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

• 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.
  BU Hub Learn More

  • Quantitative Reasoning II
• QST BA 305: Business Decision-Making with Data
  Undergraduate Prerequisites: QST BA222, or QST QM222 and CAS CS111
  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: CAS CS 108 or CAS CS 111 or QST BA 222.
  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 QST BA222; Junior standing
  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 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.
• QST BA 780: Introduction to Data Analytics
  This course focuses on data munging and the standard techniques that are necessary to work with any structured dataset. Students will learn how to work with the most common data sources and how to load them into python. 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. Data munging will be the core of this course, where students will learn how to clean the data, handle missing values, perform data transformations and manipulations, and prepare it for analysis. Through learning data visualization, exploratory techniques, and summarizing methods students will become competent to perform exploratory data analysis. These techniques are typically applied before any modeling begins and can help to formulate or refine the business problem. They are also stepping stones in informing the development of more complex statistical models. The course will conclude with creating data reports and interactive dashboards, two major communication tools required in any data science project.
• 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.
• 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.