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Explores data handling fundamentals, including models, sources, and wrangling, emphasizing the importance of understanding and addressing data problems.
Covers the fundamentals of scaling to massive data using Spark, focusing on RDDs, transformations, actions, Spark architecture, and Spark's machine learning toolkit.
Delves into the complementary methodologies of discrete choice and machine learning, covering notations, variables, models, data processes, extrapolation, what-if analysis, and more.
Explores the importance of causality for robust machine learning, covering ideal datasets, missing data problems, graphical models, and interference models.
