INTRODUCTION TO BIG DATA SYSTEMS

Prerequisites & course information

Requisites: COMP SCI 320 , 400 , or Graduate/Professional Standing Course Designation: Core GenEd - Natural Science & Wellness Breadth - Natural Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Deploy distributed systems for data storage and analytics Audience: Undergraduate 2. Demonstrate competencies with tools and processes necessary for loading data into distributed storage systems Audience: Undergraduate 3. Write programs that use distributed platforms to efficiently analyze large datasets Audience: Undergraduate 4. Produce meaning from large datasets by training machine learning models in parallel or on distributed systems Audience: Undergraduate 5. Measure resource usage and overall cost of running distributed programs Audience: Undergraduate 6. Optimize distributed analytics programs to reduce resource consumption and program runtime Audience: Undergraduate 7. Demonstrate competencies with cloud services designed to store or analyze large datasets Audience: Undergraduate Learning Outcomes: 1. Deploy distributed systems for data storage and analytics Audience: Undergraduate 2. Demonstrate competencies with tools and processes necessary for loading data into distributed storage systems Audience: Undergraduate 3. Write programs that use distributed platforms to efficiently analyze large datasets Audience: Undergraduate 4. Produce meaning from large datasets by training machine learning models in parallel or on distributed systems Audience: Undergraduate 5. Measure resource usage and overall cost of running distributed programs Audience: Undergraduate 6. Optimize distributed analytics programs to reduce resource consumption and program runtime Audience: Undergraduate 7. Demonstrate competencies with cloud services designed to store or analyze large datasets Audience: Undergraduate