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Assessing the effects of fuel energy consumption, foreign direct investment and GDP on CO2 emission: New data science evidence from Europe & Central Asia

Documentation matters: human-centered ai system to assist data science code documentation in computational notebooks.

Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code, and neglect creating or updating their documentation during quick iterations. Inspired by human documentation practices learned from 80 highly-voted Kaggle notebooks, we design and implement Themisto, an automated documentation generation system to explore how human-centered AI systems can support human data scientists in the machine learning code documentation scenario. Themisto facilitates the creation of documentation via three approaches: a deep-learning-based approach to generate documentation for source code, a query-based approach to retrieve online API documentation for source code, and a user prompt approach to nudge users to write documentation. We evaluated Themisto in a within-subjects experiment with 24 data science practitioners, and found that automated documentation generation techniques reduced the time for writing documentation, reminded participants to document code they would have ignored, and improved participants’ satisfaction with their computational notebook.

Data science in the business environment: Insight management for an Executive MBA

Adventures in financial data science, gecoagent: a conversational agent for empowering genomic data extraction and analysis.

With the availability of reliable and low-cost DNA sequencing, human genomics is relevant to a growing number of end-users, including biologists and clinicians. Typical interactions require applying comparative data analysis to huge repositories of genomic information for building new knowledge, taking advantage of the latest findings in applied genomics for healthcare. Powerful technology for data extraction and analysis is available, but broad use of the technology is hampered by the complexity of accessing such methods and tools. This work presents GeCoAgent, a big-data service for clinicians and biologists. GeCoAgent uses a dialogic interface, animated by a chatbot, for supporting the end-users’ interaction with computational tools accompanied by multi-modal support. While the dialogue progresses, the user is accompanied in extracting the relevant data from repositories and then performing data analysis, which often requires the use of statistical methods or machine learning. Results are returned using simple representations (spreadsheets and graphics), while at the end of a session the dialogue is summarized in textual format. The innovation presented in this article is concerned with not only the delivery of a new tool but also our novel approach to conversational technologies, potentially extensible to other healthcare domains or to general data science.

Differentially Private Medical Texts Generation Using Generative Neural Networks

Technological advancements in data science have offered us affordable storage and efficient algorithms to query a large volume of data. Our health records are a significant part of this data, which is pivotal for healthcare providers and can be utilized in our well-being. The clinical note in electronic health records is one such category that collects a patient’s complete medical information during different timesteps of patient care available in the form of free-texts. Thus, these unstructured textual notes contain events from a patient’s admission to discharge, which can prove to be significant for future medical decisions. However, since these texts also contain sensitive information about the patient and the attending medical professionals, such notes cannot be shared publicly. This privacy issue has thwarted timely discoveries on this plethora of untapped information. Therefore, in this work, we intend to generate synthetic medical texts from a private or sanitized (de-identified) clinical text corpus and analyze their utility rigorously in different metrics and levels. Experimental results promote the applicability of our generated data as it achieves more than 80\% accuracy in different pragmatic classification problems and matches (or outperforms) the original text data.

Impact on Stock Market across Covid-19 Outbreak

Abstract: This paper analysis the impact of pandemic over the global stock exchange. The stock listing values are determined by variety of factors including the seasonal changes, catastrophic calamities, pandemic, fiscal year change and many more. This paper significantly provides analysis on the variation of listing price over the world-wide outbreak of novel corona virus. The key reason to imply upon this outbreak was to provide notion on underlying regulation of stock exchanges. Daily closing prices of the stock indices from January 2017 to January 2022 has been utilized for the analysis. The predominant feature of the research is to analyse the fact that does global economy downfall impacts the financial stock exchange. Keywords: Stock Exchange, Matplotlib, Streamlit, Data Science, Web scrapping.

