• Start early — set aside a minimum of one to two months prior to the capstone quarter to determine the industry and modeling interests.
• Networking — pitch your idea to potential organizations for projects and focus on the business benefits you can provide.
• Permission request — make sure your final project can be shared with others in the course and the information can be made public.
• Engagement — engage with the capstone professor prior to and immediately after getting the dataset to ensure appropriate scope for the 10 weeks.
• Teambuilding — recruit team members who have similar interests for the type of project during the first week of the course.

Capstone Lesson Learned

• Access to company data can take longer than expected; not having this access before or at the start of the term can severely delay the progress
• Project timeline should align with coursework timeline as closely as possible
• One point of contact (POC) for business facing to ensure streamlined messages and more effective time management with the organization
• Expectation management on both sides: (business) this is pro-bono (students) this does not guarantee internship or job opportunities
• Data security/masking not executed in time can risk the opportunity completely

Publication of Work

Northwestern University Libraries offers an option for students to publish their master’s thesis or capstone in Arch, Northwestern’s open access research and data repository.

Benefits for publishing your thesis:

• Your work will be indexed by search engines and discoverable by researchers around the world, extending your work’s impact beyond Northwestern
• Your work will be assigned a Digital Object Identifier (DOI) to ensure perpetual online access and to facilitate scholarly citation
• Your work will help accelerate discovery and increase knowledge in your subject domain by adding to the global corpus of public scholarly information

Get started:

• Visit Arch online
• Log in with your NetID
• Describe your thesis: title, author, date, keywords, rights, license, subject, etc.
• Upload your thesis or capstone PDF and any related supplemental files (data, code, images, presentations, documentation, etc.)
• Select a visibility: Public, Northwestern-only, Embargo (i.e. delayed release)
• Save your work to the repository

Your thesis manuscript or capstone report will then be published on the MSDS page. You can view other published work here .

For questions or support in publishing your thesis or capstone, please contact [email protected] .

Chapman University Digital Commons

Home > Dissertations and Theses > Computational and Data Sciences (PhD) Dissertations

Computational and Data Sciences (PhD) Dissertations

Below is a selection of dissertations from the Doctor of Philosophy in Computational and Data Sciences program in Schmid College that have been included in Chapman University Digital Commons. Additional dissertations from years prior to 2019 are available through the Leatherby Libraries' print collection or in Proquest's Dissertations and Theses database.

Dissertations from 2024 2024

A Novel Correction for the Multivariate Ljung-Box Test , Minhao Huang

Machine Learning and Geostatistical Approaches for Discovery of Weather and Climate Events Related to El Niño Phenomena , Sachi Perera

Global to Glocal: A Confluence of Data Science and Earth Observations in the Advancement of the SDGs , Rejoice Thomas

Dissertations from 2023 2023

Computational Analysis of Antibody Binding Mechanisms to the Omicron RBD of SARS-CoV-2 Spike Protein: Identification of Epitopes and Hotspots for Developing Effective Therapeutic Strategies , Mohammed Alshahrani

Integration of Computer Algebra Systems and Machine Learning in the Authoring of the SANYMS Intelligent Tutoring System , Sam Ford

Voluntary Action and Conscious Intention , Jake Gavenas

Random Variable Spaces: Mathematical Properties and an Extension to Programming Computable Functions , Mohammed Kurd-Misto

Computational Modeling of Superconductivity from the Set of Time-Dependent Ginzburg-Landau Equations for Advancements in Theory and Applications , Iris Mowgood

Application of Machine Learning Algorithms for Elucidation of Biological Networks from Time Series Gene Expression Data , Krupa Nagori

Stochastic Processes and Multi-Resolution Analysis: A Trigonometric Moment Problem Approach and an Analysis of the Expenditure Trends for Diabetic Patients , Isaac Nwi-Mozu

Applications of Causal Inference Methods for the Estimation of Effects of Bone Marrow Transplant and Prescription Drugs on Survival of Aplastic Anemia Patients , Yesha M. Patel

Causal Inference and Machine Learning Methods in Parkinson's Disease Data Analysis , Albert Pierce

Causal Inference Methods for Estimation of Survival and General Health Status Measures of Alzheimer’s Disease Patients , Ehsan Yaghmaei

Dissertations from 2022 2022

Computational Approaches to Facilitate Automated Interchange between Music and Art , Rao Hamza Ali

