Session I - DATA Analytics

When: July 10th to July 21st, 2023

Time: 9AM – 4PM (Mon-Fri)

Cost: $2500 (Includes all materials, access to a laptop, light refreshments, lunch, and program T-shirt. Fee waivers available based on financial need.

Eligibility: Rising high school students in grades 9th-12th and graduating 12th graders.

Prerequisites (recommended): Algebra II or Integrated Math II

Please visit the Agenda page for more information.

student collaborating

Course Descriptions:

This two-week camp (sponsored by the School of ICS at UCI) will explore the foundations of data science. The students will learn about data science methods and concepts via hands on projects. In particular, they become familiar with methods associated with data collection, data processing, data summarization and visualization, and statistical modeling approaches for prediction. Moreover, statistical inference techniques for testing scientific hypotheses are discussed. Lastly, students learn about statistical thinking for decision making under uncertainty. Students will apply the concepts communicated through the lectures by analyzing real data, using the statistical programming language R. Through teamwork and interactive learning, students will become involved with the processes related to scientific investigation and inquiry, with applications in biology and medical sciences, among others. 

By the end of the program, students will work on a capstone project based on a real-life scientific problem and will present their findings to a broader audience. Through the Donald Bren School of Information and Computer Sciences, DATA students will:

  • Engage in hands-on analytics projects, involving real life scientific data.
  • Learn how to formulate scientific questions and utilize statistical thought process to provide practical solutions to those problems.
  • Interact and engage with university faculty and college students and visit research labs in the School’s three departments.
  • Explore career pathways in data science and related fields.
  • Work in teams while emphasizing the essential skills required for scientific research, including creativity, communication, collaboration, and critical thinking; Projects will emphasize Science, Technology, Engineering and Mathematics (STEM) concepts and be correlated with Common Core Standards and the Framework for Next Generation Science Standards.
The topic I really liked was learning how to use R and relate it back to the statistics theory! It was very satisfying to take these abstract concepts and produce useful visualizations and statistics on our dataset.
Vincent
Student, Summer 2022

NEW! Session II - Artificial Intelligence & Machine Learning

When: July 24th – August 4th, 2023

Time: 9AM – 4PM (Mon-Fri)

Cost: $2500 (Includes all materials, access to a laptop, light refreshments, lunch, and program T-shirt. Fee waivers available based on financial need.

Eligibility: Rising high school students in grades 9th-12th and graduating 12th graders.

Prerequisites (recommended): Session I OR Completion of Statistics

Visit the Agenda page for more information.

Students listening in lecture

Course Descriptions:

This two week camp will explore the foundations of machine learning.

Students will learn about machine learning algorithms, methods, and concepts through both paper and programming approaches. In particular, they will be taught methods associated with classification, clustering, and regression testing. We will see early and modern applications of these techniques.

Students will practice using the concepts communicated through the lectures by analyzing real data, using the statistical programming language R. Lecture notes will be provided. Students will work in teams using a case study approach and will engage in the process of developing systems for such applications as handwriting recognition, detecting network intrusions, and advertising strategies.

By the end of the program, students will complete a team capstone project based on a real-life machine learning application and will present their findings to a broader audience at a closing symposium event.

Students will:

  • Engage in hands-on analytics projects, involving real life scientific data
  • Learn how to formulate machine learning models and use common tools to attempt to solve these
  • Explore career pathways in machine learning and related fields.