Online Master of Science in Applied Business Analytics Degree
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.
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, Boston University’s Metropolitan College offers an online Master of Science in Applied Business Analytics Management degree program. Students have the opportunity to gain hands-on experience with a variety of analytical models and decision-support tools, which they 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 issues, prescribe possible outcomes, and propose optimal data-based solutions.
Students who complete the master’s degree in Applied Business Analytics will be able to demonstrate:
- 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.
Awards & Accreditations
Accredited member of AACSB International―The Association to Advance Collegiate Schools of Business (through BU’s Questrom School of Business)
Newsweek magazine ranked Boston University’s online programs #4 in the nation in its 2023 survey.
Why Choose BU’s Master of Science in Applied Business Analytics?
- The Applied Business Analytics program offers the flexibility of online or on-campus study formats, ensuring that students can earn their degree the way that suits them the best.
In 2025, Metropolitan College’s online master’s degrees in management were ranked #10 among the Best Online Master's in Business Programs (Excluding MBA) by U.S. News & World Report.
BU MET’s MS in Applied Business Analytics is ranked #2 (and Best in Northeast) Best Online Master’s in Business Analytics Degree Programs for 2024 by Intelligent.com.- Through Boston University’s Questrom School of Business, BU MET is an accredited member of AACSB International―The Association to Advance Collegiate Schools of Business.
- Learning from expert faculty from MET’s Department of Administrative Sciences, students benefit from our unique combination of dynamic academic curriculum, the latest educational technologies, flexible delivery modes, advanced pedagogy, and professional contacts within the business analytics industry.
Meet Dr. Vladimir Zlatev, one of the faculty members you’ll work with in the Applied Business Analytics program.
Career Outlook
Management Analysts
10% increase in jobs through 2032
$95,290 median annual pay in 2022
Financial Analysts
8% increase in jobs through 2032
$96,220 median annual pay in 2022
Market Research Analysts
13% increase in jobs through 2032
$68,230 median annual pay in 2022
Mathematicians and Statisticians
30% increase in jobs through 2032
$99,960 median annual pay in 2022
Operations Research Analysts
23% increase in jobs through 2032
$85,720 median annual pay in 2022
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, at https://www.bls.gov/ooh/math/home.htm (visited September 06, 2023).
Employment for management analysts is projected to grow 10% from 2022-2032, much faster than average for all occupations.
Bureau of Labor Statistics
Tuition & Financial Assistance
Money Matters
Boston University Metropolitan College (MET) offers competitive tuition rates that meet the needs of part-time students seeking an affordable education. These rates are substantially lower than those of the traditional, full-time residential programs yet provide access to the same high-quality BU education. To learn more about current tuition rates, visit the MET website.
Financial Assistance
Comprehensive financial assistance services are available at MET, including scholarships, graduate loans, and payment plans. There is no cost to apply for financial assistance, and you may qualify for a student loan regardless of your income. Learn more.
Curriculum
The online Master of Science in Applied Business Analytics consists of 10 required courses (40 credits).*
Students who already hold the Graduate Certificate in Applied Business Analytics may waive the four specialization courses.
*Degree requirements may vary for those students transferring credits from previous coursework at Boston University or receiving course waivers due to professional designations.
Courses
Students must complete the degree core courses, specialization courses, and electives as indicated.
As a prerequisite to the course MET AD 571 Business Analytics Foundations, students are required to complete MET AD 100 Pre-Analytics Laboratory and MET ADR 100 Introduction to R for Business. Some courses may have additional prerequisites.
Degree Core Courses
(Four courses/16 credits)
METAD571 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 credits]
METAD605 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 credits]
METAD632 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 credits]
METAD715 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 credits]
Specialization Courses
(Four courses/16 credits)
METAD616 Enterprise Risk Analytics
Prereq: METAD571
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 credits]
METAD654 Marketing Analytics
Prereq: METAD571
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 credits]
METAD688 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 credits]
METAD699 Data Mining for Business Analytics
Prereqs: AD100,ADR100,AD571
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 credits]
Elective Courses
(Two courses/8 credits)
Choose two additional Administrative Sciences graduate-level courses, with the advice of the Administrative Sciences department coordinator.