Causal Inference in Psychology and Neuroscience: From Association to Causation , Dehua Liang

Advances in NLP Algorithms on Unstructured Medical Notes Data and Approaches to Handling Class Imbalance Issues , Hanna Lu

Novel Techniques for Quantifying Secondhand Smoke Diffusion into Children's Bedroom , Sunil Ramchandani

Probing the Boundaries of Human Agency , Sook Mun Wong

Dissertations from 2021 2021

Predicting Eye Movement and Fixation Patterns on Scenic Images Using Machine Learning for Children with Autism Spectrum Disorder , Raymond Anden

Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models , Alexander Barrett

Applications of Machine Learning to Facilitate Software Engineering and Scientific Computing , Natalie Best

Exploring Behaviors of Software Developers and Their Code Through Computational and Statistical Methods , Elia Eiroa Lledo

Assessing the Re-Identification Risk in ECG Datasets and an Application of Privacy Preserving Techniques in ECG Analysis , Arin Ghazarian

Multi-Modal Data Fusion, Image Segmentation, and Object Identification using Unsupervised Machine Learning: Conception, Validation, Applications, and a Basis for Multi-Modal Object Detection and Tracking , Nicholas LaHaye

Machine-Learning-Based Approach to Decoding Physiological and Neural Signals , Elnaz Lashgari

Learning-Based Modeling of Weather and Climate Events Related To El Niño Phenomenon via Differentiable Programming and Empirical Decompositions , Justin Le

Quantum State Estimation and Tracking for Superconducting Processors Using Machine Learning , Shiva Lotfallahzadeh Barzili

Novel Applications of Statistical and Machine Learning Methods to Analyze Trial-Level Data from Cognitive Measures , Chelsea Parlett

Optimal Analytical Methods for High Accuracy Cardiac Disease Classification and Treatment Based on ECG Data , Jianwei Zheng

Dissertations from 2020 2020

Development of Integrated Machine Learning and Data Science Approaches for the Prediction of Cancer Mutation and Autonomous Drug Discovery of Anti-Cancer Therapeutic Agents , Steven Agajanian

Allocation of Public Resources: Bringing Order to Chaos , Lance Clifner

A Novel Correction for the Adjusted Box-Pierce Test — New Risk Factors for Emergency Department Return Visits within 72 hours for Children with Respiratory Conditions — General Pediatric Model for Understanding and Predicting Prolonged Length of Stay , Sidy Danioko

A Computational and Experimental Examination of the FCC Incentive Auction , Logan Gantner

Exploring the Employment Landscape for Individuals with Autism Spectrum Disorders using Supervised and Unsupervised Machine Learning , Kayleigh Hyde

Integrated Machine Learning and Bioinformatics Approaches for Prediction of Cancer-Driving Gene Mutations , Oluyemi Odeyemi

On Quantum Effects of Vector Potentials and Generalizations of Functional Analysis , Ismael L. Paiva

Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability , Luciano Rodriguez

Gaining Computational Insight into Psychological Data: Applications of Machine Learning with Eating Disorders and Autism Spectrum Disorder , Natalia Rosenfield

Connecting the Dots for People with Autism: A Data-driven Approach to Designing and Evaluating a Global Filter , Viseth Sean

Novel Statistical and Machine Learning Methods for the Forecasting and Analysis of Major League Baseball Player Performance , Christopher Watkins

Dissertations from 2019 2019

Contributions to Variable Selection in Complexly Sampled Case-control Models, Epidemiology of 72-hour Emergency Department Readmission, and Out-of-site Migration Rate Estimation Using Pseudo-tagged Longitudinal Data , Kyle Anderson

Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images , Justin J. Gapper

Estimating Auction Equilibria using Individual Evolutionary Learning , Kevin James

Employing Earth Observations and Artificial Intelligence to Address Key Global Environmental Challenges in Service of the SDGs , Wenzhao Li

Image Restoration using Automatic Damaged Regions Detection and Machine Learning-Based Inpainting Technique , Chloe Martin-King

Theses from 2017 2017

Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods , Michael Schwartz

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Department of Computer Science

Thesis projects and research in ds.

The Master's thesis is a mandatory course of the Master's program in Data Science. The thesis is supervised by a professor of the data science faculty list .

Research in Data Science is a core elective for students in Data Science under the supervision of a data science professor.

