40 Data Science Research Topics for High School Students
If you are a student curious about how data can be used to answer real questions, independent research is one of the most direct ways to find out. Data science is uniquely accessible because the tools are free, most of the best datasets are publicly available, and the questions worth asking are everywhere.
What do data science research projects actually involve?
A data science project typically starts with a question, then moves through collecting or sourcing data, cleaning and analyzing it, and communicating what you found. You will use tools like Python, Pandas, and Matplotlib for most of the work, and free platforms like Kaggle and UCI Machine Learning Repository provide datasets for nearly every topic on this list.
How do I pick a topic worth working on?
The best starting point is a domain you already care about, whether that is sports, health, music, climate, or something else, since genuine curiosity leads to more thorough analysis than picking a topic that sounds impressive. Choose something specific enough to answer with the data that actually exists, and start with a single focused question rather than trying to cover everything at once.
To help you get started, we've put together 40 data science research topics for high school students. For guidance on structuring and presenting your findings, check out our guides on startup tips for young entrepreneurs and 30 startup ideas for high school students.
Quick Look
40 research topics organized across four broad areas: predictive modeling and machine learning, social and environmental data analysis, business and market applications, and educational and behavioral research
Most beginner-accessible topics: Titanic survival prediction (classic ML dataset, well-documented), Iris flower classification (simple multiclass dataset), and Predicting Student Performance (uses commonly available academic data)
Best for students with Python or coding experience: Sentiment analysis with NLP, image classification with CNNs, traffic congestion prediction with deep learning, and stock price forecasting with time series models
Best for students interested in social impact: Analyzing educational inequality, COVID-19 vaccination trends, urban air quality modeling, and measuring personal carbon footprint
Best for students with sports or entertainment interests: Predicting game outcomes or player performance, music genre classification, Netflix/Spotify recommendation systems, and YouTube video popularity prediction
Recommended free dataset sources: Kaggle, UCI Machine Learning Repository, Google Dataset Search, and government open data portals (data.gov, WHO, World Bank)
40 Data Science Research Topics for High School Students
1. Predicting Housing Prices Using Regression Analysis
Investigate how factors such as location, house size, number of rooms, and property age influence housing prices in a specific city or region.
2. Analyzing Global Temperature Trends Using Time Series Data
Study long-term global temperature records to understand climate change trends and make simple forecasts.
3. Identifying Sentiment in Social Media Posts with Natural Language Processing
You will examine how people feel about a specific topic, such as a movie release, a political event, or a sports final, by analyzing social media posts.
4. Credit Card Fraud Detection with Imbalanced Classification
In this research topic, you will use transaction data to build a model that distinguishes genuine credit card transactions from fraudulent ones.
5. Predicting Survival on the Titanic Using Classic Machine Learning Data
Use the famous Titanic dataset, which includes information about passengers such as age, gender, cabin class, and survival outcome, to explore binary classification.
6. Classifying Iris Flowers with Multiclass Machine Learning
In this topic, you will work with the classic Iris flower dataset, which contains measurements of sepal length, sepal width, petal length, and petal width for three species of iris flowers.
7. Predicting Diabetes Risk from Health and Demographic Data
This project uses a medical dataset in which each row represents a person, with features such as age, body mass index (BMI), blood pressure, and glucose levels, along with a label indicating whether they are likely to have diabetes.
8. Breast Cancer Classification Using Machine Learning
In this topic, you will analyze a breast cancer dataset containing measurements of cell-nucleus features (such as radius, texture, and smoothness) extracted from medical images, along with labels indicating benign or malignant tumors.
9. Analyzing Bike-Sharing Demand with Time Series and Regression
This project focuses on understanding and predicting the demand for bike-sharing services in a city. Public datasets like the Capital Bikeshare dataset provide hourly and daily counts of bike rentals along with weather and seasonal information.
10. Forecasting Energy Consumption for Smarter Grids
In this topic, you will analyze energy consumption time series to forecast future demand and discuss how such forecasts can contribute to more efficient and sustainable power systems.
11. Predicting Airline Passenger Satisfaction from Survey Data
Here, you will use survey data from airline passengers to understand what factors drive overall satisfaction and to build a model that predicts whether a passenger is satisfied or dissatisfied.
12. Modeling Customer Churn in Banking or Subscription Services
In this research topic, you will predict whether a customer will leave a bank or cancel a subscription based on their usage patterns and demographic information.
13. Building a Movie Recommendation System with Clustering
In this project, you will create a simple recommendation system that suggests movies to users based on content similarities or user preferences.
14. Understanding How Spotify or Netflix Recommendation Systems Work
Instead of building your own large-scale recommender, this topic invites you to reverse-engineer and critically analyze how major streaming platforms personalize content.
15. Predicting Game Outcomes or Player Performance in Sports
If you love sports, you can combine that passion with data science by modeling game outcomes or individual player performance, like predicting individual statistics, such as points per game or expected goals, and analyzing how performance trends evolve over a season.
16. Analyzing COVID-19 Vaccination Trends and Their Correlates
In this project, you will use COVID-19 vaccination data to study patterns across countries or regions and investigate factors associated with higher or lower vaccination rates.
17. Creating a COVID-19 Data Dashboard with Interactive Visualizations
Your goal is to build an interactive dashboard that lets users explore COVID-19 case counts, deaths, recoveries, or vaccinations across regions and over time.
