8 Data Science Extracurriculars for High School Students

Data science is an interdisciplinary field employing scientific approaches, algorithms, processes, and systems to derive valuable insights from data. As a high school student, participating in data science extracurriculars such as bootcamps, competitions, and pre-college courses provides unique advantages. These experiences offer hands-on exposure to real-world datasets, reinforcing classroom learning and honing practical data analysis skills. Honing one’s data science abilities in high school offers a head start on essential concepts, providing a solid foundation for future academic pursuits. 

To help you get started, here are 8 data science extracurriculars high school students can get started with! 

1. Join a data science program

If you’re aspiring to forge a career in the field of data science, an excellent starting point is enrolling in a program dedicated to this discipline. Joining a data science program provides a comprehensive learning experience, mentorship from industry experts, networking opportunities, and valuable preparation for college. Here are some programs you should consider: 

• Veritas AI: Through the 10-session AI Scholars program you learn the fundamentals of data science and AI and create real-world projects in groups of 3-4 students. You learn from expert mentors and engage in coding notebooks to understand how to create hands-on models. 

• Wharton Global Youth’s Data Science Academy: This program enables students to design their own data science projects exploring techniques such as data visualization, wrangling, and machine learning methodologies. 

• The University of Chicago’s Data Science Institute Summer Lab: This is a 10-week research opportunity pairing students with mentors and delving deep into a data science research topic. 

As a participant in a data science summer program, you’ll have the opportunity to delve into a range of data science topics and explore advanced concepts, including artificial intelligence and machine learning. These programs establish a robust foundation in data science, exposing students to the latest innovations and cutting-edge technologies in the field.

2. Join or start a data science club at your school

Joining a data science club or organization presents an exciting avenue to immerse yourself in the realm of data and analytics. It enables you to extend your knowledge and skills beyond the standard academic curriculum while fostering connections with peers who share similar interests.

Here are a few great sources of inspiration to consider if you plan on joining or forming a new club: Data Rizon supports middle and high school student-led data science clubs, GirlsWhoCode offers 3rd-5th and 6th-12th grade students to join a free community of supportive peers and role models using computer science to change the world, and the Green Level High School AI and Machine Learning Club offers students educational resources, access to competitions, and tools to work on independent projects. 

Understanding the value that data science clubs can bring to your student resume and college applications is crucial. These clubs emphasize collaboration, offering hands-on learning experiences where you can actively engage in data analysis projects and develop practical skills in machine learning and statistical modeling. However, it’s worth noting that clubs might limit exposure since members typically share a similar age and experience level.

3. Take part in data science, machine learning, and coding competitions

If you aim to enhance your skills in data science, delve into machine learning, and explore advanced coding techniques, participating in local and national competitions is a compelling choice! From Microsoft’s Imagine Cup, the Congressional App Challenge, and Stanford ProCo, there are multiple coding, data science, and machine learning competitions to consider participating in, scaling varying levels of experience. 

The inclusion of data science and machine learning competition achievements in your portfolio highlights your practical problem-solving skills, demonstrates your application of theoretical knowledge, and underscores your dedication and enthusiasm for the field. The collaborative nature of these competitions fosters an engaging environment, facilitating the building of a network with peers who share your interests. However, the competitive aspect may pose a challenge, particularly for less experienced participants.

4. Enroll in a data science internship 

Internships provide a prime opportunity to acquire practical insight and immerse oneself in the vibrant field of data science. Collaborating with seasoned professionals offers invaluable insights into how academic knowledge translates into tangible solutions, effectively preparing you for a career in data science. Here are some internship opportunities open to high school students: 

• Ladder Internships is a selective internship program for high school students who wish to work with start-ups on data science-related topics.

• The Stanford Center for Artificial Intelligence in Medicine and Imaging’s Summer Research Internship is a 2-week virtual internship for any high school student interested in exploring the technical and clinical aspects of AI in healthcare. 