Research in Data Science

The project is in independent work under the supervision of a member of the faculty in data science

Only students who have passed at least one core course in Data Management and Processing, and one core course in Data Analysis can start with a research project.

Before starting, the project must be registered in mystudies and a project description must be submitted at the start of the project to the studies administration by e-mail (address see Contact in right column).

Master's Thesis

The Master's Thesis requires 6 months of full time study/work, and we strongly discourage you from attending any courses in parallel. We recommend that you acquire all course credits before the start of the Master’s thesis. The topic for the Master’s thesis must be chosen within Data Science.

Before starting a Master’s thesis, it is important to agree with your supervisor on the task and the assessment scheme. Both have to be documented thoroughly. You electronically register the Master’s thesis in mystudies.

It is possible to complete the Master’s thesis in industry provided that a professor involved in the Data Science Master’s program supervises the thesis and your tutor approves it.

Further details on internal regulations of the Master’s thesis can be downloaded from the following website: www.inf.ethz.ch/studies/forms-and-documents.html .

Overview Master's Theses Projects

Chair of programming methodology.

• Prof. Dr. Martin Vechev

Institute for Computing Platform

• Prof. Dr. Gustavo Alonso
• Prof. Dr. Torsten Hoefler
• Prof. Dr. Ana Klimovic
• Prof. Dr. Timothy Roscoe

Institute for Machine Learning

• Prof. Dr. Valentina Boeva
• Prof. Dr. Joachim Buhmann
• Prof. Dr. Ryan Cotterell    
• external page Prof. Dr. Menna El-Assady call_made   
• Prof. Dr. Niao He
• Prof. Dr. Thomas Hofmann
• Prof. Dr. Andreas Krause
• external page Prof. Dr. Fernando Perez Cruz call_made
• Prof. Dr. Gunnar Rätsch
• external page Prof. Dr. Mrinmaya Sachan call_made
• external page Prof. Dr. Bernhard Schölkopf call_made  
• Prof. Dr. Julia Vogt

Institute for Persasive Computing

• Prof. Dr. Otmar Hilliges

Institute of Computer Systems

• Prof. Dr. Markus Püschel

Institute of Information Security

• Prof. Dr. David Basin
• Prof. Dr. Srdjan Capkun
• external page Prof. Dr. Florian Tramèr call_made

Institute of Theoretical Computer Science

• Prof. Dr. Bernd Gärtner

Institute of Visual Computing

• Prof. Dr. Markus Gross
• Prof. Dr. Marc Pollefeys
• Prof. Dr. Olga Sorkine
• Prof. Dr. Siyu Tang

Disney Research Zurich

• external page Prof. Dr. Robert Sumner call_made

Automatic Control Laboratory

• Prof. Dr. Florian Dörfler
• Prof. Dr. John Lygeros

Communication Technology Laboratory

• Prof. Dr. Helmut Bölcskei

Computer Engineering and Networks Laboratory

• Prof. Dr. Laurent Vanbever
• Prof. Dr. Roger Wattenhofer

Computer Vision Laboratory

• Prof. Dr. Ender Konukoglu
• Prof. Dr. Luc Van Gool
• Prof. Dr. Fisher Yu

Institute for Biomedical Engineering

• Prof. Dr. Klaas Enno Stephan

Integrated Systems Laboratory

• Prof. Dr. Luca Benini
• Prof. Dr. Christoph Studer

Signal and Information Processing Laboratory (ISI)

• Prof. Dr. Amos Lapidoth
• Prof. Dr. Hans-Andrea Loeliger

D-MATH does not publish Master's Theses projects. In case of interest contact the professor directly.

FIM - Insitute for Mathematical Research

• Prof. Dr. Alessio Figalli

Financial Mathematics

• Prof. Dr. Josef Teichmann

Institute for Operations Research

• Prof. Dr. Robert Weismantel
• Prof. Dr. Rico Zenklusen

RiskLab Switzerland

• external page Prof. Dr. Patrick Cheridito call_made
• external page Prof. Dr. Mario Valentin Wüthrich call_made

Seminar for Applied Mathematics

• Prof. Dr. Rima Alaifari
• Prof. Dr. Siddhartha Mishra

Seminar for Statistics

• Prof. Dr. Afonso Bandeira
• Prof. Dr. Peter Bühlmann
• Prof. Dr. Yuansi Chen
• Prof. Dr. Nicolai Meinshausen
• Prof. Dr. Jonas Peters
• Prof. Dr. Johanna Ziegel

Law, Economics, and Data Science Group

• Prof. Dr. Eliott Ash , D-GESS)

Institute for Geodesy and Photogrammetry

• Prof. Dr. Konrad Schindler (D-BSSE)

Home > CCSE > Data Science and Analytics > PhD DSA

Doctor of Data Science and Analytics Dissertations

The PhD Website

The Ph.D. in Data Science and Analytics is an advanced degree with a dual focus of application and research - where students will engage in real world business problems, which will inform and guide their research interests.

