Your curriculum includes a mix of courses in core business functions and data science and quantitative methods. While the course topics may evolve over time, and when they are offered may shift between calendar years, the MSQM: Business Analytics structure is likely to follow this format:
Programming for Data Analytics
Build a foundation in R and Python to prepare for subsequent courses in your program that use these languages. In addition, you’ll learn basic principles of visualization.
Applied Probability and Statistics
Understand how to address management decisions that, invariably, will need to be made under conditions of uncertainty. This course provides you with a solid foundation in applied probability and statistics, required for data-driven quantitative managerial decision-making as well as for subsequent courses in your program.
Learn the fundamental concepts of microeconomics, such as pricing decisions, market equilibrium, strategic interaction, and asymmetric information, which will serve as a foundation for future courses in business such as finance, marketing, and strategy.
Business Fundamentals: Accounting and Finance
Explore two core areas of business: financial accounting and finance. The accounting module introduces you to the types of information requirements imposed on a firm by agencies in its environment, and study financial accounting, reporting, and measurement problems from both a theoretical and an applied basis. The finance module introduces you to fundamental concepts in finance and provides a set of tools for analyzing the investment and financing decisions of both individuals and firms.
Business Fundamentals: Marketing and Strategy
Examine two core areas of business: marketing and strategy. The marketing module provides you with an overview of the role of marketing in organizations by exposing you to the fundamental issues and decisions involved in planning and managing marketing activities. You’ll develop an understanding of the underlying forces that influence marketing decisions, including customer behavior, competitive marketing activity, and organizational considerations. The strategy module examines topics related to the question: Why are some firms more profitable than others? You’ll learn the concepts and skills necessary for managers, management consultants, and financial analysts to understand, craft, and support a firm’s strategy.
Data Analytics and Applications
Investigate how data analysis can be used to guide business practices by discussing a variety of real-world situations. You will study the core concepts behind data analytics, the challenges associated with big data, and the interplay between data science and business decisions, with your focus being on the long-lasting, general principles that endure the rapid change of technology and the "hands-on" analyses of actual datasets to develop methodologies.
Assess the impact of the rapidly evolving communication and distribution channels in the context of digital technology and consumer migration to the Internet. You’ll consider advertising budgets shifting to display and search, and goods now positioned for online purchase, and review the associated key performance indicators and tools to use to improve the efficiency of digital marketing.
Financial Risk Management
Study key concepts of fixed income securities and learn how to calculate the return of a portfolio of securities as well as quantify the market risk of that portfolio, using the R programming language with Microsoft Open R and RStudio, to calculate Value-at-Risk (VaR) and Expected Shortfall (ES). You’ll master these important skills for financial market analysts in banks, hedge funds, insurance companies, and other financial services and investment firms.
Advanced Data Analytics and Applications
Learn how an expansion in data availability, improvements in computational power, and the design of digital- and data-centric organizations have fostered data-driven business decisions. You’ll build on the material you covered in “Data Analytics and Applications” with advanced tools, algorithms, and technologies currently being used in many industries.
Understand and improve business processes, such as capacity planning, scheduling, queueing analysis, inventory analysis, and lean and six-sigma program implementation, through data analysis. Technology has enhanced the way that the data generated in these processes is collected and assessed to make more effective decisions.
Identify decision situations that are too difficult to grasp intuitively, or where the stakes are too high to learn by experience. You'll leverage decision models learned in this course that allow you to consider the different possible scenarios and learn more about the problem.
Find out how quantitative analytic techniques combined with expert analysis can identify potentially fraudulent behavior. Once you’ve detected a new fraud pattern, quantitative techniques can help identify potential perpetrators and put corrective measures into place. In this course, you will explore analytics techniques currently in use to identify and prevent fraud in relevant business contexts.
Empirical Analysis for Business Strategy
Gain exposure to the statistical techniques, primarily causal inference, used to evaluate business outcomes, as well as potential confounding factors and the quasi-experimental methods, such as instrumental variables regression, regression discontinuity, and difference-in-differences estimation, to mitigate their effects.
Conduct smart quantitative analysis by overcoming common cognitive biases as well as by working effectively with others in your organization. Identify what principles you can draw on to analyze and improve performance in your firm and how to become an effective leader and contributor to your firm that others respect and are willing to follow.
Master the foundations of effective management communication, including communicating clearly, strategically, persuasively, and collaboratively in professional business settings. You’ll learn about and practice a variety of crucial communication skills and hone them in team presentations, where analysis and recommendations must withstand the challenges of audience members.
Ethics and Legal Issues in Business Analytics
Examine the leading issues in ethics and the law, including those that arise in the decisions made by manufacturers and marketers. You’ll cover topics including privacy, data ownership, restrictions on data analysis, the effect of new technologies on business policy, and potential for biases. The course will use case examples to illustrate the dilemmas and challenges.
Your faculty will bring online courses to life through lectures, class discussions, and case-based learning. Professors use actual data science and business problems, leverage interdisciplinary perspectives, and help you understand the "Data to Decision" cycle. The case-based learning method uses real-world business analytics cases as the basis for discussions and team projects. This approach challenges you to see business problems from a corporation's perspective and to take what you learn in class to develop quantitatively-supported recommendations. Because so much of your online learning happens through debate and analysis, you’re expected to be an active participant in class discussions. As a result, you’ll learn to think as a data scientist does--practically, critically, and creatively--preparing you to tackle business problems from multiple perspectives.
Fuqua is serious about ethical leadership and we create a climate of integrity. All members of our community are governed by Fuqua's Honor Code. By electing to join our community, in turn, you will be expected to abide by our standards of honesty and integrity.