An introduction to basic data science workflow following current best practices. This course will introduce students to computational or algorithmic ways to think about and learn from data. Emphasis will be placed on data visualization, exploratory data analysis, and foundational modeling principles and techniques implemented using an appropriate programming language.

This course provides a concise overview of certain mathematical methods that are essential in data science. The primary methods to be covered should come from probability and statistics, networks and graph theory, and optimization. Additional data science relevant topics may be covered at the discretion of the instructor.

This course covers the process of knowledge discovery including data selection, pre-processing, transformation, data mining, evaluation, and validation, with an emphasis on data mining concepts, algorithms, and techniques for common tasks such as association rule learning, classification, regression, clustering, and outlier detection.