Courses

The listing of a course description here does not guarantee a course’s being offered in a particular term. 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 AC 865: Auditing Issues & Problems
  Graduate Prerequisites: QST AC848 (previous or concurrent) - Introduces the basic concepts underlying auditing and assurance services (including materiality, audit risk, and evidence) and demonstrates how to apply those concepts to audit and assurance services through financial statement audits.
• QST AC 869: Principles of Income Taxation 1
  Graduate Prerequisites: QST AC847 (previous or concurrent) - Federal income tax law common to all taxpayers--individuals, partnerships, corporations. Tax returns for individuals. Topics include tax accounting, income to be included and excluded in returns, tax deductions, ordinary and capital gains and losses, inventories, installment sales, depreciation, bad debts, and other losses.
• QST AC 898: Directed Study: Accounting
  Graduate Prerequisites: consent of instructor and the department chair - Graduate-level directed study in Accounting. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST AC 899: Directed Study: Accounting
  Graduate Prerequisites: consent of instructor and the department chair - Graduate-level directed study in Accounting. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST AC 901: Introduction to Accounting Research
  Introduction to basic tools in financial accounting and managerial accounting research; domain of accounting research and research methods employed; using computerized databases in large sample financial accounting studies; basic managerial accounting modeling tools.
• QST AC 909: Contemporary Accounting Topics
  This course, required of accounting doctoral students, introduces several fields of contemporary accounting research and research methodologies which are not covered in the financial accounting, managerial accounting, and research methods seminars. This seminar is also intended to provide an opportunity for students to study interdisciplinary research involving accounting.
• QST AC 918: Financial Accounting Research
  This course, required of accounting doctoral students, covers contemporary research in financial accounting, reviewing major trends and addressing methodological issues in such research. The course emphasis is on development of skills in designing and executing research projects involving financial accounting.
• QST AC 919: Managerial and Cost Accounting
  This course, required of accounting doctoral students, covers contemporary research in managerial accounting. We review major trends in analytical and empirical research, including agency theory. Students are required to design a research project around a managerial accounting question.
• QST AC 990: Current Topics Seminar
  For PhD students in the Accounting department. Registered by permission only.
• QST AC 998: Directed Study: Accounting
  Graduate Prerequisites: consent of instructor and the department chair - PhD-level directed study in Accounting. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST AC 999: Directed Study: Accounting
  Graduate Prerequisites: consent of instructor and the department chair - PhD-level directed study in Accounting. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST BA 222: Modeling Business Decisions and Market Outcomes with Spreadsheets and Statistical Programming
  Undergraduate Prerequisites: CAS EC101, QST QM221, and QST SM131 - Examines the use of economic and statistical tools for making business decisions at an advanced level, and prepares students for future study in business analytics. Introduces programming for data analysis (no previous programming knowledge required) and links data analysis to decision making using both spreadsheet modeling and statistical programming. Topics include multiple regression, causal inference, forecasting, predictive analytics, machine learning, demand modeling, and optimization. Case studies apply advanced concepts to practical business problems. Effective Spring 2021, this course fulfills a single unit in the following BU Hub area: Quantitative Reasoning II.
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  • Quantitative Reasoning II
• QST BA 305: Business Decision-Making with Data
  Undergraduate Prerequisites: QST BA222 or QST QM222 and either CAS CS111 or CDS DS110 - Explores advanced business analytics topics, including risk and uncertainty, optimization, decision analysis, multi-attributes objective functions, and time tradeoffs. Links data models to strategy and ethics. Relies on both statistical programming and spreadsheet modeling and introduces novel techniques. Cases studies and projects apply topics to practical business problems.
• QST BA 472: Business Experiments and Causal Methods
  Undergraduate Prerequisites: CASCS108 or CASCS111 or CDS DS110 or QST BA222 - Formerly MK472. When is making a change to a price, algorithm, or product worthwhile? Rather than relying on the gut intuition of a manager, businesses are increasingly using experiments and other forms of causal data analysis to answer these questions. In this class, we will learn about causal methods, when they work, how to implement them in R, and how to apply them to digital markets. The business topics covered include pricing, balancing digital marketplaces like Airbnb and Uber, reputation systems, measuring influence in social networks, and algorithmic design.
• QST BA 476: Machine Learning for Business Analytics
  Undergraduate Prerequisites: CAS CS108 or CAS CS111 or CDS DS110 or QST BA222 - Formerly MK476. This course introduces students to the foundational machine learning techniques that are transforming the way we do business. Machine learning relies on interdisciplinary techniques from statistics, linear algebra, and optimization to detect structure in large volumes of data and solve prediction problems. You will gain a theoretical understanding of why the algorithms work, when they fail, and how they create value. You will also gain hands-on experience training machine learning models in Python and deriving insights and making predictions from real-world data. Prior programming experience is strongly recommended.
• QST BA 600: Introduction to Programming with Python
  This course provides a short, intensive introduction or refresher to fundamental programming concepts using the python programming language. It will cover pre- requisite topics important for future MSBA classes such as data types, flow control, iteration, functions, I/O, error handling, use of libraries and code documentation. Students will also be introduced to tools or platforms used throughout the MSBA program such as Jupyter notebooks and Git/Github.
• QST BA 602: Fundamentals of Data Analysis and Statistics
  This course provides a short, intensive introduction or refresher to data analysis and statistics. It will cover fundamental concepts such as single and multivariate analyses, probability, statistical distribution, hypothesis testing, sampling, regression, and early data visualization. The class will leverage and further build on python knowledge reviewed in BA600, and in some cases Excel (as more mainstream tool used in many business environments).
• QST BA 775: Business Analytics Toolbox
  This course will primarily focus on data and the key techniques that are necessary when working programmatically. Data is obtained from a data source; students will learn how to work with the most common data sources and how to load it into R. Once the data is loaded and before it can be analyzed one needs to apply a series of steps known as data munging to get a tidy and workable dataset.
• QS
• QST BA 810: Supervised Machine Learning
  The internet has become a ubiquitous channel for reaching consumers and gathering massive amounts of business-intelligence data. This course will teach students how to perform hands-on analytics on such datasets using modern supervised machine learning techniques through series a lectures and in-class exercises. Students will analyze data using the R programming language, derive actionable insights from the data, and present their findings. The goal of the course is to create an understanding of modern supervised machine learning methods, and the types of problems to which they can be applied.