Accounting and Analytics Master of science degree

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An accounting master's degree that combines technology, finance, analytics, strategy, and compliance to advance your career. 


100%

Outcome Rate of RIT Graduates


Overview

RIT’s accounting and analytics master's degree pulls together important areas of technology, finance, strategy, analytics, and compliance to help you advance your accounting career.

This innovative program teaches you how technologies and business analytics are used in the accounting profession, with a specific focus on:

  • Hands-on experience working data-science oriented computing languages such as R and Python
  • Working knowledge of databases in Structured Query Language (SQL) and Systems Applications and Products in Data Processing (SAP)
  • Data visualization skills, such as Tableau
  • Understanding of essential technologies such as blockchain

As an accountant or business professional seeking career advancement, the accounting and analytics program provides course work in areas of technology that are making a significant impact on today's business operations, including big data, AI, and advanced analytics based on the foundation of accounting and auditing. You'll be taught business analytics and technology skills by faculty who teach in RIT's nationally ranked program in management information systems.

Graduates work primarily in the Accounting industry.

Typical Job Titles

Certified Public Accountant (CPA)  Auditor
 Management Accountant  Government Accountant
 Fraud Examiner  Tax Accountant

Curriculum for Accounting and Analytics MS

Accounting and Analytics, MS degree, typical course sequence

Course Sem. Cr. Hrs.
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. (Prerequisites: ACCT-705 or equivalent course. Pre- or Corequisites: ACCT-708 or equivalent course.) Lecture 3 (Spring).
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. (Prerequisites: ACCT-110 or ACCT-603 or equivalent course.) Lecture 3 (Fall, Summer).
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. Lecture 3 (Spring).
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. Lecture 3 (Fall).
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. (Prerequisite: BANA-680 or equivalent course.) Lecture 3 (Spring).
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. Lecture 3 (Fall).
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. Lecture 3 (Fall).
3
 
BANA or MGIS Elective
3
 
Electives
6
Total Semester Credit Hours
30


 

Admission Requirements

To be considered for admission to the MS program in accounting and analytics, candidates must fulfill the following requirements:

  • Complete a graduate application.
  • Hold a baccalaureate degree (or equivalent) from an accredited university or college.
  • Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
  • Submit scores from the GMAT or GRE. (GMAT preferred for international applicants and those applying for scholarships.)
  • Submit a personal statement of educational objectives.
  • Submit a current resume or curriculum vitae.
  • International applicants whose native language is not English must submit scores from the TOEFL or IELTS exams. A minimum score of 88 on the TOEFL or 6.5 on the IELTS exams is required (requirements on sub-scores on each component of the TOEFL or IELTS may also apply). The English language test score requirement is waived for native speakers of English or for those submitting transcripts from degrees earned at American institutions.
  • Completed applications for admission should be on file in the Office of Graduate and Part-time Enrollment Services at least four weeks prior to registration for the next academic semester for students from the United States, and up to 10 weeks prior for international students applying for student visas.

For further information about specific GMAT/GRE waiver opportunities, tips on personal statements, and additional guidance on how to submit a successful application, please visit Saunders College of Business Admissions Requirements.

Deferment

Accepted students can defer enrollment for up to one year. After one year, a new application must be submitted and will be re-evaluated based on the most current admission standards.

 

Learn about admissions, cost, and financial aid 

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