Immersive Experience Leveraging Data to Drive Business Decisions
Designed for business analysts, data scientists, or others looking to transition into roles as data-driven business analysts or consultants, the Master of Science in Applied Business Analytics at Boston University’s Metropolitan College (MET) can help you harness the concepts, techniques, and tools needed to transform available data into a valuable catalyst for business growth.
According to a recent report by McKinsey, data needs to be treated as one of the products produced within the organization rather than as a raw material that supports decisions. As data-driven business models change the face of industry, developing the ability to capitalize upon this valuable resource is compulsory.
Program at a Glance
- Top 10 Online Program
- Online and On Campus
- Part-Time or Full-Time Study
- STEM Designated
- 40 Credits
- 12–20 Months to Completion
- 12 Core Faculty
- No GRE/GMAT
Advance Your Career in Applied Business Analytics
In a recent analysis, PwC determined that 67 percent of job openings requiring data science and analytics competencies are for “analytics-enabled” roles, such as chief executive officer, chief data officer, director of IT, human resources manager, financial manager, and marketing manager. While the jobs are there, there is a shortage of analytics-enabled personnel who combine expertise in their industry with the ability to understand and leverage big data to engage in predictive analytics or make data-informed business decisions. To help close this skills gap, BU MET offers the master’s in Applied Business Analytics degree program on campus and online. Graduates of the program have gone on to work at organizations such as Google, Boeing, Amazon, Microsoft, Apple, Viacom, State Street, and Fidelity.
#1 Best Business Analytics & Intelligence Programs of 2021
BU MET’s online Applied Business Analytics master’s program is ranked #1 in the nation for 2021 by BestColleges.com.Learn More
#5 Best Online Master’s in Business Analytics of 2022
BU MET’s MS in Applied Business Analytics is ranked #5 (and Best in Northeast) Best Online Master’s in Business Analytics Degree Programs for 2022 by Intelligent.com.Learn More
#10, Best Online Master's in Business Programs (Excluding MBA)
MET’s online master’s degrees in management are ranked #10 in the nation by U.S. News & World Report for 2022.Learn More
“From my perspective, the most important factor that distinguishes BU MET’s programs in Applied Business Analytics is the element of working through problems as they would be explored in reality. Although solving textbook problems can be a good tool to use, MET has an inclination to use powerful tools and projects that allow a problem and process of problem solving to be brought into the classroom.
There are no classes that will leave a student to wonder where they will apply the concepts they are learning. Since all instructors are experts in their craft, and working professionals, students get to work with tangible perspectives and problems. This is a great way to develop confident leaders.
Students are challenged from the perspective of a decision-maker in many courses. The need to apply critical thinking, and to work through problems with uncertainty, becomes a muscle that is exercised until students become comfortable in leadership situations. These situations are ones in which information needs to be processed and creatively applied to projects that represent the way it would be done in an actual situation. Practice is primary and theory plays a supporting role with many of the courses, which is highly valuable from my experience.”—Roman Rabinovich (MET’15), Vice President of Business Development, Eventige Media Group; Lecturer, Administrative Sciences
Explore Careers in Applied Business Analytics
Use the Career Insights tool to explore jobs that are the right fit for you. Filter by career area and job title or by industry sector to explore employment demand and average salaries. Select “Learn More” for a downloadable career report, or “Explore Other Options” to find the BU MET degree or certificate program that will prepare you for the job you want.
Why BU’s Applied Business Analytics is Ranked in the Top 10
- Active Learning Environment: BU MET’s Applied Business Analytics program focuses on practical, hands-on education, ensuring you develop expertise in integrating the “5 Vs of big data”—volume, velocity, variety, veracity, and value of information—into sound analytics-based business decisions.
- Engaged Faculty: In BU MET’s Applied Business Analytics master’s program, you benefit from working closely with highly qualified faculty who draw from active research and substantial professional achievements in areas such as descriptive, predictive, and prescriptive analytics.
- Extensive Network: Study analytics alongside peers with solid business experience, learn from faculty who have valuable contacts within the business analytics field, and benefit from an alumni community with strong professional connections.
