INET 4061

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 


Tyler Deutsch
Tyler Deutsch

MS, Data Science, Northwestern University; BS, Operations Management, University of Dayton

Tyler Deutsch is a data science leader at Procter & Gamble (P&G) where he leads teams of data scientists building marketing and retail models focused on the consumer. He has experience deploying machine learning and optimization models into highly scalable production cloud and big data architectures. His specialties include regression, classification, NLP, time series models, computer vision, optimization, and deep learning frameworks, leveraging the latest techniques in Python and R. Tyler is pleased to share with students his passion for consumer insights and enterprise scale algorithms.

INET 4061 – Data Science I: Fundamentals

Information Subject to Change

Course details, syllabus, and instructor are subject to change. Current course details can be found by clicking on the Term link(s) above.