Data Engineering

Catalog description

This course introduces students to the objectives and techniques of data engineering as a critical step in the effective application of machine learning and other data analysis techniques. The course will include data selection and profiling to improve the nature and understanding of the problem space; transformation and aggregation to cleanse and consolidate data; and feature engineering to expose critical characteristics for downstream analytics. Through instructor-provided programming assignments and self-designed projects, by the end of this course students will be able to implement data processing pipelines that transform data from their raw format into datasets useful for analysis. AI, Machine Learning, and Data Science are hot topics and seen (rightly and wrongly) as a fast path to new insight and innovation across all industries. One of biggest challenges we face in delivering on that promise is the connection between business context, available data, and analysis techniques (e.g., ML).