- Complimentary Analytics Labs: Two levels of preparatory analytics laboratories offer access to advanced tools and provide opportunities to hone analytics skills using cases that are populated with realistic data.
- STEM Designated: Eligible graduates on student visas have access to an Optional Practical Training (OPT) of 12 months and an extension for up to 24 additional months.
- 15:1 Class Ratio: Enjoy an exceptional student-to-instructor ratio, ensuring close interaction with faculty and access to support.
- Advisory Board: The ABA advisory board actively engages with students, alumni, and industry veterans. It provides access to the latest industry trends in data science and business analytics and facilitates learning about career pathways through seminars and talks.
- Valuable Resources: Make use of Boston University’s extensive resources, including the Center for Career Development, Educational Resource Center, Fitness & Recreation Center, IT Help Centers, Mugar Memorial Library, Center for Antiracist Research, Howard Thurman Center for Common Ground, George Sherman Union, and many others.
- Flexible Options: Study at the pace that works for you, evenings on campus or fully online. Courses begin fall, spring, and summer; online courses have two starts per term.
- Track Record: Learn from the best—since 2014, BU MET’s part-time master’s programs in business and management have been ranked among the top in the nation by U.S. News & World Report.
- Merit Scholarships: All applicants are automatically considered, and admitted students are nominated based on eligibility.
Master the Analytics Tools to Excel in Business
Offered through BU MET’s Department of Administrative Sciences, the Master of Science in Applied Business Analytics (MSABA) provides a unique combination of dynamic academic curriculum, the latest educational technologies, flexible delivery modes, advanced pedagogy, and professional contacts within the business analytics industry.
In this business analytics master’s degree program, you will gain hands-on experience with a variety of analytical models and decision-support tools that you can apply to interlinked data-inputs and large data sets in the areas of marketing, operations, product and technology innovations, financial services, and others. Boston University’s applied business analytics curriculum covers advanced software tools and functions, such as descriptive, predictive, and prescriptive modeling; text and data mining; visual analytics; and business simulations. Graduates of the program will be able to analyze data-driven business processes, select appropriate analytical methods to monitor and identify performance trends, prescribe possible outcomes, and propose optimal data-driven solutions.
Graduate Ready to Transform Business Using Data
Metropolitan College’s Applied Business Analytics master’s degree will equip you with:
- The knowledge and skills necessary to better utilize available information in operational, tactical, and strategic decision-making in organizations.
- Experience with various powerful emerging technologies and techniques for increasing the value of both in-house and third-party data sets.
- An understanding of how organizations are using interlinked data-inputs, analytics models, and decision-support tools to better understand their operations, customers, and markets.
- Expertise in web analytics and metrics, and the ability to procure and process unstructured text, and delve into hidden patterns within data sets.
- The ability to facilitate knowledge discovery using data mining and visualization techniques over vast amounts of data.
Access to Analytics Laboratories
As a student in BU MET’s MS in Applied Business Analytics program, you have free access to hands-on analytics preparatory laboratories offered through the Department of Administrative Sciences. Our self-paced laboratories (SPLs) are organized in two levels:
- Level 1 consists of the following two required prerequisite labs for our ABA programs: Open to all MET students, Level 1 labs include Pre-Analytics Laboratory (AD 100) and Introduction to R for Business (ADR 100).
- Level 2 comprises intermediate knowledge-based labs where students learn how to work with professional tools or approaches, or to introduce professional software applications not covered in the graduate program but used in the industry. These labs include: Business Analytics Tools and Applications (AD 200) and Business Analytics with R (ADR 200).
In these specially designed SPLs, you will be exposed to cloud-based educational tools, software applications, and databases, along with pre-recorded internal and/or external tutorials, lectures, and video conferencing. You will have the flexibility to build your own path through the learning units and to proceed by completing assignments in a “learn and test yourself” mode—at your own pace. Upon successful completion of an SPL, you earn a standardized, digitally verifiable badge in recognition of your performance and visibility to current and future employers.
BU MET graduate certificate programs can serve as building blocks to a master’s degree. The Graduate Certificate in Applied Business Analytics shares specific courses with the master’s in Applied Business Analytics program, giving you the option to take the certificate on your path to a master’s degree. To be eligible for the degree, you must apply for admission and be accepted into the degree program. Consult with a graduate admissions advisor to learn more about this option.
