Combine vital areas of technology, Big Data, AI, and advanced analytics based on a foundation of accounting and auditing.
Hands-on experience working with R and Python computer languages, and Tableau data visualization software
Understand Structured Query Language (SQL) and Systems Applications and Products in Data Processing (SAP)
Understand essential technologies such as blockchain
Accounting analytics can help an organization answer financial questions by looking at all the data gathered by a company (e.g., transactional data, financial data, investment analysis, etc.) and analyzing this information to gain significant insights, predict future outcomes, or even ascertain risk.
Data Analytics for Accounting: Why Financial Data Matters
There are four key types of data analytics–descriptive, diagnostic, predictive, and prescriptive–and each has a role in helping an accountant report on activity happening within an organization. All four of these types of data can be used to create a full picture of what's happening within a business, what decisions can and should be made, and where growth opportunities lie.
Descriptive analytics tell us what is happening. Descriptive analytics categorizes and classifies a range of information. Accountants can use this trove of data to report on what is happening within a company, from cash flow, revenue and expenses, and inventory, to website traffic and social media analytics.
Diagnostic analytics tell us why something happened. Accountants are skilled at using data to create forecasts and predict trends. Diagnostics analytics is the use of data to determine the causes of trends and the correlations between any number of variables. For example, diagnostic analytics can help examine market demand for a product, can provide insight into why a product’s sales are up or down, or they can help explore correlation or causation between variables.
Predictive analytics tell us what’s going to happen. Will a key piece of machinery break down? Will an organization have enough cash flow in nine months? Should a company anticipate different staffing needs during a specific time period? Predictive analytics helps accountants examine data to forecast a range of different scenarios that can impact drive strategic decision making.
Prescriptive analytics tell us what we should do next. Prescriptive analytics is data-driven decision-making. It’s the use of data to determine a course of action. Social media applications use predictive analytics to determine what content to serve you based on your engagement with past content. Banks analyze transaction histories in order to identify fraud. Data on consumer behavior and shopping patterns can determine new product lines and product improvements. Prescriptive analytics can also point to problems that may arise or decision paths to avoid going down.
RIT’s Accounting Analytics Degree
In RIT’s master’s in accounting analytics, you’ll develop analytics skills to conduct descriptive, diagnostic, predictive, and prescriptive analysis of accounting information. The program pulls together key areas of technology, finance, strategy, analytics, data modeling, and more to help you advance your accounting career.
This innovative accounting analytics program teaches you how technologies and business analytics are used in the accounting profession, with a specific focus on:
Hands-on experience working with 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, you’ll benefit from the accounting analytics courses in areas 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.
Analytics for Accountants
Accounting has become quantitative and technology-infused. As a result, an accounting analytics degree can help you manage internally- and externally-collected data, and analyze it in ways that help your organization grow, respond to change, meet consumer exceptions, make financial decisions, and predict and forecast the future. Graduates of RIT’s master’s in accounting analytics are in demand and work for dynamic companies in every single industry.
RIT undergraduates qualify for a tuition scholarship when they choose an RIT Master’s program.
What makes an RIT education exceptional? It’s the ability to complete relevant, hands-on career experience. At the graduate level, and paired with an advanced degree, cooperative education and internships give you the unparalleled credentials that truly set you apart. Learn more about graduate co-op and how it provides you with the career experience employers look for in their next top hires.
Co-ops and internships take your knowledge and turn it into know-how. Business co-ops provide hands-on experience that enables you to apply your knowledge of business, management, finance, accounting, and related fields in professional settings. You'll make valuable connections between course work and real-world applications as you build a network of professional contacts.
Students in the accounting and analytics MS are encouraged to participate in at least one cooperative education or internship experience.
Research Insights: When Money is Blue and Red
Institutional shareholders’ political leanings and corporate performance.
Accounting and Analytics, MS degree, typical course sequence
Sem. Cr. Hrs.
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.) Lecture 3 (Spring).
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).
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).
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).
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).
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).
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).
BANA or MGIS Elective
Total Semester Credit Hours
To be considered for admission to the MS program in accounting and analytics, candidates must fulfill the following requirements:
Submit copies of official transcript(s) (in English) of all previously completed undergraduate and graduate coursework, including any transfer credit earned.
Hold a baccalaureate degree (or US equivalent) from an accredited university or college.
Recommended minimum cumulative GPA of 3.0 (or equivalent).
Submit a current resume or curriculum vitae.
Letters of recommendation are optional.
Students are required to complete online preparatory coursework in R and Python prior to joining the MS in accounting and analytics program. The coursework does not need to be completed prior to applying, and will take roughly 3-4 weeks to complete.
International applicants whose native language is not English must submit official test scores from the TOEFL, IELTS, or PTE. Students below the minimum requirement may be considered for conditional admission. Refer to Graduate Admission Deadlines and Requirements for additional information on English language requirements. International applicants may be considered for an English test requirement waiver. Refer to the English Language Test Scores section within Graduate Application Materials to review waiver eligibility.