• SPARK Summer Internship Program has enabled students to work on real-world scenarios involving machine learning, artificial intelligence, and data visualization. 

Data science internships empower students to actively participate in real projects, tackle challenges, and contribute to broader work areas. This experiential learning not only enhances technical and programming skills but also instills industry-specific competencies, whether in software development, data analysis, or cybersecurity.

5. Volunteer to teach data science

If you possess a foundational understanding of data science and are proficient enough to guide fellow students, consider volunteering in a data science outreach program! 

For instance, Data Science for Social Good’s Solve for Good Volunteering Program is a platform where social good organizations post data projects they need help with, and volunteers scope those projects into well-defined challenges to solve them. Volunteering with DataKind involves collaborating with interdisciplinary teams, navigating diverse datasets, and tackling real-world problems. Volunteering via Catchafire enables you to help organizations looking for data solutions in finance, tech, and customer services. 

Volunteering in this capacity allows you to assist students in enhancing their data science skills and building confidence in participating in related activities. This also provides you with the opportunity to refine your skills as you teach fellow students. It also underscores your commitment to community service, demonstrating a passion for sharing knowledge and contributing to the educational growth of others.

6. Contribute to Open-Source projects

In the realm of open source, data science projects are freely accessible for individuals to modify and share. This presents a significant growth opportunity for those seeking to refine their data science abilities. 

For instance, Cookiecutter Data Science is a standardized yet flexible project structure for doing and sharing data science work. Project Zamba is a Python-based tool that uses machine learning and computer vision to detect and classify animals in camera trap videos. Concept to Clinic is an open-source project that allows radiologists to better interact with state-of-the-art AI as part of their diagnostic process. Each of these accepts contributions on their website. 

As a high school student, involvement in different projects allows you to showcase your coding proficiency, problem-solving aptitude, and teamwork skills. Each project becomes a visible representation of your expertise and passion in the field of data science, bolstering your credibility as a prospective data scientist. Contributing to open-source projects in data science often requires a high level of proficiency, which may pose a challenge for beginners.

7. Conduct a data science research project 

Certain internships and summer programs on data science can be research-based enabling you to delve into a preferred topic supervised by a mentor. Some research programs you should consider are: 

• Veritas AI: The AI AI Fellowship with Publication & Showcase program is 1-1 mentorship program where you create a unique, individual data science/AI project on a topic of your choice under the guidance of expert mentors! You can opt to write a research paper which can then be sent to journals for publication! 

• Lumiere Education: Through this program, you can work 1–1 with a PhD data science researcher to explore your chosen topic. This is a great option for students interested in interdisciplinary research. Here is our review of the program! 

• MIT’s Research Science Institute: Through this program, you get a chance to engage in STEM classes and work on an independent research project under the guidance of researchers and scientists. You can read more about the program here! 

Engaging in data science research projects allows you to delve into specific interests, exploring advanced concepts not covered in your regular school curriculum. The mentorship from experts and researchers provides valuable opportunities to expand your knowledge, explore new topics, and engage in diverse research methodologies. 

8. Attend data science conferences

Conferences are opportunities to meet like-minded peers and professionals, find valuable research topics, gain industry insights, and more. They take place all year round and can also be free to attend. 

Some like the Radar: The Analytics Edition Conference (March 21), are even online! Other options include the Women in Data Science (WiDS) Stanford Conference (March 8) which includes a datathon, podcast, workshops, and on-site virtual conferences, and The Data Science Conference (May 23) which will showcase speakers on Speech Models, Computer Vision, Privacy and Governance, and other data topics and case studies.

Attending data science conferences allows you to network with professionals, gain exposure to diverse perspectives, and spark inspiration for future academic or career pursuits. Additionally, experiencing firsthand presentations and discussions enhances your understanding of the dynamic and interdisciplinary nature of data science, contributing to a well-rounded understanding of the field.

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