Teenlife
    Link

    Want to learn more about taking a gap year? Check out our latest guide!

    UCLA Summer Sessions: Computer Science Summer Institute – Intermediate Track

    Details

    • Listing Type: Summer Programs
    • Program Delivery: Day
    • Provided By: College
    • Session Start: June
    • Session Length: Three Weeks
    • Entering Grade: 11th, 12th, PG
    • Category: STEM
    • Sub-Categories: Computer Science, Coding
    • Selective: No
    • Ages: 15, 16, 17, 18, 19
    • Minimum Cost: $3,000 - $6,999
    • Career Clusters: Science, Technology, Engineering, and Mathematics
    • Credit Awarded: No
    • Facebook
    • Instagram
    • X
    Write a Review

    Overview

    The Computer Science Intermediate Track provides a unique combination of coding boot camp, and lab touring experiences, as well as UCLA coursework covering critical concepts and skills in computer programming related to statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, health data, geographical data, and social networks.

    Computer science experience with basic programming skills (python) is required. Knowledge in basic matrix analysis, probability, and statistics is preferred.

    The fundamental question this course aims to address is how does one analyze real-world data so as to understand the corresponding phenomenon.? Students will learn critical concepts and skills in computer programming related to statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, health data, geographical data, and social networks.?  Planned topics include machine learning, data analytics, and statistical modeling classically employed for prediction. The program will be a blend of theoretical and practical instruction, providing a comprehensive, hands-on overview of the Data Science domain. 

    Hands-on projects will form the bulk of the work for the class and will seek to teach students the data science lifecycle: data selection and cleaning, feature engineering, model selection, and prediction methodologies.