Summer Research Program with
Columbia University

Develop data science skills | Conduct research with top university faculty | Increase college competitiveness

Grades: Incoming 10 - 12th + College Freshmen

5-week program: Summer 2024 dates announced in February 2024

2024 Interest Form

Research Programs created by professors and researchers at:

  • UNC

Program Overview

This program offers an unprecedented opportunity for high school students to develop tangible technical skills in one of the world's most sought after areas of tech - Data Science - which has applications across every industry from medicine to sports. Transforming from zero knowledge to a full skill set in data science, students will then be trained in conducting research and will have the opportunity to work on current research being conducted at the Columbia University Data Science Institute. Serving as research assistants to Columbia University professors and PhD candidates along with other top university faculty, students will develop a research project and presentation to showcase in their college applications.

This five-week program consists of two main components:

Weeks 1-2:

Students receive intensive training in data science, developing all the skills needed to conduct research at a college level including coding skills in R, data visualizations, data cleaning, data analysis and so much more.

Weeks 3-5:

Students put their data science skills into action while assisting PhD researchers with real-life research projects that are overseen by university professors.

Upon completion of the program, students are presented with a certificate and will have a research project to showcase on college apps.

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It is exciting to see high school students learn and practice data skills before they have even started college considering that students normally gain this level of knowledge at the undergraduate and graduate level."

Susan McGregor

Susan McGregor - Professor, Columbia University Data Science Institute

After completing this program...


of students are more likely to major in a STEM subject in college after this program


of students felt more confident in both their coding and research skills after this program


of students agree this program has prepared them for college

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Hear from Our Students

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Group of students working on computers around a table

What Students Learn

  • Data Analysis
  • Data Visualization
  • Web Scraping
  • Data Cleaning and Wrangling
  • Research Protocol

Alongside developing technical skills, students are trained to professionally present their findings to professors in both lightning pitches and full-style presentations. Faculty will provide personal feedback to help the student grow in their research and presentation skills. Depending on the student's findings there is a potential for publication in academic journals. By the end of the program, students have comprehensive programming skills in R, technical skills in data science and research, a completed research project and are presented with a certificate of completion.

More from Our Students

Testimonial Student

Learn data science - one of the fastest growing fields of CS

Participate in research projects utilizing data analysis

Receive mentorship from university faculty and PhDs

Increase college competitiveness

Program Benefits

  • Increase college competitiveness
  • Acquire research knowledge
  • Create research project for portfolio
  • Build tangible computer science skills
  • Real-world application to current research project
  • Build relationships with researchers at top universities

Students will conclude the program with data science research knowledge, relationships with professors at top universities, tangible computer science skills, and hands-on experience in interdisciplinary research. Whether students are looking to learn more about how data science is applicable to their own interests, ethics in research, or get a head start on college research experience, they will all gain knowledge not easily accessible to high school students.

2023 Professors

Columbia University Data Science Research Institute

The Coding School is honored to be partnered with Columbia University professors and faculty to provide students with the highest quality research experience available to high school students. Students will have an opportunity to meet and work under the guidance of Columbia University's outstanding Data Science Research Institute team.

Susan E. McGregor

Susan E. McGregor is an associate research scholar at Columbia University's Data Science Institute, where she co-chairs the Center for Data, Media & Society.

McGregor's research primarily centers on security and privacy issues affecting journalists and media organizations. Her current work includes an NSF-funded project to provide readers with stronger guarantees about digital media by incorporating cryptographic signatures into digital publishing workflows. She works with natural language processing experts to develop novel classifiers for detecting abusive speech on Twitter, and is researching effective models for peer support with a focus on the media space. Her work in these and related areas has received support from the NSF, the Knight Foundation, Google, Columbia University, among others.

As an educator, McGregor is committed to increasing the reach of data science education to help ensure that essential literacies about data-driven systems are accessible to learners of every age and background. Prior to her work at Columbia University, McGregor was the senior programmer for the News Graphics team at the Wall Street Journal (WSJ), a front-end programmer at the photo wire service MediaVast (acquired by Getty Images), and a reporter for The New York Amsterdam News.

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Alexander Urban

Alex Urban is an Assistant Professor of Chemical Engineering and a core faculty-member of the Columbia Electrochemical Energy Center (CEEC) and the Columbia Center for Computational Electrochemistry (CCCE).

His research interests are in understanding and discovering materials and processes for clean-energy applications using atomic-scale modeling and data science. Prior to joining the Department of Chemical Engineering at Columbia University in 2019, Alex was an independent University Fellow in the School of Chemistry at the University of St Andrews (UK). Alex obtained his PhD in 2012 from FAU Erlangen-Nuremberg in Germany and conducted postdoctoral research at MIT and UC Berkeley. In 2019, Alex was named a Scialog Fellow for Advanced Energy Storage by the Research Corporation for Science Advancement.