The following courses offered by other Metropolitan College departments are allowed with advisor approval:
METAD599 Introduction to Python and SQL for Business Analytics
Prerequisite: PY100 (Intro to Python)
Python is a modern, high-level programming language. One of the most popular programming languages, its use has steadily increased across a large number of industries. This course introduces students to the Python environment and teaches a solid foundation in the basic syntax and structure. Structured Query Language (SQL) is the most common language globally for interacting with relational databases. Employers have indicated that knowledge of SQL is one of the most important skills for new graduates entering the workforce. Even with advances in database technologies and languages for handling heterogeneous data types, SQL remains the core skill for interacting with data. This course introduces both languages to equip students pursuing an analytics education with the skills necessary to succeed in the analytics and data visualization field. The outcome of this course will be a focused survey of Python and SQL topics designed to equip analytics professionals rather than a deep focus on technical programming topics. [4 credits]
METAD799 Neural Networks for Business Applications
Prereq: MET AD 599 (Introduction to Python and SQL for Business Analytics) or MET CS 521 (Information Structures with Python) or equivalent (e.g., MET AD 587, MET AD 654) or approval by the instructor.
Neural networks have revolutionized business domains. In many tasks, they perform better than traditional predictive models, which quickly become core business analytics components. In line with the market trend, this course aims to equip students with knowledge on business-related implementations of neural networks. The topics covered in this course include neural network architectures, techniques, models, and their business applications in time- series forecasting, sentiment classification, and recommendation system. Each module first explains the core concepts of a neural network model and guides students to write Python scripts. Please note that this course is not for developing neural network models from scratch but for understanding and using the models employed in TensorFlow and Keras for business applications. [4 credits]
METAD899 Capstone Project in Applied Business Analytics
Prereq: at least three of the ABA specialization courses AD616, AD654, AD688, AD699
The Business Analytics Capstone Project provides valuable learning experiences and opportunities to apply a set of techniques, competencies, and procedures acquired after the completion of all core and specialization courses within the MS in Applied Business Analytics program. The purpose of this course is to obtain insights about a business that results in improved data-driven decision- making to create value on different levels of an enterprise. Includes application of statistical, stochastic, and dynamic modeling, data mining, forecasting, and operations research techniques to the analysis of problems of business organization and performance. R, Python, SQL, and Power BI software are used in this course. The solving of real problems facing different size companies are assigned to small teams of students and is overseen by our curriculum advisory board, ABA faculty, and business partners from a range of industries. [4 credits]
METAD587 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 credits]
METCS521 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.
Prerequisite: Programming experience in any language. Or Instructor's consent. [4 credits]
METCS555 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 credits]
METCS579 Database Management
Undergraduate Prerequisites: (METCS231 OR METCS232) or consent of instructor. ; Undergraduate Corequisites: Restrictions: This course may not be taken in conjunction with CS 669 or CS 469 (undergraduate). Only one of these courses can be counted to wards degree requirements. - 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 credits]
METCS669 Database Design and Implementation for Business
Undergraduate Prerequisites: Restrictions: Only for MS CIS. This course may not be taken in conjunc tion with MET CS 469 (undergraduate) or MET CS 579. Only one of these courses can be counted towards degree requirements. - 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 credits]
METCS677 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 credits]
Vladimir Zlatev
Associate Professor of the Practice of Administrative Sciences
PhD, MS, BS, Dresden University of Technology
Krystie Dickson
Lecturer, Administrative Sciences
MS, Boston University
MS, The Arthur Lok Jack Global School of Business, University of the West Indies
BS, University of the West Indies
Benjamin P. Harris
Assistant Professor, Administrative Sciences
PhD, MS, BS, Northeastern University
View all Faculty
Christopher Athaide
Assistant Professor, Administrative Sciences
PhD, Massachusetts Institute of Technology
MS, Rensselaer Polytechnic Institute
BTech, Indian Institute of Technology, Mumbai
Gregory Page
Lecturer
MBA, Massachusetts Institute of Technology
MEd, Harvard University
BA, Stanford University
David Ritt
Lecturer, Administrative Sciences
MS, Boston University; BA, University of Chicago
Aidar Orunkhanov
Lecturer, Administrative Sciences
Data Strategy Partner, Decile
MA, University of Illinois; BA, Ohio Wesleyan University
Roman Rabinovich
Lecturer, Administrative Sciences
Director, Customer & Strategic Analytics, PTC
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
Getting Started
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Master of Science