M.S. in Business Analytics
GB-517 Ethics in Business and Society
This course provides a framework for students to recognize ethical dilemmas and analyze the business implications in terms of consequences, autonomy, rights, virtues and equality. Extensive use is made case studies and current events using presentation, discussion and debate delivery methods.
GB-530 Managerial Finance and Decision Making
A study of the problems associated with the financial management of business organizations. Topics include the analysis of types of firms and markets, review of accounting, time value of money, valuation, and short-term financing.
GB-622 Managerial Economics
This course examines the foundation concepts for how organizations allocate resources for the production, distribution, and consumption of goods and services. Economic decisions are linked to the organization, management, and strategy involved with the conduct of operations. This course focuses on how managers can improve their understanding of the economic environment and its impact on the business firm.
DS-520 Data Analysis and Decision Modeling
This course will provide students with an understanding of common statistical techniques and methods used to analyze data in business. Topics covered include probability, sampling, estimation, hypothesis testing, linear regression, multivariate regression, logistic regression, analysis of variance, categorical data analysis, Bootstrap, permutation tests and nonparametric statistics. Students will learn to apply statistical techniques to the processing and interpretation of data from various industries and disciplines.
Business Analytics Courses (18 credits)
DS-510 Introduction to Data Science
Data Science is a set of fundamental principles that guide the extraction of valuable information and knowledge from data. This course provides an overview and develops student’s understanding of the data science and analytics landscape in the context of business examples and other emerging fields. It also provides students with an understanding of the most common methods used in data science. Topics covered include introduction to predictive modeling, data visualization, probability distributions, Bayes’ theorem, statistical inference, clustering analysis, decision analytic thinking, data and business strategy, cloud storage and big data analytics.
DS-660 Business Analytics
Business analytics is the process of generating and delivering the information acquired that enables and supports an improved and timely decision process. The aim of this course is to provide the student with an understanding of a broad range of decision analysis techniques and tools and facilitate the application of these methodologies to analyze real-world business problems and arrive at a rational solution. Topics covered include foundations of business analytics, descriptive analytics, predictive analytics, prescriptive analytics, and the use of computer software for statistical applications. The course work will provide case studies in Business Analytics and present real applications of business analytics. Students will work in groups to develop analytic solutions to these problems.
DS-640 Predictive Analytics & (with Bloomberg certification)
Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns. This course will provide predictive analytics foundational theory and methodologies as well as teach students how to build predictive models for practical financial and business applications and verify model effectiveness. Topics covered are linear modeling and regression, nonlinear modeling, time series analysis and forecasting, segmentation and tree models, support vector machine, clustering, neural networks and association rules.
DS-680: Marketing Analytics and Operations Research with Tableau
Organizations need to interpret data about consumer choices, their browsing and buying patterns and to match supply with demand in various business settings. This course examines the best practices for using data to prescribe more effective business strategies. Topics covered include marketing resource allocation, metrics for measuring brand assets, customer lifetime value, and using data analytics to evaluate and optimize marketing campaigns. Students learn how data is used to describe, explain, and predict customer behavior, and meet customer needs. Students also learn to model future demand uncertainties, predict the outcomes of competing policy choices and take optimal operation decisions in high and low risk scenarios.
DS-542 Python in Data Science
The course gives an introduction to Python programming for statistical analyses and managing, analyzing and visualizing data. Topics include numeric and non-numeric values, arithmetic and assignment operations, arrays and data frames, special values, classes and coercion. Students will learn to write functions, read/write files, use exceptions, measure execution times, perform sampling and confidence analyses, plot a linear regression. Students will explore tools for statistical simulation, large data analysis and data visualization, including interactive 3D plots.
Elective. Chosen from GB, DS or CO graduate courses