Courses

The listing of a course description here does not guarantee a course’s being offered in a particular semester. Please refer to the published schedule of classes on the Student Link for confirmation a class is actually being taught and for specific course meeting dates and times.

• QST BA 815: Competing with Analytics
  The objective of this class is to examine how business analytics is applied across different industries and functions, how it delivers value, which skills are core to capturing this value, and which pitfalls await organizations. The course will rely extensively on seasoned industry experts sharing their direct experiences and include readings, case study discussions, and hands-on team assignments. Rather than taking a narrow(er) focus on any one topic, the course will take a broad lens and provide a wide set of pertinent examples of application in industry (e.g. recommender systems, web analytics, personalization campaigns, pricing and revenue management, ML Ops, data storytelling, demand forecasting/sensing, inventory optimization, fraud and claims analytics, ESG modeling, managing data science projects, etc.).
• QST BA 820: Unsupervised and Unstructured Machine Learning
  It has been reported that as much as eighty percent of the world's data is unstructured. This course will cover the methods being applied to both unstructured and unlabeled datasets. Through a series of lectures and hands-on exercises, students will examine the techniques to unlock insights from data that appear to lack a known outcome. The goal of this course is to compare and contrast the application of various methods being applied today and provide the foundation to develop impactful insights from these datasets.
• QST BA 830: Business Experimentation and Causal Methods
  This course teaches students how to measure impact in business situations and how to evaluate others' claims of impact. We will draw on a branch of statistics called causal inference that studies when data can be used to measure cause and effect. The course will begin by discussing randomized controlled trials, the most reliable way of measuring effects, and will move onto other methods that can be used when experiments are not feasible or unavailable. We will learn how to implement these methods in R. Causal inference has become especially important for digital businesses because they are often able to run experiments and to harness 'big data' to make decisions. We will illustrate the methods we learn with examples drawn from digital businesses such as Airbnb, Ebay, and Uber and through topic areas such as price targeting, balancing digital marketplaces, reputation systems, measuring influence in social networks, and algorithmic design. We will also use data from other business and social science applications.
• QST BA 840: Data Ethics: Analytics in Social Context
  This class examines ethical issues of data, data science, and algorithms. We consider unintended consequences and transparency of algorithms, phenomena such as mass personalization and experimentation, and examine competing ideas about privacy and the sometimes blurry line between the private and the public spheres in the digital age. The course is intended to place analytics in a social context and equip students to anticipate and understand the ethical tradeoffs they will be making in the process of doing analytical work.
• QST BA 860: Marketing Analytics
  This is an introductory course on Digital Marketing emphasizing analytics that seeks to familiarize students with digital marketing tactics. At the heart of marketing lies consumers and their marketing journey through the stages of awareness, intent, conversion and finally retention. In this course, we will learn how digital has revolutionized the interactions between firms and consumers along this journey. Digital offers powerful tactics to reach consumers along the funnel: online display ads raise awareness, search listings reach consumers with intent, on-site e-commerce marketing facilitate conversion, and social medial both energizes and retains customers.
• QST BA 865: Advanced Analytics Topics
  This course will introduce you to the Python programming language and the ecosystem of software packages needed for Data Science and to build and train Neural Networks in Python, including: NumPy, Pandas, SKlearn, and PyTorch. After reviewing key Python building blocks, the course will focus on Neural Networks and Deep learning Concepts and implementation in PyTorch. This is an intensive course and the majority of it will be presented through interactive python notebooks (Google Colab).
• QST BA 870: Financial Analytics
  This is an introductory course on Financial Analytics providing students with knowledge about key "financial" concepts (financial accounting, financial statements, managerial accounting, corporate finance, and investments) so that they can intelligently apply their prior analytics knowledge and tools to real- world financial applications.
• QST BA 875: Operations and Supply Chain Analytics
  This is an introductory course on principles, methods, and techniques used in operations and supply chain analytics. Emphasis is given on the big data age where firms are continuously designing, assessing, and improving the systems that create and deliver their products and services. Students will learn visual representation techniques to enhance their understanding of complex data and models. Such visual techniques will be paired with network analysis to better identify patterns, trends and differences from datasets across categories, space, and time. The course will also draw on real-world applications to demonstrate their use in a variety of contexts.
• QST BA 880: People Analytcs
  
