Introduction to Data Science

Catalog description

Introduces algorithms and techniques used in the organization and analysis of large amounts of data. Covers the extraction, cleansing, and preparation of large data sets, and their analysis using basic statistical principles of supervised and unsupervised machine learning, including clustering, feed-forward and recurrent neural networks, and top-down induction of decision trees. Covers techniques for testing and validating the accuracy of machine learning models. Discusses the use of machine learning models in classification, data mining and exploration, summarization, and prediction. Prerequisites: CIS 277A , STAT 243Z . Audit available.