— amount the market for analytics and business intelligence software grew by in 2019 (Gartner)

In the online master’s in business analytics (MSBA) from Worcester Polytechnic Institute, our students gain more than a skillset—they also cultivate a decision-making mindset.

As the analytics field expands, employers are looking for highly trained experts who not only know how to collect and interpret data but also know how to communicate their insights clearly and persuasively.

  • The demand for professionals with business analytics skills is expected to grow by 10% in the next decade (Bureau of Labor Statistics).
  • More than 60% of data and analytics decisionmakers report expanding their data management and analytics budgets (Forrester).
  • The market for analytics and business intelligence software grew 10.4% in 2019 (Gartner).

The MS in Business Analytics Online builds the advanced quantitative and qualitative analysis competencies professionals need to make data-driven business decisions. Our students graduate with the skills they need to better understand their customers and stay competitive.

The curriculum blends practical and theoretical learning, which is a foundation of WPI and The Business School. Students acquire a deep understanding of data processes while working with the same software and applications used in business environments, such as Tableau, Microsoft Power BI, SPSS, NVivo, Qualtrics, and auto ML tools. They also participate in team-based projects that expand their management abilities and hone their communication skills.

Learning Outcomes

  • Build descriptive, predictive, and prescriptive analytical skills in order to guide data-driven decisions and influence organizational success.
  • Learn advanced methods in machine learning, market research, operations modeling, and optimization analysis.
  • Gain leadership skills to manage teams and oversee projects.
  • Boost communication abilities to effectively present findings and get buy-in from stakeholders.
  • Increase your value to your organization and future employers.

Who Can Benefit From an Online Master’s in Business Analytics?

The WPI online MSBA program welcomes both part-time and full-time students. Our students include:

  • Those who have previous business knowledge, either through their undergraduate degree or job experience, and want to add more technical skills to expand their data-analysis abilities
  • Those who have a degree in a science, technology, engineering, or math (STEM) field who want to increase their business knowledge through a program that speaks the language of tech
  • Anyone who wants to increase their influence in their organization—whether they want to move into a management position or have direct influence on their business’s decisions

View our admissions requirements page to learn what you need to become an MSBA student.

Online Master’s in Business Analytics Curriculum

The online Master of Science in Business Analytics is a fully asynchronous program. It consists of 11 courses totaling 33 credit hours.

Each student completes required core courses and then chooses two specializations from three options: Advanced Business Analytics Methods, Marketing Analytics, and Operations Analytics.

The degree culminates with a final capstone project that connects students from around the world with companies that need solutions to real challenges.

WPI emphasizes project-based learning, and collaborative work is a core element of this degree. Throughout the coursework, faculty also address big-picture issues, such as ethics, security, and sustainability.

Online Master’s in Business Analytics Course Listing

Required Core Courses

Today’s business computing infrastructures are producing the large volumes of data organizations need to make better plans and decisions. This course introduces the processes, technologies, and techniques for organizing, analyzing, visualizing, and interpreting data and information about business operations in a way that creates business value.

During the course, students will study a variety of business decisions that can be improved by analyzing data about customers, sales, and operations, preparing students to be knowledgeable producers and consumers of business intelligence. Students will apply commercially available business intelligence software to develop performance dashboards to facilitate organizational decision-making.

The course explores the technical challenges of organizing, analyzing, and presenting data and the managerial challenges of creating and deploying business intelligence expertise in organizations. The course includes business cases, in-class discussion, and hands-on analyses of business data. It is designed for any student interested in learning about data-driven business performance management and decision-making, including students whose primary focus is data science, IT, marketing, operations, or business management.

This course is designed to provide students with a variety of quantitative tools and techniques useful in modeling, evaluating, and optimizing operation processes. Students are oriented toward the creation and use of spreadsheet models to support decision-making in industry and business.
This course develops the skills business students need for handling data. It focuses on student skills in (1) cleaning and preparing data for analysis, (2) writing SQL queries to access and manipulate data, and (3) ethical uses of data and data privacy issues. It also covers the types of data typically found in organizations, e.g., employee, customer, product, marketing, operations, and financial data.


Students must fulfill two specializations.

Advanced Business Analytics Methods

Complete all three courses

This course explores how machine learning (ML) and artificial intelligence (AI) are applied to solve business problems, to satisfy specific business needs, or to discover new opportunities for businesses.

Applications of ML and AI are constantly evolving across many industries. This course utilizes existing auto ML solutions to address issues identified in business case studies e.g., predicting hospital readmissions, loans likely to default, customer churn. The course covers the machine learning project lifecycle, which includes defining ML project objectives, acquiring and exploring data, modeling using auto ML tools, interpretation of models and communication of outcomes, and implementation and deployment of predictive models in organizations.

This course covers mathematical optimization beyond the foundational concepts of linear programming. Approaching from the perspective of obtaining globally optimal solutions, a variety of optimization problem classes will be addressed. This includes integer programming, nonlinear programming, and stochastic programming. While ensuring an appropriate level of theory, the emphasis of the course will be on the mathematical modeling and computational solution aspects of such problems that may arise in the finance, healthcare, humanitarian, inventory, nonprofit, operations, production, staffing, and supply chain sectors, among others.

