Your curriculum consists primarily of courses in data science and quantitative methods. While the set of courses and timing may shift between calendar years, the Accelerated MSQM: Business Analytics structure is likely to follow this format:
Programming for Analysis and Visualization
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.
Identify decision situations that are too difficult to grasp intuitively, or where the stakes are too high to learn by experience, in order to leverage decision models you’ll learn in this course that allow you to consider the different possible scenarios and learn more about the problem.
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.
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.
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.
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.
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.
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.
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.
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 analytics 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.