This course introduces students to modern data-driven decision making with an emphasis on the essential roles of business analytics and artificial intelligence (AI). Students examine enterprise data and analytics infrastructure, analytics approaches from descriptive to generative, and the use of AI to support analysis and decision making. Through hands-on exercises using R, Python, and real-world business cases, students develop foundational AI and analytics literacy, interpretation skills, and responsible judgment for AI-enabled business contexts.

QM 17400 AI & Decision Analytics

AI & Decision Analytics

Introduction to business statistics as related to facilitating managerial decision making. Topics include descriptive statistics, probability models, estimation, hypothesis testing, and regression analysis. Students use software to do their own analyses.

QM 30500 Business Statistics

Business Statistics

Use of optimization, simulation, and decision theory models to support management decision making. Emphasis on modeling and interpreting results for managerial applications of linear and integer programming models, network problems, simulation models, and decision analysis. Computer applications are stressed.

QM 30600 Management Science

Management Science

In this course, you will explore and learn how artificial intelligence empowers the development of data-driven solutions that support business decision-making. You will learn to integrate AI tools, programming environments, platforms, and analytical techniques to design, build, and deploy intelligent solutions for real organizational challenges. Emphasizing experimentation, teamwork, and creative problem-solving, the course equips students with the technical and foundational analytical skills needed to transform data into actionable insights and to thrive in AI-enhanced decision environments.

QM 38900 Using AI For Business Analytics

Using AI For Business Analytics

The course is an undergraduate elective offered as part of the Center for Manufacturing Management Enterprises (CMME) quality initiatives. Provides an overview of various tools and methods for total quality management.

QM 40500 Six Sigma And Quality Analytics

Six Sigma And Quality Analytics

This course covers up-to-date and practical spreadsheet modeling tools that can be applied to a wide variety of business problems from finance, marketing, and operations. The topical coverage consists of the following five modules: (1) Excel functions, skills and financial models; (2) deterministic and stochastic optimization techniques to determine the best managerial actions under internally-and/or externally-imposed constraints; (3) probability distribution fitting techniques to find the most likely description of the uncertainty in future business; (4) simulation modeling techniques to discover and analyze the risk and uncertainties in business environment and processes; (5) application of spreadsheet modeling and simulation techniques in forecasting asset dynamics( stock price) and pricing options and real investment opportunities. This course provides hands-on experience of computer application using Microsoft Excel and the spreadsheet add-ins @RISK, RISKOptimizer, SimQuick, etc.

QM 47200 Advanced Spreadsheet Modeling And Simulation

Advanced Spreadsheet Modeling And Simulation

Students follow a structured analytical process using popular industry tools (e.g., RStudio, Tableau, SQL) to identify, visualize, and summarize relationships within large data sets to support business problems. More focus is on descriptive analytics which includes business segmentation and clustering methods, but also introduces two predictive analytic methods, decision trees, and neural networks.

QM 47300 Data Mining

Data Mining

Students build and evaluate predictive models to support regression-type and classification-type business problems using popular industry tools (e.g., RStudio, SAS Enterprise Miner). Methods covered include multiple linear regression, ridge regression, lasso, CART, logistic regression, LDA/QDA, SVM, and ensemble methods such as random forests.

QM 47400 Predictive Analytics

Predictive Analytics

This course concerns the latest techniques in machine learning, focusing on deep learning and reinforcement learning, with applications to computer vision, natural language understanding, speech recognition, news recommendation, and others. The course plans to cover Neural Nets, Backpropagation, Convolutional neural network, Recurrent Networks, Autoencoders and other architectures in Deep Learning in the rst half. Next, the course plans to cover multi-armed bandits, Contextual bandits, Markov decision process, and Policy gradient methods in Reinforcement Learning.

