MSIA Course Descriptions and Syllabus Archive

IAF 601: Introduction to Analytics

Course Description: Managing, manipulating, and analyzing structured/unstructured data to understand relationships and generate useful insights. Principles such as programming for analytics, data visualization, statistical modeling, database design, high performance computing are discussed.
Download Fall 2019 Syllabus

IAF 602: Statistical Methods for Data Analytics

Course Description: This course introduces fundamental statistical techniques for data analytics such as hypothesis testing, data transformation, estimation, confidence intervals, regressions models, ANOVA, multivariate analysis, non-parametric methods, and design of experiments.
Download Fall 2019 Syllabus

IAF 603: Preparing Data for Analytics

Course Description: Students are exposed to current approaches, techniques and best practices for collecting, cleaning and normalizing data, processing, storing, managing, securing and preparing structured and unstructured big data sets for analytics.
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IAF 604: Machine Learning and Predictive Analytics

Course Description: This course is an introduction to machine learning and predictive analytics for Big Data. Some key components include deep learning, supervised, unsupervised models, regression, inductive learning, and time series analysis.
Download Spring 2020 Syllabus

IAF 605: Data Visualization

Course Description: Data are analyzed to answer questions. Students are exposed to concepts and techniques to understand analytics results and appropriately infer relationships to answer questions and visualize results using contemporary techniques.
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IAF 606: Solving Problems with Data Analytics

Course Description: This course addresses how data analytics is used to solve applied problems in varied contexts. Students will learn how to choose appropriate methodologies, manage data, conduct analyses and report results.
Download Spring 2020 Syllabus