Curriculum

12 Months, 30 Credits

The Saunders Master of Science in Accounting and Analytics program at RIT is designed for students with a BS in accounting, a BS in other business-related majors or a BS in non-related business majors.

Students may complete the program on a full-time or part-time basis.

Curriculum for current and past students

Current students and alumni – please visit the Office of the Registrar for a History of Course Catalogs to view and download official degree requirements pertaining to the academic year you began your degree.

Curriculum

Accounting and Analytics, MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
ACCT-738
Information Systems Auditing and Assurance Services
An examination of the unique risks, controls, and assurance services resulting from and related to auditing financial information systems with an emphasis on enterprise resource systems.
3
ACCT-745
Accounting Information and Analytics
The objective for this course is helping students develop a data mindset which prepare them to interact with data scientists from an accountant perspective. This course enables students to develop analytics skills to conduct descriptive, diagnostic, predictive, and prescriptive analysis for accounting information. This course focuses on such topics as data modeling, relational databases, blockchain, visualization, unstructured data, web scraping, and data extraction.
3
ACCT-796
Accounting Capstone Experience
The principal focus of this course is students completing several projects provided by members of CPA firms and industry employers. Employers provide assignments, which may include data or require students to gather relevant data, and students use defined technology, which may include a variety of applications common in technological accounting practice, to complete projects in teams. Students also write comprehensive individual reports and analyses related to the projects. Peripheral work in the course includes examination of theoretical concepts, definitions, and models espoused in the accounting literature and relevant to analyzing various contemporary issues in financial accounting and reporting. The historical development of accounting standards and contemporary issues in financial reporting are integrated. The course requires writing and student presentations. Subject to approval by the Program Director, an individual student internship/coop followed by an in-depth report may obtain equivalent credit.
3
BANA-680
Data Management for Business Analytics
This course introduces students to data management and analytics in a business setting. Students learn how to formulate hypotheses, collect and manage relevant data, and use standard tools such as Python and R in their analyses. The course exposes students to structured data as well as semi-structured and unstructured data. There are no pre or co-requisites; however, instructor permission is required for students not belonging to the MS-Business Analytics or other quantitative programs such as the MS-Computational Finance which have program-level pre-requisites in the areas of calculus, linear algebra, and programming.
3
BANA-780
Advanced Business Analytics
This course provides foundational, advanced knowledge in the realm of business analytics. Advanced topics such as machine learning, analysis of structured data, text mining, and network analysis are covered. Industry standard tools such as R and Python are extensively used in completing student projects.
3
FINC-780
Financial Analytics
This course provides a survey of financial analytics applications in contexts such as investment analysis, portfolio construction, risk management, and security valuation. Students are introduced to financial models used in these applications and their implementation using popular languages such as R, Matlab, and Python, and packages such as Quantlib. A variety of data sources are used: financial websites such as www.finance.yahoo.com, government sites such as www.sec.gov, finance research databases such as WRDS, and especially Bloomberg terminals. Students will complete projects using real-world data and make effective use of visualization methods in reporting results. There are no pre or co-requisites; however, instructor permission is required – student aptitude for quantitative work will be assessed; waived for students enrolled in quantitative programs such as the MS-Computational Finance which have pre-requisites in the areas of calculus, linear algebra, and programming.
3
MGIS-650
Introduction to Data Analytics and Business Intelligence
This course serves as an introduction to data analysis including both descriptive and inferential statistical techniques. Contemporary data analytics and business intelligence tools will be explored through realistic problem assignments.
3
BANA or MGIS Elective
3
Graduate Electives
6
Total Semester Credit Hours
30
Saunders students have a 95% Outcome Rate 6-months after graduation

MS of Accounting and Analytics