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University at Buffalo

UB Graduate Academic Schedule: Fall 2020

This information is updated nightly. Additional information about this course, including real-time course data, prerequisite and corequisite information, is available to current students via the HUB Student Center, which is accessible via MyUB.


IE 500LEC - Special Topics-Data Analytics & Predictive Mo
Special Topics-Data Analytics & Predictive Mo 1 Enrollment Information (not real time - data refreshed nightly)
Class #:   22436   Enrollment Capacity:   80
Section:   1   Enrollment Total:   9
Credits:   3.00 credits   Seats Available:   71
Dates:   08/31/2020 - 12/11/2020   Status:   OPEN WITH RESERVES
Days, Time:   T R , 5:30 PM - 6:45 PM
Room:   Remote view map
Location:   Remote      
Reserve Capacities
Description Enrollment Capacity Enrollment Total  
Ind Eng GRAD: Seats Reserved 80 7  
Course Description Data analytics is the use of computational statistics and data mining to draw insights and build predictive models based on large data sets. As data becomes more prevalent across many different areas of importance in engineering, policy analysis, and management, analytics is becoming an increasingly important topic. This course assumes a working knowledge of regression and statistics and builds from this to introduce modern data analytics. The course covers fundamental concepts of predictive modeling and major classes of methods beyond linear regression, including additive models, tree-based models, Bayesian networks, random forests, multi-level models, boosting, bagging, and model averaging. The course focuses on the application and interpretation of the methods while also providing an understanding of the underlying basis and theory behind them. Lab sessions, the midterm, and the term project are primarily data-driven analytics exercises. In-class quizzes will be used to test the basic concepts and theories. Opportunities will be given to students to work on projects of their own interest, provided they are relevant and aligned with the learning outcomes of the course. R programming language will be used for this course. Prerequisites: Familiarity with computer programming, basic knowledge of probability and statistics Course Objectives After taking this class students should be able to: ? Comfortably use the R programming language ? ? Develop a logical statistical model that is reasonable for problems faced in research and practice. ? Use a variety of parametric, semi-parametric, and non-parametric methods to?model a given dataset. ? Understand the underlying information in a dataset and be able to draw inferences from the model
  Course Description
This course is dual-listed with IE 459.
             Mukherjee, S look up    
  On-line Resources
Other Courses Taught By: Mukherjee, S