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MS Business Analytics Overview

View our: Core Courses | Elective Courses

The Master of Science in Business Analytics (MSBA) degree is a professional degree for students wishing to pursue careers management with a strong quantitative and data analysis training.  Modern management professionals and business data analysts increasingly need significant mathematical, statistical and computational knowledge to understand and manage data available to business and government enterprises, and to utilize that understanding in making optimal quantitative decisions using mathematical models.  The MSBA program is structured to provide and build not only mathematical and statistical skills such as quantitative modeling, operations management, data mining and simulation and relevant computational skills such as big data, network and infrastructure management, but also business and managerial skills and domain knowledge to apply the technical skills in a business environment.

Specifically, the MSBA program offered by the Robert H. Smith School of Business provides students with:

  • comprehensive training in foundations and methodology of quantitative managerial analysis;
  • comprehensive training in data analysis and data-based managerial decision making;
  • an in-depth training on methods and tools of contemporary data analytics and big data;
  • strong background on spreadsheet based modeling and optimization fundamentals and techniques;
  • good understanding of  modern computational data analysis techniques such as data mining, Monte Carlo and discrete event simulation, and network analytics;
  • strong hands-on training in data handling and data base management;
  • mastery of the contemporary software used for managerial quantitative and data analysis including web based software and tools.

Learning Outcomes

  • Students will demonstrate a clear understanding of the fundamental concepts of Statistics, Data Analysis, Quantitative Modeling, Simulation, and Optimization.
  • Students will demonstrate proficiency in the practical tools and techniques of modern Business Analytics  
  • Students will demonstrate written and oral communication skills through class participation and group presentations.
  • Students will demonstrate their ability to work effectively with other members of a team in the preparation of a group project.

Core Courses

BUSI 630 Data, Models, and Decisions (3): Introduces students to analytical techniques that establish the optimality of managerial decisions via empirical (“data models”) and logical (“decisions”) means. The course may be viewed as consisting of two integrated parts. In the first part, various methods of analyzing data, including regression analysis are studied. The second part covers models for making optimal decisions in situations characterized by either an absence of uncertainty or where the uncertainty arises from non-competitive sources.

BUDT 732 Decision Analytics (3):  This course explores basic analytical principles that can guide a manager in making complex decisions. It focuses on two advanced analytics techniques: optimization, dealing with design and operating decisions for complex systems, and simulation, dealing with the analysis of operating decisions of complex systems in an uncertain environment. The course provides students with a collection of optimization and simulation modeling and solution tools that can be useful in a variety of industries and functions. The main topics covered are linear, integer, and nonlinear optimization applications in a wide variety of industry segments, and Monte-Carlo Simulation and risk assessment.  Application-oriented cases are used for developing modeling and analytical skills, and to simulate decision-making in a real-world environment.

BUDT 733 Data Analytics (3): Increasingly, governments and businesses are collecting more and more data. Examples include the Internet, point-of-sale devices, medical databases, search engines, and social networks. The increased data availability coupled with cheap computing power provides us with an unprecedented opportunity to use sophisticated data-driven mathematical models to achieve many important goals and/or gain a competitive edge. This course gives an overview of the data-mining process, from data collection, through data modeling and analytical algorithms, to data-driven decision making. The focus is on introducing data-mining algorithms such as logistic regression, classification trees and clustering, and their application to real-world data, as well as introducing some of the more recent developments in the field such as ensemble methods.

BUDT 704 Database Management Systems (3): Provides fundamental concepts and skills necessary for designing, building, and managing business applications which incorporate database management systems as their foundation. Topics covered include the fundamentals of database management (DBMS) technology, alternative methods for modeling organizational data, the application of delivering data through Web-based and other graphical interfaces. Non-majors should review their registration eligibility in the statement preceding the BUDT courses.

Students will take up to 12 credits of core courses, and complete a total of 30 credits to graduate.

Elective Courses

Elective courses include but are not limited to the following:

BUDT 758K Computer Simulation for Business Applications (3): This course covers the basic techniques for computer simulation modeling and analysis of discrete-event systems. Course emphasis is on conceptualizing abstract models of real-world systems (for example, inventory and queuing systems), implementing simulations in special purpose software, planning simulation studies, and analyzing simulation output. Some mathematical theory will be covered.

