Business Analytics Master of science degree
Business Analytics
Master of science degree
A business analytics master's degree is perfect for professionals who need to analyze and interpret information to inform and guide sound business decisions.
Overview
Today's businesses collect an incredible amount of data from nearly every customer touch point, from point-of-sale transactions, customer service interactions, social media feedback, search engine entries, market research activities, sales data, demographic information, and more. Right now, only a tiny portion of this data is analyzed and used to guide and inform business decisions. By earning a business analytics master’s degree, you’ll become skilled in using big data to create powerful solutions to help companies increase sales, reach new customers, develop new products, enhance customer experiences, and more. The program is available on-campus, or you may earn an online master’s in business analytics.
Business Analytics Courses
In RIT’s business analytics master’s degree you’ll acquire a broad and in-depth training in multiple disciplines related to business analytics, including management information systems (MIS), marketing, accounting, finance, analytics, and data management.
This is a career-focused degree developed in conjunction with top employers–such as Intuit, Excellus, and PriceWaterhouse–with a curriculum designed to help you understand and connect contemporary analytics technologies with today's business practices. You’ll complete business analytics courses that will enable you to develop the advanced skills needed to conduct the descriptive, diagnostic, predictive, and prescriptive analysis of information as you learn to manage data and analytics in a range of business settings.
Learn in High Tech Analytics Labs
You’ll pair your master’s in business analytics with access to two high tech labs. The Sklarsky Center for Business Analytics, a modern, interactive lab that features Bloomberg Terminals and the most advanced analytics software. The REDCOM Active Learning Collaboratory supports interactive learning and features CISCO Telepresence for connecting interactively with RIT’s global campuses in China, Croatia, Dubai, and Kosovo, and our corporate partners around the world.
Careers in Business Analytics
You’ll graduate prepared to launch your career in business analytics in positions that range from marketing research, analytics, and consulting; digital analytics; web intelligence and analytics; accounting and financial analytics and risk management; supply chain analytics; customer analytics; and consulting.
Typical Job Titles
| Business Intelligence Analyst |
Curriculum for Business Analytics MS
Business Analytics, MS degree, typical course sequence
| Course | Sem. Cr. Hrs. | |
|---|---|---|
| First Year | ||
| 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 |
| 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 |
| BANA-785 | Business Analytics Experience 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. (Prerequisite: BANA-780 or equivalent course.) Lecture 3 (Summer). |
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 |
| MKTG-768 | Marketing Analytics 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. Lecture 3 (Spring). |
3 |
Analytics Elective |
3 | |
Open Elective |
6 | |
| Total Semester Credit Hours | 30 |
|
Analytics Electives
| Course | |
|---|---|
| MGIS-720 | Information Systems Design 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. Lecture 3 (Fall). |
| MGIS-725 | Data Management and Analytics 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. Lecture 3 (Spring). |
| MGIS-735 | Design and Information Systems Students who complete this course will understand the principles and practices employed to analyze information needs and design appropriate IT-based solutions to address business challenges and opportunities. They will learn how to conduct requirements analysis, approach the design or redesign of business processes, communicate designs decisions to various levels of management, and work in a project-based environment. Lecture 3 (Spring). |
| MGIS-760 | Integrated Business Systems 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) Lecture 3 (Spring). |
| STAT-641 | Applied Linear Models - Regression A course that studies how a response variable is related to a set of predictor variables. Regression techniques provide a foundation for the analysis of observational data and provide insight into the analysis of data from designed experiments. Topics include happenstance data versus designed experiments, simple linear regression, the matrix approach to simple and multiple linear regression, analysis of residuals, transformations, weighted least squares, polynomial models, influence diagnostics, dummy variables, selection of best linear models, nonlinear estimation, and model building. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring). |
| STAT-745 | Predictive Analytics This course is designed to provide the student with solid practical skills in implementing basic statistical and machine learning techniques for the purpose of predictive analytics. Throughout the course, many real world case studies are used to motivate and explain the strengths and appropriateness of each method of interest. In those case studies, students will learn how to apply data cleaning, visualization, and other exploratory data analysis tools to a variety of real world complex data. Students will gain experience with reproducibility and documentation of computational projects and with developing basic data products for predictive analytics. The following techniques will be implemented and then tested with cross-validation: regularization in linear models, regression and smoothing splines, k-nearest neighbor, and tree-based methods, including random forest. (Prerequisite: This class is restricted to students in APPSTAT-MS and SMPPI-ACT who have successfully completed STAT 611 and STAT-741 or equivalent courses.) Lecture 3 (Spring). |
| STAT-747 | Principles of Statistical Data Mining This course covers topics such as clustering, classification and regression trees, multiple linear regression under various conditions, logistic regression, PCA and kernel PCA, model-based clustering via mixture of gaussians, spectral clustering, text mining, neural networks, support vector machines, multidimensional scaling, variable selection, model selection, k-means clustering, k-nearest neighbors classifiers, statistical tools for modern machine learning and data mining, naïve Bayes classifiers, variance reduction methods (bagging) and ensemble methods for predictive optimality. (Prerequisites: This class is restricted to students in APPSTAT-MS or SMPPI-ACT who have successfully completed STAT-611, STAT-731 and STAT-741 or equivalent courses.) Lecture 3 (Fall, Spring). |
| STAT-773 | Time Series Analysis and Forecasting This course is designed to provide the student with a solid practical hands-on introduction to the fundamentals of time series analysis and forecasting. Topics include stationarity, filtering, differencing, time series decomposition, time series regression, exponential smoothing, and Box-Jenkins techniques. Within each of these we will discuss seasonal and nonseasonal models. (Prerequisites: This class is restricted to students in APPSTAT-MS or SMPPI-ACT who have successfully completed STAT-741 or equivalent course.) Lecture 3 (Fall, Spring). |
| STAT-784 | Categorical Data Analysis The course develops statistical methods for modeling and analysis of data for which the response variable is categorical. Topics include: contingency tables, matched pair analysis, Fisher's exact test, logistic regression, analysis of odds ratios, log linear models, multi-categorical logit models, ordinal and paired response analysis. (Prerequisites: This class is restricted to students in APPSTAT-MS or SMPPI-ACT who have successfully completed STAT-741 or equivalent course.) Lecture 3 (Fall, Spring). |
Admission Requirements
To be considered for admission to the MS program in business analytics, candidates must fulfill the following requirements:
- Complete an online graduate application. Refer to Graduate Admission Deadlines and Requirements for information on application deadlines, entry terms, and more.
- Submit copies of official transcript(s) (in English) of all previously completed undergraduate and graduate course work, 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.
- Not all programs require the submission of scores from entrance exams (GMAT or GRE). Please refer to the Graduate Admission Deadlines and Requirements page for more information.
- Submit a personal statement of educational objectives. Refer to Application Instructions and Requirements for additional information.
- 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 requirements. International applicants may be considered for an English test requirement waiver. Refer to Additional Requirements for International Applicants to review waiver eligibility.
For further information about tips on personal statements and additional guidance on how to submit a successful application, please visit Saunders College of Business Admissions Requirements.
Learn about admissions, cost, and financial aid
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