Data Analytics and Applied Statistics

Data Analytics and Applied Statistics Program Area

Learn the essential methods used to translate raw data into informed decisions for a wide range of industry applications. Develop the skills and knowledge to collect, manage, and analyze data to create mathematical and statistical models for inference, prediction, machine learning, and data-driven decision-making to improve the performance of complex systems.

Key Topics: Data-driven Optimization and Decision Making, Design of Experiments, Machine Learning, Predictive Modeling, Risk Analysis, Simulation, Uncertainty Quantification

Area Lead: Eunshin Byon

Foundation Courses

IOE 473 Advanced Data Analytics

Advisory prerequisites: IOE 310, IOE 366, or IOE 373. (3 credits)

This course focuses on fundamental computational methods in data analytics with case studies from real-world applications. The goal is to expose students to a variety of data analytics methods and then demonstrate the applicability of these methods through a diverse set of real-world problems.

STATS 570 (IOE 570) Design of Experiments

Advisory prerequisites: Stats 500 or background in regression. (3 credits) 

Basic design principles, review of analysis of variance, block designs, two-level and three-level factorial and fractional factorial experiments, designs with complex aliasing, data analysis techniques and case studies, basic response surface methodology, variation reduction, and introductory robust parameter designs.

IOE 591 Statistical Learning for Data Science

Advisory prerequisites: Math 214 or IOE 366. (3 credits)

This course is an introductory graduate course on data analytics. The course introduces fundamental theories and methods for regression analysis and applications. Topics include multiple regression models, generalized linear models, and nonparametric regression models. Concepts of estimation, inference, diagnostics, transformation, regularization, variable selection, and cross-validation are studied. Students have opportunities to formulate statistical models developed from case studies and to use various computer programs.

Suggested courses to further explore the data analytics and applied statistics program area

Statistical and machine learning

IOE 466 Statistical Quality Control

IOE 551 Benchmarking, Productivity Analysis, and Performance 

IOE 561 Risk Analysis I

IOE 565 Time Series Analysis

IOE 568 Statistical Learning and Applications in Quality Engineering

IOE 691 Modern Bayesian Data Science

STATS 503 Statistical Learning II: Multivariate Analysis

STATS 600 Linear Models

EECS 545 Machine Learning (CSE) or EECS 553 Machine Learning (ECE) 

EECS 505 Computational Data Science and Machine Learning


IOE 510 Linear Programming I

IOE 511 Continuous Optimization Methods

IOE 512 Dynamic Programming

Stochastic systems

IOE 515 Stochastic Processes I

IOE 516 Stochastic Processes II

IOE 545 Stochastic Networks and Operations

IOE 574 Simulation

Applications-oriented classes

IOE 517 Game Theory and Operations Applications

IOE 525 Lean Principles and Scientific Thinking in Organizations

IOE 543 Scheduling

IOE 552 Financial Engineering I

IOE 553 Financial Engineering II