UConn Pre-College Summer: Data Science

    UConn Pre-College Summer: Data Science


    • Listing Type: Summer Programs
    • Destination: United States
    • Program Delivery: Residential
    • Provided By: College
    • Session Start: July
    • Session Length: One Week
    • Entering Grade: 10th, 11th, 12th
    • Gender: Coed
    • Category: STEM
    • Sub-Categories: Mathematics
    • Selective: No
    • Ages: 14, 15, 16, 17, 18
    • Minimum Cost: $1,500 - $2,999
    • Credit Awarded: No
    • Call: (860) 486-0149
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    We have a summer in Storrs for you!

    New this year, UConn's Pre-College Summer Data Science course provides high school students the opportunity to dive into the ever-expanding world of data science at a nationally ranked public university campus.

    Where will your future take you?

    That’s a hard question to answer, especially for high school students. UConn’s Pre-College Summer can help students figure out what path is right for them. Offered during Session 4, our Data Sciencecourse combines several disciplines, including statistics, data analysis, machine learning, and computer science. With the evolution of technology in the 21st century and the rapid increase of demand for these skills, the field of Data Science is growing at an extraordinary rate.

    Imagine that you are the Chief Data Scientist of an online movie store. Your task is to recommend movies for your customers to watch based on collected rating data from other users. This task is similar to what Amazon data scientists do behind the scenes at IMDb, the world's most popular and authoritative source for all things cinema. IMDb, an Amazon company, uses components of data science and uses data rating to recommend movies to users. So, what are you going to do to drive revenue for an online movie enterprise?

    The Data Science session will offer an introduction to some techniques for recommendation systems used in the field of Data Science. Students will learn how to program using Python, review basic linear algebra and probability, build models and websites that give recommendations based on given rating inputs, and learn how to utilize selected recommendation system techniques such as user-based and item-based collaborative filtering and matrix factorization. After completing the course, students will be able to understand and utilize the same algorithms that have been applied for recommending various products in business world, as well.

    Tentative Schedule:

    • Monday: Python Programming Fundamentals
    • Tuesday: Brief review on Linear Algebra and Probability
    • Wednesday and Thursday: Recommendation Systems, Model building, and Web design and development
    • Friday: Students complete their projects and showcase work.

    Course Prerequisites:

    Some exposure to linear algebra (basic knowledge about vectors, matrices, and operations) and Python programming will be helpful in taking this course, but is not necessary. 

    For information on how UConn is handling the COVID-19 pandemic, visit our website!