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Xuan (Sharon) Di

Dr. Xuan (Sharon) Di is an Associate Professor in the Department of Civil Engineering and Engineering Mechanics at Columbia University and serves on a committee for the Smart Cities Center in the Data Science Institute.

Prior to joining Columbia, she was a Postdoctoral Research Fellow at the University of Michigan Transportation Research Institute. She received her Ph.D. degree from the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities in 2014. Dr. Di directs the DitecT (Data and innovative technology-driven Transportation) Lab @ Columbia University. Her research lies at the intersection of game theory, dynamic control, and machine learning. She is specialized in emerging transportation systems optimization and control, shared mobility modeling, and data-driven urban mobility analysis. Details about DitecT Lab and Prof. Sharon Di's research can be found in the following link:

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Hongseok Namkoong

Hongseok Namkoong is an Assistant Professor in the Decision, Risk, and Operations division at Columbia Business School and a member of the Columbia Data Science Institute.

His research interests lie at the interface of machine learning, operations research, and causal inference, with a particular emphasis on developing reliable learning methods for decision-making problems. He received his Ph.D. from Stanford University and worked as a research scientist at Facebook Core Data Science before joining Columbia. Hong is a recipient of several awards and fellowships, including best paper awards at the Neural Information Processing Systems conference and the International Conference on Machine Learning, and the INFORMS Applied Probability Society.

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Dimitri Livitz

Dimitri is a chemical engineer, who approaches research computationally. He has worked in bioinformatics, studying the dynamics of cancer growth and now studies soft matter, focusing on microrobots.

He is interested in exploring the intersection of data science and physical systems, and uses python and julia for his research. He is also excited about data visualization, and trying to present findings in interesting ways.

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Gustavo Novoa

Gustavo Novoa is a PhD candidate in Political Science with a minor in quantitative methods at Columbia University.

He is currently writing a mixed-methods dissertation on local gerrymandering in U.S. cities using Bayesian simulations and expert interviews to determine how local gerrymandering is different from what we see at the federal level. In his free time he likes climbing, playing guitar, and gardening.

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Maryum Sayeed

Maryum Sayeed is a PhD student in the Department of Astronomy at Columbia University. She received her Bachelor's in Physics & Astronomy from the University of British Columbia in Vancouver, Canada.

While she has a diverse research background, her main focus is in stellar astrophysics, galactic archaeology and the stellar-exoplanetary synergy. She is interested in using large-scale photometric, spectroscopic, and astrometric surveys - such as Kepler, TESS, Gaia, APOGEE, LAMOST and GALAH - to study the formation and evolution of the Milky Way. She is also passionate about mentorship and outreach.

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*2022 program professors and PhD researchers. 2023 team may include new or different university faculty.

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2023 Lead Instructor

Sarah Parker

Data Science instruction and daily research task assignments will be led and overseen by Sarah Parker, PhD researcher in the Genetics Department at the University of North Carolina.

The University of North Carolina is one of the top five federal research institutions in the United States and is known for its innovative approach to conducting research. Sarah is a PhD candidate at the University of North Carolina pursuing a PhD in Bioinformatics and Computational Biology. She previously graduated from Pepperdine University with a BS in Biology and a minor in Computer Science. Her main interests lie in developing computational tools to aid in biological research and scientific communication. Some of her projects include a pipeline for the virtual screening process of drug discovery and a computer game to promote the conservation of giant kelp. Currently, she is developing genomics software, including a project to predict pairs of genes and the genomic elements that regulate them. Some of her other hobbies include baking, singing, and SCUBA diving.

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How to Apply

Due to the overwhelming interest in this Research Program and rigor of the material, students are required to submit a Statement of Interest when filling out the application form.

The Statement of Interest should be 300-500 words that highlights your interest in learning data science and conducting research, future university and career aspirations, and any other relevant information.

Note: We do not expect students to have experience in research or STEM.

We are looking for students with passion, interest and curiosity to learn more about these fields.

Steps to Apply

  1. Complete the application and upload the Statement of Interest at the link below.
  2. Applicants are not required to submit payment at the time of application.
  3. Applicants will be notified via email on a rolling basis.
  4. If your child is selected, acceptance and tuition will be due within 72 hours.
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Incoming 10th - 12th graders and college freshmen


5-week program:
Dates announced in 2024.


Monday - Friday
9am - 1pm PST (12-4pm EST) + Independent work.

*Must be able to join live sessions


Virtual, with live instruction


No prior STEM experience required Students will learn all skills required during the program



*Need-based scholarships are available