Pre-requisites: Python basics (e.g. IS717, IS834, QM877 (Python Bootcamp) or equivalent); Some prior experience with analytics (e.g. QSTIS 823, QSTIS 833, QSTIS 834, QSTIS 841, QSTMK 842, QSTMK 872, QSTMK 876); or permission of the instructor. - Every company is a ¿data company,¿ possessing vast quantities of data from operations, customers, products, and transactions. With big data comes significant challenges requiring specific infrastructure and skills. The analytics process, including deploying and using big data tools, is essential for organizations to improve efficiency, drive new revenue streams, and gain a competitive edge. This course addresses these challenges, discusses methods to overcome them, and common pitfalls in implementation and unnecessary analysis. Data analytics involves exploring, discovering, interpreting, and communicating meaningful patterns, whereas big data analytics focuses on analyzing data on a larger scale, where a single computer cannot process it timely. Distributed computation, the foundation of big data analytics, involves a network of computers processing data segments. This course teaches students to perform statistical data analysis of large datasets using distributed computation and introduces machine learning techniques and libraries that handle big data. Basic programming in python, and basic analytics are prerequisite.

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