Massive Data Fundamentals

3 Credits · Level: Graduate, Undergraduate

Massive Data Fundamentals equips students to analyze datasets that exceed the capabilities of traditional computing tools. In this hands-on course, students master cloud computing, distributed systems, and the big data technologies essential for understanding modern data challenges in public policy. Students build practical skills with industry-standard tools including SQL, Apache Spark, Databricks, graph databases, and Snowflake, using Python as the primary programming language. The curriculum progresses through three phases: cloud infrastructure fundamentals, SQL and data engineering, and distributed computing. Students also navigate the evolving landscape of data governance, exploring privacy laws, security protocols, and disaster recovery planning. The course combines project-based learning with analyses of big data history and discussions of privacy and data governance issues. Students develop end-to-end data capabilities, using cloud platforms to ingest, transform, analyze, and present data, creating portfolio-ready work that demonstrates both technical expertise and policy insight. Prerequisites: DSAN 5000 or equivalent, working knowledge of Python and the Unix command line. Students need to own a laptop computer with at least 100GB of free disk space and 8GB of RAM.