This course introduces Business Analytics and its application to business problems in functional domains including Operations, Marketing, Management, etc. Students learn contemporary tools, models, and methods for developing and deploying descriptive, predictive, and prescriptive analytics approaches to solving such problems. This core course enables future in-depth knowledge of Business Analytics in advanced classes.

This course introduces Business Analytics and its application to business problems in functional domains including Operations, Marketing, Management, etc. Students learn contemporary tools, models, and methods for developing and deploying descriptive, predictive, and prescriptive analytics approaches to solving such problems. This core course enables future in-depth knowledge of Business Analytics in advanced classes.

This course introduces students to the principles, tools and best practices of business intelligence and data visualization to enable the analysis of business data to improve decisions and performance. The course provides students with hands-on experience using appropriate software to create visual displays of quantitative data - in reports, graphs, charts, maps, stories and dashboards -- to facilitate communication of actionable insights for managerial decision-making.

This course introduces students to the principles, tools and best practices of business intelligence and data visualization to enable the analysis of business data to improve decisions and performance. The course provides students with hands-on experience using appropriate software to create visual displays of quantitative data - in reports, graphs, charts, maps, stories and dashboards -- to facilitate communication of actionable insights for managerial decision-making.

This course introduces "big data," data harvesting and cleaning, relational databases, structured query language (SQL), Data Warehousing, and online analytical processing (OLAP). The course emphasizes data structure, database design, data queries and data manipulation.

This individually customized course enables the student to independently study a topic of personal interest that is not ordinarily covered in the standard curriculum. The student confers with a willing qualified instructor to design the course including the course scope, learning methods, timetable, milestones, deliverables, and evaluation metrics. Typically, a final written report or paper irequried. To ensure progress, the student stays in contact with the instructor throughout the course of independent study.

Data mining is concerned with tools and techniques to numerically and visually explore vast data sets, classify data, predict outcomes, and identify associations, patterns, and exceptional events. Such capabilities enable firms to - for example -- better segment markets, evaluate and classify stocks, identify prospective customers, predict contingencies and catastrophes, identify defaulters and fraudulent transactions, measure churn, identify threats, perform service requests, and bundle goods and services. Such capabilities are critical in global, competitive business settings.

This case-based course examines the role of analytics in the development and execution of business strategy. The examination includes applying the principles, tools, and techniques for data-driven business decision- making within business domains. Students will learn to critically analyze data for its use in achieving strategic goals.

This case-based course examines the role of analytics in the development and execution of business strategy. The examination includes applying the principles, tools, and techniques for data-driven business decision- making within business domains. Students will learn to critically analyze data for its use in achieving strategic goals.

This course is an introduction to the use and application of modern analytical modeling for supporting decision making in business. It explores the use of analytical models and technologies including descriptive modeling, data mining, machine learning, and predictive modeling. It also examines a variety of topics in applied regression analysis, including linear and complex models. Emphasis is on business applications of analytical models.