THE PROGRAM
The following three tracks were offered for the most recent FutureMakers program. We placed each accepted applicant in one of these tracks. During the application process, participants had the option to rank their preferred track(s). No prior coding experience was necessary except for 1 year of Python for the deep learning track.
Code and debug mobile apps using MIT App Inventor and use web-based UX design tools. Learn about machine learning, computer vision, Internet of Things, genAI, and their societal implications. Participate in design jams and apply computational action to create your final project.
No prior coding experience required.
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Learn about machine learning through hands-on examples in affective computing, computer vision, and natural language processing. Use ML frameworks such as TensorFlow and explore Kaggle challenges using publicly available datasets. Learn industry tools and libraries such as Python, Numpy, Github, and Google Colab.
At least 1 year of Python experience required.
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Learn data science tools and techniques and apply them to a critical participatory action research project. Learn data analysis and data visualization using Python, Pandas, and Google Sheets. Student teams work with a non-profit to analyze real-world data for advocacy.
No prior coding experience required.
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EARN A CERTIFICATE OF COMPLETION
Participants finish the program with a certificate of completion, practical technical skills, a compelling project (some with GitHub code), an enhanced resume, and improved interview skills that will help them secure their next summer internship, apply to college, or even make progress toward their own startup idea.
INSPIRING PROJECTS
The last two weeks culminates in a create-a-thon where teams develop and pitch a solution to judges who provide feedback and award prizes!
Check out the pitch from the Deep Learning Track winning entry from Team Kinex. This could be you!
Mobile App Development Track
Team Code & Chaos won the Mobile App Development track in 2025 with “Hand2Hand”, addressing food insecurity by connecting volunteers, transportation, and food sources.
Deep Learning Track
Data in Action Track
In 2024, student teams worked with non-profits in the Data in Action track to analyze data for advocacy. For instance, they worked with AfroPink to detect breast cancer early within the African-American community and with the Vera Institute of Justice to explore how systemic inequities contribute to high school dropout rates in Baltimore City.
I am no stranger to judging and it was amazing to see what I saw today. It compares to a lot more experienced teams and considering the time you were able to put into it, it's a big achievement...reflect on what you have learned. Mayb it's not just the information about how to use certain technologies. I'm sure there have been a lot of things that you have been able to achieve by understanding how to work with each other, how to communicate in challenging situations, resolve conflicts, and look at things creatively.