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 MyBU Student Portal for confirmation a class is actually being taught and for specific course meeting dates and times.

  • 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 843: Big Data Analytics for Business
    This programming-based analytics course will cover how to perform statistical analysis of large datasets that do not fit on a single computer. We will design a Hadoop cluster on Google Cloud Platform to analyze these datasets. Utilizing Spark, Hive, and other technologies, students will write scripts to process the data, generate reports and dashboards, and incorporate common business applications. Students will learn how to use these tools through Jupyter Notebooks and experience the power of combining live code, equations, visualizations, and narrative text. Employer interest in these skills is very high. Basic programming in python, and basic analytics are prerequisite. 3 cr.
  • 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 Analytics
    This course focuses on developments in People Analytics, an evolving data- driven approach to employee decisions and practices. The course covers theory, practice, and methods that are critical for addressing people- related challenges at companies, such as hiring, retaining, evaluating, rewarding performance, and managing teams and social networks, to name a few. By drawing on the latest company practices, research, and cases studies, this course will help students apply people analytics to achieve organizational objectives and to advance in their own career. We will also focus on how to apply insights to align people strategies with the organization's broader goals.
  • QST BA 885: Advanced Analytics 2
    This course covers analytics topics in applied optimization (or prescriptive analytics). In contrast to the unsupervised and supervised machine learning studied in BA820 and BA810 (and BA865) where the focus was to discover patterns and predict uncertain events, this course focuses on determining the best course of action given an objective and a set of constraints. In other words, making operational and strategic decisions using a rigorous and principled approach. The methods learned in this course have broad application including in logistics, marketing, health care, finance, and more. Example problems include determining which products to advertise to which customer to maximize sales, identifying best location of warehouses to best serve geographically dispersed stores or customers, and allocating medical resources to health care facilities to minimize the fallout during an active pandemic. Topics include linear programming, integer programming, network models, and related methods. Students will learn how to set up such optimization problems and solve them using spreadsheets and Python.
  • 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 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 921: Behavioral Science Writing Seminar
  • 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.
  • QST DS 999: Doctoral Dissertation Study
    This 2-credit course is a requirement to maintain doctoral student status during the completion of your Comprehensive Exam, Dissertation Proposal Defense and ultimately, Dissertation Defense. Each department has its own section which are as follows: Accounting (A1); Finance/Economics (B1); Information Systems (C1); Strategy and Innovation (D1); Marketing (E1); Operations and Technology Management (F1); Management & Organizations (G1); and Mathematical Finance (M1).
  • QST ES 090: Ascend Seminar
    The Ascend Seminar is designed to engage Questrom Ascend students in their own career exploration and development through company/industry exposure as well as timely professional development, leadership, and topics related to student success. Using the core values of a Questrom School of Business education as a foundation, the course will focus on preparing for a meaningful career and life, as well as building a professional network and community of Questrom students, faculty, staff, alumni, and corporate partners. This course is open only to those students who formally participate in the Questrom Ascend Fellowship.