MS in Applied Business Analytics Curriculum
A total of 40 credits is required.
Students must complete the degree core courses, specialization courses, and electives as indicated*. Students who already hold the Graduate Certificate in Applied Business Analytics may waive the core course (Business Analytics Foundations) and three of the specialization courses.
Prior to or during the course MET AD 571 Business Analytics Foundations, students are required to complete AD 100 Pre-Analytics Laboratory and ADR 100 Introduction to R for Business. These are resources that will introduce students to the software environments we use throughout the program. Some courses may have additional prerequisites.
Degree Core Courses
(Four courses/16 credits)
MET AD 571 Business Analytics Foundations
Prereq: AD100 Pre-Analytics Laboratory and ADR100 Introduction to R
This course presents fundamental knowledge and skills for applying business analytics to managerial decision-making in corporate environments. Topics include descriptive analytics (techniques for categorizing, characterizing, consolidating, and classifying data for conversion into useful information for the purposes of understanding and analyzing business performance), predictive analytics (techniques for detection of hidden patterns in large quantities of data to segment and group data into coherent sets in order to predict behavior and trends), prescriptive analytics (techniques for identification of best alternatives for maximizing or minimizing business objectives). Students will learn how to use data effectively to drive rapid, precise, and profitable analytics-based decisions. The framework of using interlinked data inputs, analytics models, and decision-support tools will be applied within a proprietary business analytics shell and demonstrated with examples from different functional areas of the enterprise. R, SQL, and Power BI software are used in this course. [ 4 cr. ]
|SA1||IND||Kim||FLR 152||MW||1:00 pm – 4:30 pm|
|SB1||IND||Ritt||FLR 123||MW||6:00 pm – 9:30 pm|
|A1||IND||Ritt||CAS 222||M||2:30 pm – 5:15 pm|
|A2||IND||Orunkhanov||CAS 229||M||6:00 pm – 8:45 pm|
|A3||IND||Staff||CAS B06A||T||12:30 pm – 3:15 pm|
|A4||IND||Ritt||SAR 104||T||6:00 pm – 8:45 pm|
|A5||IND||Kim||EPC 203||W||2:30 pm – 5:15 pm|
|A6||IND||Ritt||COM 217||W||6:00 pm – 8:45 pm|
MET AD 605 Operations Management: Business Process Fundamentals
This course will provide students with the analytical tools to analyze, manage, and improve manufacturing, service, and business processes. Coverage includes various options to lower operational costs and improve responsiveness to customers' needs, including operating system design, product & service design, capacity analysis & buffering, waiting line optimization, and process quality analysis using statistical approaches. Quantitative methods include application of stochastic simulation, analysis of random outcomes, statistical analysis routines (confidence intervals, hypothesis testing, machine learning), system reliability analysis, and statistical process control. The Deming philosophy of management, Lean operations principles, and Six Sigma process improvement methodologies form the underlying foundation of the course coverage. [ 4 cr. ]Sum1 2022
|SA1||IND||Cashton||MET 122||MW||6:00 pm – 9:30 pm|
|A1||IND||Maleyeff||MET 101||M||9:05 am – 11:50 am|
|A2||IND||Staff||STH B22||T||6:00 pm – 8:45 pm|
|A3||IND||Courtney||CAS B06A||W||6:00 pm – 8:45 pm|
|A4||IND||Cashton||KCB 102||W||2:30 pm – 5:15 pm|
|A5||IND||Courtney||STH B20||R||12:30 pm – 3:15 pm|
MET AD 632 Financial Concepts
Introduction to the concepts, methods and problems of accounting and financial analysis. Includes accounting principles, measurement and disclosure issues, financial statement analysis, time value of money, cash flow projection and analysis, capital budgeting and project evaluation, bond and equity valuation, cost of capital and capital structure. 4 cr. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Critical Thinking. [ 4 cr. ]
|SA1||IND||Mcgue||MET 122||TR||1:00 pm – 4:30 pm|
|SA2||IND||Mendlinger||CAS 227||TR||6:00 pm – 9:30 pm|
|A1||IND||Kanza||MCS B37||M||6:00 pm – 8:45 pm|
