0 or 3 credits
Spring 2026 Laboratory Lecture Upper Division
This course provides an integrated view of the key concepts of modern algorithmic data analytics. It focuses on teaching principles and methods needed to analyze large datasets in order to extract novel, transformative insights for the underlying application. The course emphasizes the duality between formulating questions that can be answered by statistical data analysis tools (the statistical perspective) and the algorithmic challenge of actually extracting such answers using available parallel and distributed computational resources from massive datasets. The topics cover three areas: (1) algorithmic concepts necessary for big data analytics, (2) bid data systems, including data management and programming, and (3) advanced analytic methods to address characteristics of real-world big data problems.
Learning Outcomes1Identify techniques appropriate for solving a data analytics challenge.
2Load and manipulate data in distributed computing environments.
3Apply computing paradigms to effectively write efficient and scalable algorithms for data analysis pipelines. Know limitations, design details and design decisions of these algorithms.
4Apply mixed-precision optimization and parallel learning algorithms to modern machine learning algorithms.
5Present results in methods appropriate for domain experts.
Prerequisites
Restrictions
made by Purdue students.