We launched the first formal PhD program in Data Science in 2015. Our program sits at the intersection of computer science, statistics, mathematics, and business. Our students engage in relevant research with faculty from across our eleven colleges. As one of the institutions on the forefront of the development of data science as an academic discipline, we are committed to developing the next generation of Data Science leaders, researchers, and educators. Culturally, we are committed to the discipline of Data Science, through ethical practices, attention to fairness, to a diverse student body, to academic excellence, and research which makes positive contributions to our local, regional, and global community. -Sherry Ni, Director, Ph.D. in Data Science and Analytics

This degree will train individuals to translate and facilitate new innovative research, structured and unstructured, complex data into information to improve decision making. This curriculum includes heavy emphasis on programming, data mining, statistical modeling, and the mathematical foundations to support these concepts. Importantly, the program also emphasizes communication skills – both oral and written – as well as application and tying results to business and research problems.

Need to Submit Your Dissertation? Submit Here!

Dissertations from 2023 2023.

Quantification of Various Types of Biases in Large Language Models , Sudhashree Sayenju

Dissertations from 2022 2022

Appley: Approximate Shapley Values for Model Explainability in Linear Time , Md Shafiul Alam

Ethical Analytics: A Framework for a Practically-Oriented Sub-Discipline of AI Ethics , Jonathan Boardman

Novel Instance-Level Weighted Loss Function for Imbalanced Learning , Trent Geisler

Debiasing Cyber Incidents – Correcting for Reporting Delays and Under-reporting , Seema Sangari

Dissertations from 2021 2021

Integrated Machine Learning Approaches to Improve Classification performance and Feature Extraction Process for EEG Dataset , Mohammad Masum

A Distance-Based Clustering Framework for Categorical Time Series: A Case Study in Episodes of Care Healthcare Delivery System , Lauren Staples

Dissertations from 2020 2020

A CREDIT ANALYSIS OF THE UNBANKED AND UNDERBANKED: AN ARGUMENT FOR ALTERNATIVE DATA , Edwin Baidoo

Quantitatively Motivated Model Development Framework: Downstream Analysis Effects of Normalization Strategies , Jessica M. Rudd

Data-driven Investment Decisions in P2P Lending: Strategies of Integrating Credit Scoring and Profit Scoring , Yan Wang

A Novel Penalized Log-likelihood Function for Class Imbalance Problem , Lili Zhang

ATTACK AND DEFENSE IN SECURITY ANALYTICS , Yiyun Zhou

Dissertations from 2019 2019

One and Two-Step Estimation of Time Variant Parameters and Nonparametric Quantiles , Bogdan Gadidov

Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis , Jie Hao

Deep Embedding Kernel , Linh Le

Ordinal HyperPlane Loss , Bob Vanderheyden

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Privacy Copyright DigitalCommons@Kennesaw State University ISSN: 2576-6805

BSc/MSc Thesis

Our research group offers various interesting topics for a BSc or MSc thesis, the latter both in Computer Science and Scientific Computing . These topics are typically closely related to ongoing research projects (see our Research Page and Publications ). Below, we outline the basic procedure you should follow when planning to do a thesis in our group. Please read the following carefully! You also might want to take a quick look at past topics students covered in their theses. Please also note that we currently cannot accommodate all requests for advising a thesis as in current semester  as well as in the upcoming summer semester 2024 we are already advising numerous MSc and BSc theses.