18. Forecasting Stock Prices with Time Series and Machine Learning
This topic explores the challenge of predicting stock prices using historical price data. Academic research on stock price prediction based on time series analysis surveys various models, including traditional statistical methods and newer machine learning approaches.
19. Predicting Traffic Congestion Using Deep Learning
Research on deep learning for traffic congestion forecasting demonstrates how neural networks can model complex temporal and spatial patterns in public traffic datasets.
20. Modeling Urban Air Quality and Pollution Levels
This topic focuses on predicting or explaining air quality metrics such as particulate matter (PM2.5) concentrations using environmental and meteorological data.
21. Measuring and Modeling Your Personal Carbon Footprint
In this project, you will investigate your own or your household’s carbon footprint and explore ways to model and reduce it.
22. Exploring Trends in High School Graduation Rates with Educational Data
Analyze how graduation numbers have changed over time nationally, and compare them across states or demographic groups when available, using visualizations and trend lines.
23. Analyzing Amazon Product Reviews for Sentiment and Themes
In this project, you will work with a large collection of Amazon product reviews to study sentiment patterns and recurring themes.
24. Predicting YouTube Video Popularity with Engagement Data
Here, you will explore what makes some YouTube videos more successful than others by analyzing metadata such as title, description, length, upload time, and early engagement metrics.
25. Modeling Influencer Marketing Campaign Performance on Social Media
In this topic, you will examine metrics that determine the success of influencer marketing campaigns on platforms such as Instagram and TikTok.
26. Predicting Student Performance from Study Habits and School Data
Predict academic performance based on variables such as study time, attendance, participation in extracurricular activities, and demographic factors.
27. Forecasting Bike or Scooter Accidents in Urban Areas
Aim to understand and predict patterns of bike or scooter accidents in a city, using open transportation and safety datasets if they are available.
28. Modeling Restaurant Ratings and Building a Simple Recommender
Use restaurant data to predict ratings and recommend restaurants that match user preferences.
29. Music Genre Classification Using Audio Features and Neural Networks
Explore whether you can automatically classify songs into genres using audio features or spectrograms.
30. Image Classification of Everyday Objects Using Convolutional Neural Networks
This project involves building an image classifier that can distinguish between categories such as different types of fruits, vehicles, or animals.
31. Analyzing News Coverage with Web Scraping and NLP
Scrape news articles from multiple sources to analyze differences in coverage, framing, or sentiment around a particular event or issue.
32. Predicting Election Results with Polling and Demographic Data
This project focuses on building models that predict election outcomes using polling data, past results, and demographic indicators.
33. Time Series Analysis of Public Transportation Ridership
Examine how ridership on buses, subways, or light rail changes over time by analyzing patterns across days of the week, holidays, and special events, using time-series plotting and decomposition to identify trends and seasonal effects.
34. Time Series Analysis of Video Game Usage or Engagement
Analyze usage data for a video game, such as daily active users, session lengths, or in-game purchases, to understand engagement patterns over time.
35. Modeling School Attendance Patterns and Their Predictors
This project involves analyzing attendance data at your school or a publicly available dataset to identify patterns and predictors of absenteeism.
36. Analyzing Patterns in Independent Learning or Online Course Completion
Analyze data from online learning platforms or self-reported logs to identify factors that correlate with course completion and learning outcomes.
37. Studying the Impact of AI Use on Students’ Critical-Thinking Skills
Examining the relationship between students’ use of AI tools and their self-reported or assessed critical-thinking abilities.
38. Clustering Neighborhoods Based on Socioeconomic and Health Indicators
Group neighborhoods or regions based on socioeconomic and health indicators such as income, education level, unemployment, life expectancy, or disease rates.
39. Using Data Science to Study Educational Inequality
Analyze educational data to investigate disparities in outcomes such as test scores, graduation rates, or access to advanced courses across different groups.
40. Forecasting Demand for Public Health Services
This project involves modeling demand for public health services, such as vaccination clinics, testing sites, and community health centers, based on historical usage and contextual variables.
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If you want mentorship from successful entrepreneurs in building your business, the Young Founders Lab is one of the strongest programs you can join in high school. It’s a 100% virtual start-up boot camp run by Harvard entrepreneurs, designed specifically for students who want to launch a company or non-profit.
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Frequently Asked Questions
1. Do I need advanced coding skills to start a data science research project in high school?
No. Several topics on this list are commonly used as beginner projects and have extensive tutorials and pre-cleaned datasets available. Python is the most practical language to start with, and libraries like Pandas, Matplotlib, and Scikit-learn cover the tools needed for most topics on this list. Starting with a well-documented dataset and a single clear question is more important than having advanced coding skills from the outset.
2. How do I turn a research topic into a complete project I can include in my college application?
A complete data science project typically includes four components: a clear research question, a sourced and cleaned dataset, an analysis or model with visualizations, and a written summary of findings. Tools like Jupyter Notebook make it easy to combine code, charts, and written explanation in a single document that can be shared online via GitHub or Google Colab. Even a beginner-level project that asks a focused question, works with real data, and explains its findings clearly is a meaningful portfolio piece for college applications.
3. Where can I get mentorship or structured support for a data science research project?
Several programs offer mentorship for independent research projects. FinAcademy Research Lab and Scholastica's Economic and Finance Internship both guide students through co-authoring a research paper. If you want to combine data science skills with entrepreneurship and build a project with real-world application, the Young Founders Lab pairs you with mentors from companies like Google, Microsoft, and X to develop and validate a venture, many of which involve data-driven products or analysis.