About This Course
Introduction to data science. Design strategies for business analytics: statistics for machine learning, core data mining models, data pipeline, visualization. Hands-on labs with data mining, statistics, and in-memory analytics software.
Sample course topics: Data science, statistics for machine learning, linear regression, classification, model features, ensemble models, textual and graph analysis, deep learning, and machine learning platform.
Sample textbooks: Data Mining: Examples and Case Studies by Yanchang Zhao, and Programming in Python by Wes McKinney
MS, computer science, University of Minnesota; ME and BS, electrical engineering, Cornell University
Katherine Splett works in the health care industry as a metric architect, where she has created innovative in-memory analytics, data models, and dashboards for readmissions, care transition, care coordination, financial, and communications. She has over 25 years of experience developing computer systems as project lead, metric architect, system architect, database architect, application developer, business intelligence analyst, ETL developer, and electrical engineer in financial, identity, and government industries. Her technical interests are data science, business intelligence, databases, and design and development of data architectures. She has been an adjunct ITI instructor for 16 years and has also served as faculty director.
- INet 4061 - Data Science I: Fundamentals
- INet 4710 - Data Science II: Big Data and Analytics