Requirements

A key requirement is that you have taken some advanced courses offered by our group. This includes Data Science for Text Analytics  or  Complex Network Analysis (ICNA) and the more recent master level class on Natural Language Processing with Transformers  (INLPT). Student should also have some background in machine learning, ideally in combination with NLP. We also strongly recommend that prior to starting a thesis (especially a BSc thesis) in our group, you do an advanced software practical to become familiar with the data and tools we use in many of our projects. Most students typically do this in the semester before they officially start their thesis. Further requirements include

• very good programming experience with Python (strongly preferred, including framework like pandas and numpy)
• solid background in statistics and linear algebra
• (optionally) experience with the machine learning frameworks such as PyTorch
• (optionally) experience with NLP frameworks such as spaCy, gensim, LangChain
• (optionally) experience with Opensearch or Elasticsearch
• knowledge using tools such as Github and Docker

It is also advantageous if you have taken some graduate courses in the areas of efficient algorithms (e.g., IEA1 ) and in particular machine learning (e.g., IML , IFML or IAI ). Being familiar with frameworks like scikit-learn , Keras or PyTorch is advantageous.

If you have only taken the undergraduate course introduction to databases (IDB) and none of the other above courses, it is unlikely that we can accommodate your request.

Make also sure that you are familiar with the examination regulations ("Prüfungsordnung") that apply to your program of study.

Getting in Contact

Prior to getting in contact with us you should, of course, read this page in its entirety. If you think your interests and expertise are a good fit for our group and research activities, send an email to Prof. Michael Gertz with the subject "Anfrage BSc Arbeit" or "Anfrage MSc Arbeit" and include the following information:

• your current transcript (as PDF). You can download this from the LSF .
• information about your field of application ("Anwendungsfach"), in particular the courses you have taken
• your programming experience and projects you worked on
• areas of interest based on the research conducted in our group
• any other information you think might strengthen your request

We will then review this information and get back to you with the scheduling of an appointment in person to discuss further details.

Thesis Expose

Once we agree on a topic for your thesis, before you officially register for a thesis, we would like to get an idea of how you approach scientific research and whether you are able to do scientific writing. For this, we require that you write an expose of your planned thesis research (see, e.g., here or here ) . This document is about 4-6 pages and has to include a description of

• the context of your project and research
• problem statement(s)
• objectives and planned approaches
• related work
• milestones towards a timely completion of the thesis

Especially for the related work, it is important that you get a good overview  early on in your thesis project; of course, your advisor will give you some starting points. Most of the time, such an expose becomes an integral part of the introductory chapter of your thesis, so there is no time and effort wasted. The expose needs to be submitted to your advisor on schedule (which you arrange with your advisor), who will then discuss the expose with you and coordinate the next steps. Occasionally we also have students give a 10-15 minute presentation of their research plan in front of the members of our group in order to get further ideas, comments, suggestions, and pointers on their thesis.

Official Registration

In agreement with your advisor, after you have submitted an expose of good quality, you plan for an official start date of the thesis. For this, please fill out the  form suitable for your program of study:

• Für Anmeldung einer Bachelorarbeit, siehe hier . 
• For officially registering your master's thesis, see here . 
• Registration form for a MSc thesis in Scientific Computing (please see Mrs. Kiesel to obtain a form).

Hand in this form to Prof. Michael Gertz who will then turn in the signed form.

Thesis Research and Advising

• Here are some hints on grammar and style we maintain locally.
• Some easy, purely syntactic  hints  on writing good research papers (from Prof. Felix Naumann )
• Dos and don'ts, Universität Heidelberg, Prof. Dr. Anette Frank
• Leitfaden zur Abfassung wissenschaftlicher Arbeiten, Ruhr-Universität Bochum, Katarina Klein
• Leitfaden zur Abfassung wissenschaftlicher Arbeiten, TU Dresden, Maria Lieber

In addition, you can find a detailed description how to write a seminar paper using our template for seminar papers. The hints in this template might also be crucial when you are writing a thesis: [ seminar template .zip ] [ report sample pdf ] [ slides english pdf ] [ slides german pdf ]

Feel also free to ask us for copies of BSc/MSc thesis students did in the past in our group.

Thesis Template

• Thesis template [.zip] ; see a sample PDF here .

Thesis Presentation

• English LaTeX-Beamer template for the presentation: template [.zip] , sample PDF
• German LaTeX-Beamer template for the presentation: template [.zip] , sample PDF

Harvard University Theses, Dissertations, and Prize Papers

The Harvard University Archives ’ collection of theses, dissertations, and prize papers document the wide range of academic research undertaken by Harvard students over the course of the University’s history.

Beyond their value as pieces of original research, these collections document the history of American higher education, chronicling both the growth of Harvard as a major research institution as well as the development of numerous academic fields. They are also an important source of biographical information, offering insight into the academic careers of the authors.