BUDT 758X Big Data: Strategy, Management and Applications (3): Digitization is occurring in every aspect of business and our daily lives, generating a huge amount of data.  Big data represents unprecedented opportunities for companies to generate insights to improve products and services and contribute to the bottom line. At the same time, much of the big data is unstructured, in real time and only loosely connected. It defies the traditional ways of managing databases. This creates challenges even to tech-savvy companies on how to leverage the big data to gain competitive advantage. This course provides cutting edge knowledge about various aspects of big data, including: how to identify strategic values of big data, major types of big data, methods to capture and store big data, analytical tools for big data, and pitfalls to avoid in formulating a big data strategy.  In the end of the course, students will have a comprehensive understanding of important business issues related to big data, and be able to successfully design and implement big data strategy.

BUDT 758X Price Optimization and Revenue Management (3): Revenue (or yield) management (RM) first emerged in the post-deregulation US airline industry, and hit the jackpot in the mid 90's with American Airlines RM scoring $1 billion annual incremental revenues. The business strategy reformed the entire transportation and tourism industry, as well as telecommunications, broadcasting, ticketing, healthcare, fashion, manufacturing etc. Recently RM evolved to a new dimension with internet companies practicing dynamic and targeted pricing or auctions for products, services or advertisement slots.  This course that specializes on dynamic price optimization and revenue management is meant to provide students with the right bundle of tools and principles, drawn from several disciplines  in order to maximize profits. The RM solution integrates pricing with sales and inventory management strategies. The first part of the course addresses pricing issues such as pricing under various constraints, non-linear pricing, markdown pricing. The second part of the course provides tools and methods for combined pricing and capacity management decisions from an operational perspective.

BUDT 758F Google Online Challenge Analytics (3): This course is a hands-on learning-by-doing course. Students will design, develop, and implement sponsored search strategies for real-world clients are part of the Google Online Challenge. Students will work in teams of 4 or 5, spend real advertising dollars to run a sponsored-search advertising campaign for their client. In conjunction with the client, students will also develop digital and social media strategies that complement and support their sponsored search advertising campaigns on Google. The teams will also learn to use analytical tools to analyze the performance of their campaigns and provide guidelines to the client for future campaigns. This “real-time, real-business, real-money” challenge provides a valuable opportunity for students to gain a first-hand experience with online advertising and benefit from the immediate campaign performance feedback. At the end of this course, a student should feel comfortable developing and implementing digital strategies and executing online campaigns for firms. They should know all the key terminology and theories of the field and have a good idea of how things work below the surface.

BUDT 758X Healthcare Analytics (3): This class will focus on some of the key aspects of conducting analysis and applying the results in the health care system. The course will a) discuss the business of health care, payment systems and insurance b) discuss health care data, privacy and HIPAA, and c) explore successful  implementations of analytics in healthcare settings. Various applications of healthcare analytics will be discussed, focusing on costs, operations, quality, equity, and access.

BUDT 758X Operations Analytics (3): This course explores analytical methods, tools and strategies that can enable firms to achieve effective and sustainable operations. The course covers a mix of qualitative and quantitative problems and issues confronting operations managers. The first part of the course focuses on analytics that measure the performances of business operations, explaining how to measure key process parameters like capacity and lead time and analyze the impact of variability on business processes. The second part of the course focuses on analytics that improve the performances of business operations, examining analytics in quality management as well as recent moves toward lean operations. The course also includes a module on inventory analytics with applications in pricing and revenue management. Throughout the course various operations analytics applied to real operational challenges are illustrated. The aim is to provide both tactical knowledge and high-level insights of operations analytics needed by general managers and management consultants. It is also demonstrated how companies can use operational principles from to significantly enhance their competitiveness.

BUDT 758X Capstone Project in Operations Analytics (3): This course gives students an opportunity to apply the knowledge and  skills they learned in the program on real world operational data through quantitative analysis with use of statistical models and the application of modeling and optimization techniques.  Students form teams of 4-5 members and pursue an operational improvement project under the supervision of the instructor. The project groups are expected to suggest operational and business improvements and solutions based on analytical techniques and methods for the case they are analyzing.   

Course Delivery

The MS in Business Analytics program is designed to be completed in 16 months. International students who require student (F-1) visas should plan on completing the program in no more than 16 months.. Classes will be taught in the afternoons and evenings in Van Munching Hall in College Park, MD.

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