Business Analytics MS

A degree driven by real-time employer demand

Business analytics jobs will grow 15% by 2026. Search our curriculum and you’ll find the expertise and skills most frequently posted by employers in this growing field.

The business analytics job market


Average Annual Salary


Employment Growth


Demand Growth for AI


Demand Growth for Blockchain

Program Highlights

RIT’s online master of science in business analytics will empower you to turn big data into actionable intelligence. At the intersection of business and data science, business analytics harnesses the power of data analysis to drive and optimize business performance, strategy, and operations.

Roles in this field are 20-40% higher-paying and are immune to replacement by automation. Hybrid roles like a business analyst—with a dual business and IT focus—are desperately needed by employers. 

Now more than ever you may decide to invest your time in pursuing your master’s degree in business analytics. For this reason, RIT is now waiving the GMAT requirement for a limited time. The online curriculum, faculty, and distinguished degree are identical to the on-campus degree program, and you may complete this degree in as few as 24 months studying part-time. The analytics courses offered focus on a variety of applications across various business disciplines.

Curriculum packed with high-demand skills

icon of a bar chart.


Data visualization and tableau skills are growing by 87%; machine learning by 102%.

circles connected with lines.

Data Science

Demand for deep learning skills will grow 135%, and AI skills are growing by 128%.

icon showing servers connected.

Software, Systems, and Technology

Demand for SAP skills is growing by 78%; Python and R by 61%.

icon of a gear with checkboxes

Industry-Specific Applications

SAS skills are found in half of marketing analytics job postings.

What you will learn

  • Both broad and in-depth training in technical, analytical, and operational areas 
  • How to use emerging technologies and practices in multiple disciplines including management information systems (MIS), marketing, accounting, finance, management, and engineering 
  • How to interpret your findings and communicate your insights clearly 
  • How to apply your skills to influence organizational optimization and outcomes


Credits 3
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.
Credits 3
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.
Credits 3
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.
Credits 3
Students apply their mathematical, data analytic, and integrative business analytics skills in a complex project involving real or simulated data. Under the supervision of an advisor, students work in teams to perform a stipulated task/project and write a comprehensive report at the end of the experience. Subject to approval by the program director, an individual student internship/coop followed by an in-depth report may obtain equivalent credit.
Credits 3
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, government sites such as, 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.
Credits 3
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.
Credits 3
This course provides an overview of marketing analytics in the context of marketing research, product portfolios, social media monitoring, sentiment analysis, customer retention, clustering techniques, and customer lifetime value calculation. Students will be introduced to, mathematical and statistical models used in these applications and their implementation using statistical tools and programming languages such as SAS, SPSS, Python and R. Multiple data sources will be used ranging from structured data from company databases, scanner data, social media data, text data in the form of customer reviews, and research databases. Students will complete guided projects using real time data and make effective use of visualization to add impact to their reports. There are no listed 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.
Credits 3
This course provides students with fundamental knowledge and skills required for successful analysis of problems and opportunities related to the flow of information within organizations and the design and implementation of information systems to address identified factors. Students are provided with knowledge and experience that will be useful in determining systems requirements and developing a logical design.
Credits 3
This course discusses issues associated with data capture, organization, storage, extraction, and modeling for planned and ad hoc reporting. Enables student to model data by developing conceptual and semantic data models. Techniques taught for managing the design and development of large database systems including logical data models, concurrent processing, data distributions, database administration, data warehousing, data cleansing, and data mining.
Credits 3
This course focuses on the concepts and technologies associated with Integrated Business Information Systems and the managerial decisions related to the implementation and ongoing application of these systems. Topics include business integration and common patterns of systems integration technology including enterprise resource planning (ERP), enterprise application integration (EAI) and data integration. The key managerial and organizational issues in selecting the appropriate technology and successful implementation are discussed. Hands-on experience with the SAP R/3 system is utilized to enable students to demonstrate concepts related to integrated business systems. (familiarity with MS Office suite and Internet browsers)

Earn a credential as-you-go

Earn the advanced certificate in accounting and financial analytics and advance your career, all while working toward your master of science in business analytics. Four courses of the MS degree program may be fully applied toward a graduate advanced certificate.

Admission Requirements

  • 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 a personal statement of educational objectives.
  • Submit a current resume or curriculum vitae.
  • Submit GRE/GMAT scores (waivers available).  
  • International applicants whose native language is not English must submit scores from the TOEFL or IELTS exams.

aacsb logoRIT’s Saunders College of Business is accredited by the AACSB, the premier agency which defines quality standards for business programs.

Certain countries and individuals are subject to comprehensive embargoes under US Export Controls, which prohibit virtually ALL exports, imports and other transactions without a license or other US Government authorization. Individuals applying for online study who are subject to these embargoes will be notified during the application process.