Spanning from the ‘theses and quaestiones’ of the 17th and 18th centuries to the current yearly output of student research, they include both the first Harvard Ph.D. dissertation (by William Byerly, Ph.D . 1873) and the dissertation of the first woman to earn a doctorate from Harvard ( Lorna Myrtle Hodgkinson , Ed.D. 1922).

Other highlights include:

• The collection of Mathematical theses, 1782-1839
• The 1895 Ph.D. dissertation of W.E.B. Du Bois, The suppression of the African slave trade in the United States, 1638-1871
• Ph.D. dissertations of astronomer Cecilia Payne-Gaposchkin (Ph.D. 1925) and physicist John Hasbrouck Van Vleck (Ph.D. 1922)
• Undergraduate honors theses of novelist John Updike (A.B. 1954), filmmaker Terrence Malick (A.B. 1966),  and U.S. poet laureate Tracy Smith (A.B. 1994)
• Undergraduate prize papers and dissertations of philosophers Ralph Waldo Emerson (A.B. 1821), George Santayana (Ph.D. 1889), and W.V. Quine (Ph.D. 1932)
• Undergraduate honors theses of U.S. President John F. Kennedy (A.B. 1940) and Chief Justice John Roberts (A.B. 1976)

What does a prize-winning thesis look like?

If you're a Harvard undergraduate writing your own thesis, it can be helpful to review recent prize-winning theses. The Harvard University Archives has made available for digital lending all of the Thomas Hoopes Prize winners from the 2019-2021 academic years.

Accessing These Materials

How to access materials at the Harvard University Archives

How to find and request dissertations, in person or virtually

How to find and request undergraduate honors theses

How to find and request Thomas Temple Hoopes Prize papers

How to find and request Bowdoin Prize papers

• email: Email
• Phone number 617-495-2461

Related Collections

Harvard faculty personal and professional archives, harvard student life collections: arts, sports, politics and social life, access materials at the harvard university archives.

The chair typically offers various thesis topics each semester in the areas computational statistics, machine learning, data mining, optimization and statistical software. You are welcome to suggest your own topic as well .

Before you apply for a thesis topic make sure that you fit the following profile:

• Knowledge in machine learning.
• Good R or python skills.

Before you start writing your thesis you must look for a supervisor within the group.

Send an email to the contact person listed in the potential theses topics files with the following information:

• Planned starting date of your thesis.
• Thesis topic (of the list of thesis topics or your own suggestion).
• Previously attended classes on machine learning and programming with R.

Your application will only be processed if it contains all required information.

Potential Thesis Topics

[Potential Thesis Topics] [Student Research Projects] [Current Theses] [Completed Theses]

Below is a list of potential thesis topics. Before you start writing your thesis you must look for a supervisor within the group.

Available thesis topics

Disputation.

The disputation of a thesis lasts about 60-90 minutes and consists of two parts. Only the first part is relevant for the grade and takes 30 minutes (bachelor thesis) and 40 minutes (master thesis). Here, the student is expected to summarize his/her main results of the thesis in a presentation. The supervisor(s) will ask questions regarding the content of the thesis in between. In the second part (after the presentation), the supervisors will give detailed feedback and discuss the thesis with the student. This will take about 30 minutes.

• How do I prepare for the disputation?

You have to prepare a presentation and if there is a bigger time gap between handing in your thesis and the disputation you might want to reread your thesis.

• How many slides should I prepare?

That’s up to you, but you have to respect the time limit. Prepariong more than 20 slides for a Bachelor’s presentation and more than 30 slides for a Master’s is VERY likely a very bad idea.

• Where do I present?

Bernd’s office, in front of the big TV. At least one PhD will be present, maybe more. If you want to present in front of a larger audience in the seminar room or the old library, please book the room yourself and inform us.

• English or German?

We do not care, you can choose.

• What do I have to bring with me?

A document (Prüfungsprotokoll) which you get from “Prüfungsamt” (Frau Maxa or Frau Höfner) for the disputation.Your laptop or a USB stick with the presentation. You can also email Bernd a PDF.

• How does the grading work?

The student will be graded regarding the quality of the thesis, the presentation and the oral discussion of the work. The grade is mainly determined by the written thesis itself, but the grade can improve or drop depending on the presentation and your answers to defense questions.