Information Resilience: the nexus of responsible and agile approaches to information use

AbstractThe appetite for effective use of information assets has been steadily rising in both public and private sector organisations. However, whether the information is used for social good or commercial gain, there is a growing recognition of the complex socio-technical challenges associated with balancing the diverse demands of regulatory compliance and data privacy, social expectations and ethical use, business process agility and value creation, and scarcity of data science talent. In this vision paper, we present a series of case studies that highlight these interconnected challenges, across a range of application areas. We use the insights from the case studies to introduce Information Resilience, as a scaffold within which the competing requirements of responsible and agile approaches to information use can be positioned. The aim of this paper is to develop and present a manifesto for Information Resilience that can serve as a reference for future research and development in relevant areas of responsible data management.

qEEG Analysis in the Diagnosis of Alzheimers Disease; a Comparison of Functional Connectivity and Spectral Analysis

Alzheimers disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain, causing a decline in cognitive abilities and difficulties in engaging in day-to-day activities. This study compares an FFT-based spectral analysis against a functional connectivity analysis based on phase synchronization, for finding known differences between AD patients and Healthy Control (HC) subjects. Both of these quantitative analysis methods were applied on a dataset comprising bipolar EEG montages values from 20 diagnosed AD patients and 20 age-matched HC subjects. Additionally, an attempt was made to localize the identified AD-induced brain activity effects in AD patients. The obtained results showed the advantage of the functional connectivity analysis method compared to a simple spectral analysis. Specifically, while spectral analysis could not find any significant differences between the AD and HC groups, the functional connectivity analysis showed statistically higher synchronization levels in the AD group in the lower frequency bands (delta and theta), suggesting that the AD patients brains are in a phase-locked state. Further comparison of functional connectivity between the homotopic regions confirmed that the traits of AD were localized in the centro-parietal and centro-temporal areas in the theta frequency band (4-8 Hz). The contribution of this study is that it applies a neural metric for Alzheimers detection from a data science perspective rather than from a neuroscience one. The study shows that the combination of bipolar derivations with phase synchronization yields similar results to comparable studies employing alternative analysis methods.

Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.

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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

Advancement in In-Silico Drug Discovery from Virtual Screening Molecular Dockings to De-Novo Drug Design Transformer-based Generative AI and Reinforcement Learning , Dony Ang

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

Medical Image Analysis Based on Graph Machine Learning and Variational Methods , Sina Mohammadi

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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How to write a great data science thesis, some tips and tricks to help make you write your thesis as smoothly as possible.

Max van der Werf

Towards Data Science

There are probably more than a thousand manuals on how to write a great thesis (some of my favorites can be found here , here and here ). They will stress the importance of structure, substance and style. They will urge you to write down your methodology and results first, then progress to the literature review, introduction and conclusions and to write the summary or abstract last. To write clearly and directly with the reader’s expectations always in mind. All of these tips are very valuable, but which tips apply to writing academically in the domain of data science?

Data science students originate from all kinds of fields and are not necessarily experienced writers of technical papers. Thus, which specific tips should one read before they proceed to write a great data science thesis? Having finished my data science master’s two weeks ago, I feel like somewhat of an expert in this field. Most of me and my peers’ struggles are still quite fresh in my mind, and I would like to convey the most practical solutions here.

Choosing a Research Topic

Written by Max van der Werf

Aspiring data scientist interested in sports, psychology, economics and everything in between.

Text to speech

Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

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

PS – This is just the start…

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

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

Data Science-Related Research Topics

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

Data Science Research Ideas (Continued)

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

Recent Data Science-Related Studies

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

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

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

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

Get 1-On-1 Help

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

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

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

As businesses seek to employ data to boost digital and industrial transformation, companies across the globe are looking for skilled and talented data professionals who can leverage the meaningful insights extracted from the data to enhance business productivity and help reach company objectives successfully. Recently, data science has turned into a lucrative career option. Nowadays, universities and institutes are offering various data science and big data courses to prepare students to achieve success in the tech industry. The best course of action to amplify the robustness of a resume is to participate or take up different data science projects. In this article, we have listed 10 such research and thesis topic ideas to take up as data science projects in 2022.

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

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