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 AC 848: Intermediate Accounting 2
  This course focuses on the recognition and measurement of issues in accounting related to income taxes, lease obligations, and pension liabilities and equity. It focuses further on the preparation of, and uses for, statement of cash flows; calculating, reporting, and interpreting financial measures, including earnings per share; the nature and purpose of segment and interim reporting; and accounting for changing prices. The course also provides a brief overview of the auditor's opinion.
• QST AC 860: Accounting Risk Management
  The objective of this course is to provide students who have no previous accounting knowledge with the accounting tools necessary for a better understanding of a firm's fundamentals, to enable a meaningful economic assessment of the firm's risk and potential return.
• QST AC 865: Auditing Issues & Problems
  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
  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 879: Income Taxation II
  Certain common and special Federal tax laws for individuals, partnerships, corporations, estates, trusts, and miscellaneous entities. Topics include income tax returns for partnerships, business corporations, special corporations, decedents, estates, and trusts. Survey coverage of corporate liquidations, pension and profit-sharing plans, IRS audits, and estate and gift taxes.
• QST AC 898: Directed Study: Accounting
  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-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
  PhD-level directed study in Accounting. 1, 2, or 3 cr. Application available on the Graduate Program Office website.
• QST AC 999: Directed Study: Accounting
  PhD-level directed study in Accounting. 1, 2, or 3 cr. Application available on the Graduate Program Office website.
• QST BA 750: Describing, Analyzing, and Using Data
  This course focuses on how to learn from data, specifically to 1) organize, portray, and summarize data; 2) assess the validity of conclusions that have been drawn from statistical analyses to support business (and other) decisions; and 3) recognize the extent to which variation characterizes products and processes, and understand the implications of variation on organizational decisions when interpreting data. Students will increase their understanding of the use of probabilities to reflect uncertainty; how to interpret data in light of uncertainty to assess risk; and how to build and interpret regression models, which can be used to inform core business and organizational decisions.
• QST BA 760: Introduction to Programming for Data Science
  This course will cover the fundamentals of programming for data science using R, the command line, and version control. These skills will be reinforced via lectures and hands-on exercises focused on elevating common programming challenges and highlight best practices. The aim of this course is to provide the pathway to fluency in the tools required to analyze data and fully manage data science projects both as an individual contributor as well as in team settings.
• QST BA 770: Business Analytics Toolbox
  Much of the data owned by companies resides in databases. This hands-on course introduces students to various relational databases and SQL, which is the standard language that is used to query the data. By working with a variety of datasets, students will become familiar with the fundamentals of both modern and traditional databases and the essentials of writing SQL queries, such as select, filter, sort, group, and join. Students will set up their own database in the cloud and learn how to work with real-world data. Some additional cloud components such as cloud storage and cloud computing will be covered as well. Students will learn how to set up a virtual cloud environment where they will run their analysis. Once the fundamentals of databases and cloud are furnished, students will work towards creating visualized summaries of the data in a dashboard. Students will become familiar with storytelling using data and learn the key aspects of creating meaningful business intelligence dashboards.
• QST BA 780: Introduction to Data Analytics
  This course primarily focuses on data and the key techniques that are necessary when working with it programmatically. 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. 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 analytics methods, and the types of problems they can be applied to. The course is open to students with or without a technical background who are interested in analytics. While no prior programming experience is required, students will learn the fundamentals of the R programming language to build and test predictive models.
• 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.

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