Prerequisite: MET AD571 Work with large, complex datasets beyond traditional desktop analytics using Apache Spark (PySpark), DuckDB, Polars, SQL-based data warehousing, and AWS cloud services. You will explore SQL databases through AWS’ Relational Database Service (RDS), learn to build feature engineering pipelines, and scalable machine-learning workflows using Spark MLlib. Key topics include cloud computing parallel processing, management of massive data stores, cloud data architectures, web scraping, API-based data collection, text and web mining, and batch and streaming analytics. You will develop an end-to-end analytics solution from data ingestion and cleaning to modeling, evaluation, and communication, using Python (PySpark), SQL, Git, and GitHub in a cloud-ready AWS environment. By the end of the course, you will be able to design scalable analytics pipelines, manage cloud data environments, and apply distributed machine learning to real-world datasets. The course culminates in a term project implementing a complete big data and cloud analytics workflow.

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