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University of Michigan School of Information


Big Data Analytics

The Big Data Analytics Mastery course prepares students to demonstrate mastery of data collection, processing, analysis, retrieval, mining, visualization, and prediction. You’ll synthesize methods from information retrieval, statistical data analysis, data mining, machine learning, and other big-data related fields.

UMSI Mastery courses are special types of courses that allow you to demonstrate synthesis of the major theories, methods, and approaches to inquiry and/or schools of practice necessary for entry into a particular career in the information professions.

Students work on semester-long projects that deal with industry-scale data sets and solve real-world problems. Aligned with best industry practices, you’ll work in a fast-paced, collaborative environment and to demonstrate independence and leadership. When you’re finished, you’ll be able to create and use tools to handle very large transactional, text, network, behavioral, and/or multimedia data sets.

Note: Current students should refer to their respective academic advising Canvas sites to ensure they are accessing the most up to date curricular information for their specific cohort.

Required courses:

SI 501: Contextual Inquiry and Consulting Foundations

SI 504: Servers, The Shell, and Git

SI 506: Programming I

SI 507: Intermediate Programming

SI 544: Introduction to Statistics and Data Analysis

SI 568: Intro to Applied Data Science

SI 602: Math Foundations

SI 618: Data Manipulation and Analysis

Plus one or more of:

SI 670: Applied Machine Learning

SI 671: Data Mining: Methods and Applications

Plus two or more of:

SI 608: Networks

SI 630: Natural Language Processing: Algorithms and People OR SI 561: Natural Language Processing

SI 649: Information Visualization

SI 650: Information Retrieval


SI 510: Information Ethics

SI 529: Online Communities 

SI 534: Theories of Social Influence

SI 563: Game Theory

SI 564: SQL and Databases

SI 617: Choice Architecture

SI 652: Incentives and Strategic Behavior in Computational Systems