Prerequisite: OIE 552, equivalent knowledge about optimization and linear programming, or consent of the instructor

This course is designed to equip students with research methods and tools that are used for marketing decision-making. Students will learn to conduct, use, apply, interpret, and present marketing research in order to become effective decision-makers. The topics covered in this course include problem formulation, research design, data collection methods, data analysis, and presentation of a research plan. This course will be an activity-based course involving design, implementation, and presentation of a marketing research plan.

Basic knowledge of marketing and statistical concepts is assumed.

Marketing Analytics

Complete three courses

Choose between:

This course focuses on the development and marketing of products and services that meet customer needs. Topics covered include management and the development of distinctive competence, segmentation and target marketing, market research, competitor analysis and marketing information systems, product management, promotion, pricing strategy, and channel management.

Students will learn how the elements of marketing strategy are combined in a marketing plan based on marketing analytics and the challenges associated with managing products and services over the life cycle, including strategy modification and market exit.


We are living in a data-driven world. Everything we do from getting our news in the morning, to buying goods, and searching for information leaves trails of data across the internet. Consumers have changed, and companies need to find new ways to engage with consumers in order to stay profitable and relevant. As a working professional, you will be tasked to use data to make business decisions and develop strategies that create value for consumers and your organization.

This course will introduce traditional theories of consumer behavior and then take you on a beginning journey through the dynamic practices of how to use consumer data and analytics in the digital age. Topics covered include consumer behavior theory, attitude formation and value creation, the challenges of consumer protection, market research, and the influence of technology on consumer decision-making.

Take both:

The rapid evolution of technology has led to increasingly well-informed buyers who are connected, communicative, and more in control than ever. This course discusses the theory and practice of digital marketing and its role in building relationships and, ultimately, driving sales. It examines digital technologies and their impact on business models, the marketing mix, branding, communication strategies, and distribution channels.

Emphasis is placed on contemporary topics that face today’s marketing managers – including online lead generation, search, social networking, and eCommerce – and their application within a comprehensive, integrated digital marketing strategy. The course considers the opportunities and challenges faced in business-to-consumer and business-to-business markets. It covers the latest research, current practices, and hands-on project work.

This course provides students with the key concepts and tools needed to turn raw data into useful business intelligence. A broad spectrum of business situations will be considered for which the tools of classical statistics and modern data mining have proven their usefulness. Problems considered will include such standard marketing research activities as customer segmentation and customer preference, as well as more recent issues in credit scoring, churn management, and fraud detection.

Roughly half the class time will be devoted to discussions on business situations, data mining techniques, their application, and their usage. The remaining time will comprise an applications laboratory in which these concepts and techniques are used and interpreted to solve realistic business problems.

Some knowledge of basic marketing principles and basic data analysis is assumed.

Operations Analytics

Complete any three courses

The operations function in an organization is focused on the transformation processes used to produce goods or provide services. Operations design is driven by strategic values, and innovative improvements can support sustained competitive advantage.

In this course, a variety of analytical and statistical techniques are introduced to develop a deep understanding of process behavior and how to use this analysis to inform process and operational designs. Topics such as process analysis and value-stream mapping, postponement and global and local supply chain strategies, queuing models, and managing system constraints are covered using case studies and hands-on activities such as online simulations. Nontraditional operations systems are also explored. The skills required to model an operational system, to reduce variation and mitigate bottlenecks, to effectively present resource needs, and to adjust capacity and inventory service levels are practiced during the course.

Risk management deals with decision-making under uncertainty. It is interdisciplinary, drawing upon management science and managerial decision-making, along with material from negotiation and cognitive psychology. This course first covers classic methods from decision analysis. It then applies these methods, from the perspective of business process improvement, to a broad set of applications in operations risk management and design. These include: quality assurance, supply chains, information security, fire protection engineering, environmental management, projects, and new products.

A course project is required (and chosen by each student according to their interests) to develop skills in integrating subjective and objective information in modeling and evaluating risk.

Prerequisite: OIE 501 or equivalent content, or instructor consent

This course studies the decisions, strategies, and analytical methods in designing, analyzing, evaluating, and managing supply chains. Concepts, techniques, and frameworks for better supply chain performance are discussed, and how digital technologies enable companies to be more efficient and flexible in their internal and external operations are explored.

The major content of the course is divided into three modules: supply chain integration, supply chain decisions, and supply chain management and control tools. Students will learn how to apply some of the techniques in operations research such as linear programming, dynamic programming, and decision trees to aid decision-making. A variety of instructional tools including lectures, case discussions, guest speakers, games, videos, and group projects and presentations are employed.

Prerequisite: OIE 501 or equivalent content, or instructor consent

Productivity management and performance analysis techniques and applications are covered from engineering and management perspectives. Topics include benchmarking, production functions, and the concept of relative efficiency and its measurement by data envelopment analysis. Application examples include efficiency evaluations of bank branches, sales outlets, hospitals, schools, and others.

In this course, Lean Six Sigma is presented as an organizational improvement system and a set of process analysis and statistical tools that have helped the world’s leading organizations achieve operational excellence, saving millions of dollars and improving customer satisfaction.

This course is organized in three parts: part one covers the essentials of Lean Six Sigma, including fundamental concepts and problem-solving methods; part two covers Lean Six Sigma tools, including topics such as value-stream mapping, process capability, and experimental design; part three describes the major activities in a Lean Six Sigma roadmap, from identifying core processes to executing improvement projects to sustaining Lean Six Sigma gains.

Find the cost-per-credit hour and other financial information on our tuition and financial aid page.