|A2||IND||Mendlinger||CAS B06B||T||6:00 pm – 8:45 pm|
|A3||IND||Mcgue||STH B19||W||6:00 pm – 8:45 pm|
|A4||IND||Kanza||PSY B53||W||6:00 pm – 8:45 pm|
|A5||IND||Mcgue||COM 213||R||12:30 pm – 3:15 pm|
|A6||IND||Staff||COM 217||R||6:00 pm – 8:45 pm|
|A7||IND||Mcgue||CGS 515||F||11:15 am – 2:00 pm|
MET AD 715 Quantitative and Qualitative Decision-Making
The purpose of this course is to help improve business problem solving and managerial decision-making through the use of quantitative and qualitative decision-making tools and techniques. This course will provide the student with an overview of how decisions are made to solve management problems in the business environment. It introduces the fundamental concepts and methodologies of the decision-making process, problem-solving, decision analysis, data collection, probability distribution, evaluation, and prediction methods. Students will learn how to apply different quantitative and qualitative analytical tools commonly used in business to provide a depth of understanding and support to various decision-making activities within each subject area of management. Through the use of case studies of decisions made by managers in various production and service industries and a business simulation package specifically prepared for this course, the scope and breadth of decision-making in business will be described. [ 4 cr. ]Sum1 2022
|SA1||IND||Dickson||PSY B51||MW||6:00 pm – 9:30 pm|
|SB1||IND||Zlatev||MET 101||TR||1:00 pm – 4:30 pm|
|A1||IND||Dickson||CGS 515||T||12:30 pm – 3:15 pm|
|A2||IND||Dickson||PSY B55||T||6:00 pm – 8:45 pm|
|A3||IND||Zlatev||MET 101||W||9:05 am – 11:50 am|
|A4||IND||Staff||STH B20||W||6:00 pm – 8:45 pm|
|A5||IND||Staff||MET 101||R||9:00 am – 11:45 am|
|A6||IND||Lindley||FLR 121||R||6:00 pm – 8:45 pm|
|A7||IND||Staff||HAR 316||M||2:30 pm – 5:15 pm|
|A8||IND||Staff||PSY B51||M||6:00 pm – 8:45 pm|
(Four courses/16 credits)
MET AD 616 Enterprise Risk Analytics
The course offers an overview of the key current and emerging enterprise risk analytical approaches used by corporations and governmental institutions and is focused on understanding and implementing the enterprise risk management framework on how to leverage the opportunities around a firm to increase firm value. The major risk categories of the enterprise risk management such as financial risk, strategic risk, and operational risk will be discussed and risk analytics approaches for each of these risks will be covered. Students will learn how to use interlinked data inputs, analytics models, business statistics, optimization techniques, simulation, and decision-support tools. An integrated enterprise risk analytics approach will be demonstrated with examples from different functional areas of the enterprise. R, SQL, and Power BI software are used in this course. [ 4 cr. ]
|SB1||IND||Ritt||MET 122||TR||6:00 pm – 9:30 pm|
|A1||IND||Kim||STH B22||T||12:30 pm – 3:15 pm|
|A2||IND||Ritt||BRB 122||T||6:00 pm – 8:45 pm|
MET AD 654 Marketing Analytics
Become familiar with the foundations of modern marketing analytics and develop your ability to select, apply, and interpret readily available data on customer purchase behavior, new customer acquisition, current customer retention, and marketing mix optimization. This course explores approaches and techniques to support the managerial decision-making process and skills in using state-of-the- art statistical and analytics tools. Students will have an opportunity to gain a basic understanding of how transaction and descriptive data are used to construct customer segmentation schemas, build and calibrate predictive models, and quantify the incremental impact of specific marketing actions. Python, R, SQL, and Power BI software are used in this course. [ 4 cr. ]
|SA1||IND||Page||HAR 220||MW||6:00 pm – 9:30 pm|
|A1||IND||Page||STH 113||W||2:30 pm – 5:15 pm|
|A2||IND||Page||CAS 227||W||6:00 pm – 8:45 pm|
MET AD 688 Web Analytics for Business
Prereq AD100, ADR100, AD571