QM 47500 Machine Learning For Business

Machine Learning For Business

Most degree programs offer students an opportunity to apply the knowledge learned from their studies in an applicable area via a thesis, a research project, or capstone. This course provides such an opportunity and is designed specifically for Business Analytics & Information Management undergraduate students that will pursue analytical-type careers (e.g., Data/OR Analyst, Data Scientist, Decision Scientist, Consultant, Developer, etc.). This course is designed to polish and integrate the knowledge, skills, and abilities you have developed from your coursework by successfully developing an analytics solution with an industry partner in a structured fashion. Thus, most of your time will be devoted to working with your project teammates to provide the answers and deliverables specified by the partner over the course of many sprints. Every project team will be provided with a project description, data dictionary, and access to the dataset (or directions in where to find the necessary data), as well as an NDA to read, understand, sign, and return to your professor before you can gain access to the data. These are tasks that might be required of you to perform when you work on a new project or have a new client. Students are expected to be familiar with at least one programming language (e.g., Python, R).

QM 47800 Experiential Project In Analytics

Experiential Project In Analytics

Covers up-to-date and practical spreadsheet modeling tools, which can be applied to a wide variety of business problems in finance, marketing, and operations. Consists of simulation modeling techniques to analyze risk and uncertainties in business environment, optimization techniques to determine the best managerial actions under internally- and/or externally-imposed constraints, and real-world examples and cases to demonstrate broad applications of spreadsheet modeling and simulations in manufacturing and service operations, supply chain systems, yield management, asset dynamics, option pricing, etc.

QM 57000 Spreadsheet Modeling And Simulation

Spreadsheet Modeling And Simulation

Introduces the concepts, techniques, tools, and applications of data mining. The material is approached from the perspective of a business analyst, with an emphasis on supporting tactical and strategic decisions. Includes a variety of techniques to identify nuggets of information or decision-making knowledge in bodies of data, and extracting these in such a way that they can be put to use in the area, such as decision support, prediction, forecasting, and estimation.

QM 57100 Data Mining

The course emphasizes applications of optimization through cases and computer exercises. The applications are chosen to provide insights into business and economics. Areas covered include linear, network, integer, and nonlinear optimization. At the end of the course, the students should have the ability to model optimization problems work with software to solve optimization problems relate to optimization theory in a variety of application settings develop optimization insights into applications in marketing, finance, and operations and get some basic exposure to EXCEL automation. Prerequisites: Students are expected to have taken a basic MBA (core) course in business analytics (such as the masters core MGMT 67000).

QM 57300 Optimization Modeling With Spreadsheets

Optimization Modeling With Spreadsheets

Graduate-level variable-title course focusing on advanced analytics and quantitative modeling. Emphasis is placed on synthesis of methods and strategic application of data analysis. Permission of instructor required.

QM 59000 Reinforcement Learning And LLM

Reinforcement Learning And LLM

QM 59000 Machine Learning

Machine Learning

QM 59000 Webstars Strategy

Webstars Strategy

QM 59000 Text-To-SQL BI

Text-To-SQL BI

QM 59000 Terracycle Analytics

Terracycle Analytics

QM 59000 EON Reality Analytics Research

EON Reality Analytics Research

QM 59000 Graduate BA Project

Graduate BA Project

Introduction to quantitative decision procedures under uncertainty. Applications of descriptive statistics, probability models, simulation models, interval estimates, and hypothesis testing to management problems. Managerial-oriented cases are used in instruction. Prerequisite: Master's student standing and Management majors only.

QM 67000 Business Analytics

Business Analytics

Advanced graduate-level variable-title course emphasizing research and advanced analytical methods. Topics may include cutting-edge analytics, modeling, or quantitative research applications. Permission of instructor required.

QM 69000 Workshop In Analytics

Workshop In Analytics

QM 69000 Topics In PhD

Topics In PhD

This course explores the design, development, and governance of agentic AI within business analytics for decision making. Students will examine the architectural and functional foundations of modern AI agents, including large language model (LLM)-based reasoning, tool use, retrieval-augmented generation (RAG), multi-agent design and orchestration, policy compliance and guardrails, and human-AI integration. Through hands-on projects, students will learn to build, evaluate, and validate agentic workflows for analytical tasks such as data collection, exploration, modeling, inference, prediction, explanation, report generation, and decision making. The course also critically addresses the organizational, ethical, and risk-management implications of deploying autonomous AI agents in enterprise settings. By the end of the course, students will be prepared to assess where agentic AI creates genuine business value, architect agent-based solutions, and establish the guardrails needed for responsible deployment.

QM Courses

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QM Courses

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An unofficial catalog for Purdue courses made by Purdue students.GitHub·Maintained byBoilerexams··Join the team