OPIM 5604 – Predictive Modeling (Master’s Level, 2022)

Introduces the techniques of predictive modeling in a data-rich business environment. Covers the process of formulating business objectives, data selection, preparation, and partition to successfully design, build, evaluate and implement predictive models for a variety of practical business applications. Predictive models such as neural networks, decision trees, Bayesian classification, and others will be studied. The course emphasizes the relationship of each step to a company’s specific business needs, goals and objectives. The focus on the business goal highlights how the process is both powerful and practical.

OPIM 5671 – Data Mining and Business Intelligence (Master’s Level, 2016 – 2022)

Discusses data mining techniques that can be utilized to effectively sift through large volumes of operational data and extract actionable information and knowledge (meaningful patterns, trends, and anomalies) to help optimize businesses and significantly improve bottom lines. The course is practically oriented with a focus of applying various data analytical techniques in various business domains such as customer profiling and segmentation, database marketing, credit rating, fraud detection, click-stream Web mining, and component failure predictions.

OPIM 6201 – Research Methods for Operations and Information Management (PhD level, 2021)

This course aims at preparing PhD students for empirical research. The course discusses the objectives and key challenges of empirical research, as well as how to address those challenges. The topics include a wide range of econometric methods, such as linear regression, instrumental variable estimation, potential outcome framework, matching methods, panel data models, difference-in-differences, choice models, count models, survival models, and multi-stage models (e.g., sample selection and endogenous treatment models). When and how to use these methods will also be discussed.

OPIM 5512 – Data Science using Python (Master’s Level, 2017 – 2018)

Data science concepts using the Python programming language. Data wrangling and management using Pandas; visualization using MatPlotLib; fundamentals of matrix algebra and regression, with illustrations using Numpy; machine learning, focusing on fundamental concepts, classification, and information extraction.