Explore web analytics, text mining, web mining, and practical application domains. The web analytics part of the course studies the metrics of websites, their content, user behavior, and reporting. The Google Analytics tool is used for the collection of website data and doing the analysis. The text mining module covers the analysis of text including content extraction, string matching, clustering, classification, and recommendation systems. The web mining module presents how web crawlers process and index the content of websites, how search works, and how results are ranked. Application areas mining the social web and game metrics will be extensively investigated. R, SQL, and Power BI software are used in this course. [ 4 cr. ]
|SA1||IND||Dickson||CAS 426||TR||6:00 pm – 9:30 pm|
|A1||IND||Harris||EPC 203||M||2:30 pm – 5:15 pm|
|A2||IND||Dickson||COM 215||M||6:00 pm – 8:45 pm|
MET AD 699 Data Mining for Business Analytics
Enterprises, organizations, and individuals are creating, collecting, and using a massive amount of structured and unstructured data with the goal to convert the information into knowledge, improving the quality and the efficiency of their decision-making process, and better positioning themselves in the highly competitive marketplace. Data mining is the process of finding, extracting, visualizing, and reporting useful information and insights from both small and large datasets with the help of sophisticated data analysis methods. It is part of business analytics, which refers to the process of leveraging different forms of analytical techniques to achieve desired business outcomes through requiring business relevancy, actionable insight, performance management, and value management. The students in this course will study the fundamental principles and techniques of data mining. They will learn how to apply advanced models and software applications for data mining. Finally, students will learn how to examine the overall business process of an organization or a project with the goal to understand (i) the business context where hidden internal and external value is to be identified and captured, and (ii) exactly what the selected data mining method does. R, SQL, and Power BI software are used in this course. [ 4 cr. ]
|SB1||IND||Page||MET 101||MW||6:00 pm – 9:30 pm|
|A1||IND||Page||STH B22||R||12:30 pm – 3:15 pm|
|A2||IND||Page||CAS 208||R||6:00 pm – 8:45 pm|
(Two courses/8 credits)
Choose two additional Administrative Sciences graduate-level courses, with the advice of an Administrative Sciences department advisor.
The following courses offered by other Metropolitan College departments are some of the elective courses allowed with advisor approval:
MET AD 587 Interdisciplinary Methods for Quantitative Finance
This course expands upon the foundations of finance theory with interdisciplinary approaches from statistical physics and machine learning. The course equips the students with the Python tools to tackle a broad range of problems in quantitative financial analysis and combines the study of relevant financial concepts with computational implementations. Students will learn to use packages like Numpy, Pandas, Statsmodels and Scikit, which are commonly used in research and in the industry. Prerequisites: MET AD 685 or PY 355 or equivalent or consent by the instructor. [ 4 cr. ]
MET CS 521 Information Structures with Python
This course covers the concepts of the object-oriented approach to software design and development using Python. It includes a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, arrays and strings, and proceeding to advanced topics such as inheritance and polymorphism, creating user interfaces, exceptions and streams. Upon completion of this course students will be able to apply software engineering principles to design and implement Python applications that can be used in with analytics and big data. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation, Critical Thinking. [ 4 cr. ]
|SC1||IND||Burstein||CAS 326||T||6:00 pm – 9:30 pm|
|SC2||IND||Orsini||CAS 116||W||6:00 pm – 9:30 pm|
|A1||IND||Lu||EPC 204||M||6:00 pm – 8:45 pm|
|A2||IND||Pinsky||CAS 426||W||8:00 am – 10:45 am|
|A3||IND||Staff||CAS 426||W||6:00 pm – 8:45 pm|
|A4||IND||Staff||HAR 212||R||6:00 pm – 8:45 pm|
MET CS 555 Foundations of Machine Learning
Formerly titled CS 555 Data Analysis and Visualization with R.
