3 credits
Fall 2026 Lecture Upper Division
Cloud computing and big data technologies are rapidly enhancing an organization's business intelligence ecosystem. Two modules of the course are specially designed for students to gain valuable hands-on experience in collecting, cleaning, formatting, integrating, and storing massive amounts of data that may be structured or unstructured, archived, or streaming in a cloud platform. The first module will introduce cloud computing fundamentals, its enabling technologies, main building blocks, and hands-on experience through projects utilizing Google Cloud Platform (GCP). The second module will cover processes for creating data pipelines in GCP so that the student will be able to curate big data for training, analysis, and prediction using AI/ML and other data science techniques.
Learning Outcomes1Configure rapidly GCP to meet any analytics related challenge.
2Build Data Lakes and Data Warehouses for big data analytics.
3Use BigQuery and related services for advanced, real-time analytics.
4Use BigQuery ML for classification and regression.
5Build complex data pipelines using GCP's Dataflow, Cloud pub/sub, and Cloud Function to prepare data for machine learning algorithms.
Prerequisites
Restrictions
made by Purdue students.