• QST BA 885: Adv Analytcs II
  
• QST BA 888: Capstone Project
  The capstone project course will allow students to work on a data project in a team setting. The goal is for the students to solve a real-world problem using the knowledge, tools, and techniques acquired throughout the program and show their skills to potential employers. This course spans across the degree program and requires multi-semester efforts, however, the vast majority of the work will be done during the spring semester. The final product will be presented to a faculty panel at the end of the spring semester, followed by a poster session which will be open to the public.
• QST BA 890: Analytics Practicum
  The analytics practicum provides an opportunity for students to gain individual, practical experience related to business analytics. Students will complete a report based on one of the following: - Reflection paper related to an internship experience: Students will describe work accomplished and knowledge gained from working on a part-time or full-time internship in an area directly related to Business Analytics (e.g., data engineering, data analysis, data modeling, machine learning, data visualization). The paper should demonstrate the student's knowledge of Business Analytics concepts acquired through the internship experience. - Research Project: Students will select a topic related to Business Analytics which has not been covered in existing coursework or significantly extends concepts taught in the MSBA curriculum. The research topic can be novel or can be an extension of work completed during the capstone project. It should be substantive enough in terms of technical, quantitative, data management, or programming aspects and contain appropriate references. Students should not merely compile work of others, but also display genuine critical thinking.
• QST DS 850: Management Internship
  This course is designed to accommodate students interning with an organization that requires that they receive credit for the internship experience.
• QST DS 906: Fundamentals of Research and the Philosophies of Science (previously MK912)
  This course introduces students to research. The class provides a brief introduction to the philosophy of science and debates about the nature of theory before diving thoroughly into different research methods. Students are exposed to research methods from their own and adjacent fields ranging from causal inference and experiments to qualitative research methods. The last part of the class introduces students to issues around diversity, ethics, and equity in research. As part of the class students will complete the introductory ethics modules that are required by the university. Students will be graded on their class participation, a research proposal which is due at the end of the class, and their feedback to other students on their research proposals.
• QST DS 907: Teaching, Publishing, and Dissemination of Knowledge
  As scholars, doctoral students will be responsible for both conducting research in their chosen field and disseminating knowledge through publishing and teaching. This course prepares participants to be successful in converting research into publications and provides a foundation for designing and delivering educational offerings in a variety of settings.
• QST DS 911: Seminar in Macro Organizational Theory
  This doctoral-level course is an introduction to the major theoretical approaches and ongoing debates in organizational theory, an inter- disciplinary subject area that draws on several traditions, including economics, political science, psychology, and sociology. Organization theory aims to explain the origins, persistence, and disappearance of the organizations that are central to our society and daily life (e.g., firms, markets, governments, occupations, non-profit organizations, and more). We will start with the classics and then trace the history of ideas as the field has evolved to its present state. The purpose of this course is to provide a roadmap to navigate the terrain of organizational theory and guide students as they generate original research ideas. (Cross-listed as GRS SO716).
• QST DS 913: Experimental Design and Methods
  This course provides an introduction to research methodology applicable to marketing and other related fields. The course will survey the major research methodologies used in marketing and social psychology, and will focus on both theoretical and practical considerations of research methods. This is not a statistics course (though an introduction to basic principles is part of the course). The purpose of the course is to give students the background to choose the methods that are most appropriate for their area of study, helping them to anticipate the shortcomings and problems they will encounter executing their chosen methodologies, and to defend their methodological choices against criticism in their interactions with investigators from allied and not-so-allied disciplines.
• QST DS 919: Machine Learning Method for Social Science Research
  This course aims to introduce PhD students in Management to Machine Learning methods with an emphasis on their application in social science research. The first half of the course discusses popular predictive models (regression models, SVM, tree-based methods, etc) and related concepts. The second half discusses graphical models to develop and estimate probabilistic models. The course will have a set of programming/estimation assignments based on recent relevant papers and one final exam. By the end of the course, students will be equipped to spot a machine learning problem in their line of research, specify a model for it, and estimate and evaluate it.
• QST DS 925: Methods for Causal Inference in Strategy Research
  (Formerly SI 915) This course reviews tools and methods for drawing causal inferences from non-experimental data. The class emphasizes conceptual difficulties associated with establishing causality in observational settings, the strengths and weaknesses of statistical methods based on so-called natural experiments, and the practical problems that arise in the application of these tools. This course is designed to complement a traditional two-semester graduate sequence in econometrics.
• QST DS 929: Analytical Modeling for Business Research
  This course is designed to provide doctoral students in a business school with an introduction to analytical models so that they can access the theoretical literature and potentially develop new models for their own research. The course will introduce basic concepts in game theory (e.g., Nash Equilibrium, Perfect Bayesian Equilibrium) and classic models in industrial organization (e.g., pricing, distribution, competition, product differentiation, advertising) and behavioral economics (e.g., prospect theory, hyperbolic discounting). Students need to be comfortable with calculus and basic probability theory.

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