This course provides an overview of the statistical tools most commonly used to process, analyze, and visualize data. Topics include simple linear regression, multiple regression, logistic regression, analysis of variance, and survival analysis. These topics are explored using the statistical package R, with a focus on understanding how to use and interpret output from this software as well as how to visualize results. In each topic area, the methodology, including underlying assumptions and the mechanics of how it all works along with appropriate interpretation of the results, are discussed. Concepts are presented in context of real world examples. Recommended Prerequisite: MET CS 544 or equivalent knowledge, or instructor's consent. [ 4 cr. ]
|SB1||IND||Raghu||MET 122||MW||6:00 pm – 9:30 pm|
|A1||IND||Staff||EPC 208||M||6:00 pm – 8:45 pm|
|A2||IND||Alizadeh-Sha||CAS 213||R||6:00 pm – 8:45 pm|
|A3||IND||Alizadeh-Sha||MET 122||R||9:00 am – 11:45 am|
MET CS 579 Database Management
This course provides a theoretical yet modern presentation of database topics ranging from Data and Object Modeling, relational algebra and normalization to advanced topics such as how to develop Web-based database applications. Other topics covered - relational data model, SQL and manipulating relational data; applications programming for relational databases; physical characteristics of databases; achieving performance and reliability with database systems; object- oriented database systems. Prereq: MET CS 231 or MET CS 232; or instructor's consent. Restrictions: This course may not be taken in conjunction with MET CS 469 (undergraduate) or MET CS 669. Refer to your Department for further details. [ 4 cr. ]Sum1 2022
|SC1||IND||Russo||HAR 316||W||6:00 pm – 9:30 pm|
|SEX||IND||Russo||HAR 316||W||6:00 pm – 9:30 pm|
|A1||IND||Lee||PHO 205||T||6:00 pm – 8:45 pm|
MET CS 669 Database Design and Implementation for Business
Students learn the latest relational and object-relational tools and techniques for persistent data and object modeling and management. Students gain extensive hands- on experience using Oracle or Microsoft SQL Server as they learn the Structured Query Language (SQL) and design and implement databases. Students design and implement a database system as a term project. Restrictions: This course may not be taken in conjunction with MET CS 469 (undergraduate) or MET CS 579. Only one of these courses can be counted towards degree requirements. [ 4 cr. ]Sum1 2022
|SC1||IND||Matthews||CAS 426||W||6:00 pm – 9:30 pm|
|A1||IND||Maiewski||CAS 201||W||6:00 pm – 8:45 pm|
|A2||IND||Russo||CAS 235||R||6:00 pm – 8:45 pm|
|A3||IND||Matthews||HAR 212||T||6:00 pm – 8:45 pm|
|E1||IND||Matthews||HAR 212||T||6:00 pm – 8:45 pm|
MET CS 677 Data Science with Python
Students will learn major Python tools and techniques for data analysis. There are weekly assignments and mini projects on topics covered in class. These assignments will help build necessary statistical, visualization and other data science skills for effective use of data science in a variety of applications including finance, text processing, time series analysis and recommendation systems. In addition, students will choose a topic for a final project and present it on the last day of class. Prerequisite: MET CS 521 or equivalent. Or, instructor's consent. [ 4 cr. ]Sum1 2022
|SC1||IND||Alizadehshab||CAS 233||R||6:00 pm – 9:30 pm|
|A1||IND||Staff||CAS 226||M||6:00 pm – 8:45 pm|
|A2||IND||Enxing||CAS 233||T||6:00 pm – 8:45 pm|
|A3||IND||Pinsky||EPC 208||W||6:00 pm – 8:45 pm|
*Degree requirements may vary for those students transferring credits from previous coursework at Boston University or receiving course waivers due to professional designations.
Applied Business Analytics Faculty
Associate Professor of the Practice Director of Digital Learning, Administrative Sciences Coordinator, Applied Business Analytics
View All Faculty
- Aidar Orunkhanov
Lecturer, Administrative Sciences
Director, Customer & Strategic Analytics, PTC
MA, University of Illinois; BA, Ohio Wesleyan University
- Roman Rabinovich
Lecturer, Administrative Sciences
Data Strategy Partner, Decile
PhD, Grenoble School of Management, France; MS, Boston University; BA, Pace University
- Patrick Tyson
Lecturer, Administrative Sciences
Data Scientist, Prime Therapeutics
MS, Boston University; BA, University of Minnesota–Twin Cities