• What should the presentation cover?

The presentation should cover your thesis, including motivation, introduction, description of new methods and results of your research. Please do NOT explain already existing methods in detail here, put more focus on novel work and the results.

• What kind of questions will be asked after the presentation?

The questions will be directly connected to your thesis and related theory.

Student Research Projects

We are always interested in mentoring interesting student research projects. Please contact us directly with an interesting resarch idea. In the future you will also be able to find research project topics below.

Available projects

Currently we are not offering any student research projects.

For more information please visit the official web page Studentische Forschungsprojekte (Lehre@LMU)

Current Theses (With Working Titles)

Completed theses, completed theses (lmu munich), completed theses (supervised by bernd bischl at tu dortmund).

Open Access Theses and Dissertations

Thursday, April 18, 8:20am (EDT): Searching is temporarily offline. We apologize for the inconvenience and are working to bring searching back up as quickly as possible.

Advanced research and scholarship. Theses and dissertations, free to find, free to use.

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About OATD.org

OATD.org aims to be the best possible resource for finding open access graduate theses and dissertations published around the world. Metadata (information about the theses) comes from over 1100 colleges, universities, and research institutions . OATD currently indexes 7,241,108 theses and dissertations.

About OATD (our FAQ) .

Visual OATD.org

We’re happy to present several data visualizations to give an overall sense of the OATD.org collection by county of publication, language, and field of study.

You may also want to consult these sites to search for other theses:

• Google Scholar
• NDLTD , the Networked Digital Library of Theses and Dissertations. NDLTD provides information and a search engine for electronic theses and dissertations (ETDs), whether they are open access or not.
• Proquest Theses and Dissertations (PQDT), a database of dissertations and theses, whether they were published electronically or in print, and mostly available for purchase. Access to PQDT may be limited; consult your local library for access information.

MIT Libraries home DSpace@MIT

• DSpace@MIT Home
• MIT Libraries

This collection of MIT Theses in DSpace contains selected theses and dissertations from all MIT departments. Please note that this is NOT a complete collection of MIT theses. To search all MIT theses, use MIT Libraries' catalog .

MIT's DSpace contains more than 58,000 theses completed at MIT dating as far back as the mid 1800's. Theses in this collection have been scanned by the MIT Libraries or submitted in electronic format by thesis authors. Since 2004 all new Masters and Ph.D. theses are scanned and added to this collection after degrees are awarded.

MIT Theses are openly available to all readers. Please share how this access affects or benefits you. Your story matters.

If you have questions about MIT theses in DSpace, [email protected] . See also Access & Availability Questions or About MIT Theses in DSpace .

If you are a recent MIT graduate, your thesis will be added to DSpace within 3-6 months after your graduation date. Please email [email protected] with any questions.

Permissions

MIT Theses may be protected by copyright. Please refer to the MIT Libraries Permissions Policy for permission information. Note that the copyright holder for most MIT theses is identified on the title page of the thesis.

Theses by Department

• Comparative Media Studies
• Computation for Design and Optimization
• Computational and Systems Biology
• Department of Aeronautics and Astronautics
• Department of Architecture
• Department of Biological Engineering
• Department of Biology
• Department of Brain and Cognitive Sciences
• Department of Chemical Engineering
• Department of Chemistry
• Department of Civil and Environmental Engineering
• Department of Earth, Atmospheric, and Planetary Sciences
• Department of Economics
• Department of Electrical Engineering and Computer Sciences
• Department of Humanities
• Department of Linguistics and Philosophy
• Department of Materials Science and Engineering
• Department of Mathematics
• Department of Mechanical Engineering
• Department of Nuclear Science and Engineering
• Department of Ocean Engineering
• Department of Physics
• Department of Political Science
• Department of Urban Studies and Planning
• Engineering Systems Division
• Harvard-MIT Program of Health Sciences and Technology
• Institute for Data, Systems, and Society
• Media Arts & Sciences
• Operations Research Center
• Program in Real Estate Development
• Program in Writing and Humanistic Studies
• Science, Technology & Society
• Science Writing
• Sloan School of Management
• Supply Chain Management
• System Design & Management
• Technology and Policy Program

Collections in this community

Doctoral theses, graduate theses, undergraduate theses, recent submissions.

The pulse amplifier in theory and experiment 

Optical studies of the nature of metallic surfaces , a controlled community for waterbury, connecticut .

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