Big Data Summer Camp
Hands-On Project-Based Camp for High School Students
Big Data Day Camp
Automating Internet of Things (IoT) with Machine Learning on AWS - January 14-15, 2020
Amazon Web Services
Seminar Series

Details & Requirements

Applicants must begin their sophomore year in high school in the Fall of the year they’re applying, and own a laptop they can bring every day.

Getting Here. We highly recommend that students be dropped off because parking at UCSD is very impacted and costly. Please email us far in advance if students have to drive themselves.


Two Sessions Schedule:
July 8 – July 12, 2019  OR
July 15 – July 19, 2019
Monday-Friday, 9:00am-3:00pm


$995 Early Bird (deadline May 31)
$1,095 Regular

The deadline for submitting a financial aid Scholarship Application has passed


For additional questions contact

Big Data Summer Camp for High School Students

The UCSD’s Qualcomm Institute’s Big Data Summer Camp provides aspiring young data scientists an immersive educational experience using cutting-edge approaches to Big Data.  This pre-college Data Science Academy is open to high school students preparing to enter their sophomore, junior or senior year of high school, This week-long summer camp is designed for students with interests in data analytics and visualization, artificial intelligence and machine learning, and how these fields and approaches assist in discovering and evaluating predictive models.  The one-week long Data Science Academy is a hands-on, project-based summer camp that will introduce participants to the use of data and tools related to Big Data ecosystem.

In the Big Data Summer Camp for High School Students, participants will learn the basic math, visualization and methods behind data science and will work on team projects with classmates using real life examples. Over the course of the week, the content will build in complexity and difficulty, and will include strategies for data visualization and model development and evaluation. No programming experience or knowledge is required. Additionally, invited guest from academia and industry will give presentations to expose the students to various data science application domains.


9:00 AM – Welcome and Introduction
9:30 AM – Data Modeling in the Age of Big Data
10:00 AM – Data Science Industry Landscape: Who Does What?
11:00 AM – Intro to Big Data Concepts, Tools and Technologies
12:00 PM – Lunch Break
1:00 PM – Analytics Platform Introduction and Tutorial
2:00 PM – Hands-on Exercises Data Load and Manipulation

9:00 AM – Intro to Basic Machine Learning Concepts and Methods
10:00 AM – Practical Approach to Data Science
10:30 AM – Hands-on Data Preparation and Cleaning
11:00 AM – Hands-on Practical Data Visualization
12:00 PM – Lunch Break
1:00 PM – Introduction to Basic Machine Learning Algorithms
2:00 PM – Basic Algorithms: K-Nearest Neighbor & Hands-on

9:00 AM – Intro to Supervised Learning: Decision Trees and Regression Trees
10:00 AM – Decision Trees Hands-on
11:00 AM – Regression Trees Hands-on
12:00 PM – Lunch Break
1:00 PM – Market Basket Analysis and Recommendation Engines (Lecture + Hands-on)
2:00 PM – Industry Guest Speaker

9:00 AM – Unsupervised Learning: Clustering Methods
10:00 AM – Hands-on Clustering
11:00 AM – QI Tour
12:00 PM – Lunch Break
1:00 PM – Guest Speaker
2:00 PM – Model Evaluation and Validation

9:00 AM – Hands-on Project: Small Group Team work on the Case Study and Discussion
(Choice of numerous data sets including Yelp, Twitter, Google, Instagram, Airlines, Bike Sharing, Sales, Foods, Health, etc.)
2:00 PM – Final Discussion, Overview and Conclusion

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