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Worcester Polytechnic Institute’s (WPI) Master of Science in Data Science online program prepares you to derive new insights from data and articulate these findings into innovative solutions for how we live, work, and interact with the world around us. In addition to the core courses that teach data science essentials, you’ll choose from a variety of electives that allow you to customize the program in two specializations to achieve your unique career goals.
Make a real-world impact by harnessing the power of data. With your online master of science in data science from WPI, you will master database management and analysis techniques such as machine learning and data-mining algorithms. You’ll gain a deeper knowledge of statistics and will have used a rich diversity of data-analysis software. WPI’s interdisciplinary approach rounds out your degree while developing your interpersonal and storytelling skills alongside technical mastery. In 2022, Fortune magazine ranked WPI fifth in their list of Best Online Master’s in Data Science Programs.
Develop fundamental skills in the computing languages and mathematics that power data science
Apply the full scope of tools and technologies needed to create, manage, and analyze large-scale databases
Choose from specializations in Artificial Intelligence (AI) & Machine Learning or Big Data Analytics or build your own area of focus from a range of electives
Apply what you’ve learned as a scientist, strategist, and professional to develop an implementable data-focused solution
The Master of Science in Data Science program online begins with a foundational track required for students without appropriate math and/or programming experience. After completing core courses, students can choose from three specializations in Artificial Intelligence (AI) & Machine Learning, Big Data Analytics & Management, or Business Intelligence.
This is an introductory graduate course teaching core computer science topics typically found in an undergraduate Computer Science curriculum, but at a graduate-level pace. It is primarily intended for students with little formal preparation in Computer Science to gain experience with fundamental Computer Science topics. After a review of programming concepts the focus of the course will be on data structures from the point of view of the operations performed upon the data and to apply analysis and design techniques to non-numeric algorithms that act on data structures. The data structures covered include lists, stacks, queues, trees and graphs. Projects will focus on the writing of programs to appropriately integrate data structures and algorithms for a variety of applications. This course may not be used to satisfy degree requirements for a B.S., M.S., or Ph.D. degree in Computer Science or a minor in Computer Science.
Introduction to Data Science provides an overview of Data Science, covering a broad selection of key challenges in and methodologies for working with big data. Topics include: data collection, integration, management, modeling, analysis, visualization, prediction and informed decision-making, as well as data security and data privacy. This introductory course is integrative across the core disciplines of Data Science, including databases, data warehousing, statistics, data mining, data visualization, high-performance computing, cloud computing, and business intelligence. Professional skills, such as communication, presentation, and storytelling with data will be fostered.
This course surveys the statistical methods most useful in data science applications. Topics covered include predictive modeling methods, including multiple linear regression, and time series; data dimension reduction; Discrimination and classification methods, clustering methods; and committee methods. Students will implement these methods using statistical software. Prerequisites: Statistics at the level of MA 2611 and MA2612 and linear algebra at the level of MA 2071.
AI and Machine Learning Focus Area
Big Data Analytics & Management Focus Area
Business Intelligence Focus Area
CS 548 - Knowledge Discovery and Data Mining
This course presents current research in Knowledge Discovery in Databases (KDD) dealing with data integration, mining, and interpretation of patterns in large collections of data. Topics include data warehousing and data preprocessing techniques; data mining techniques for classification, regression, clustering, deviation detection, and association analysis; and evaluation of patterns minded from data. Industrial and scientific applications are discussed. Recommended background: Background in artificial intelligence, databases, and statistics at the undergraduate level, or permission of the instructor. Proficiency in a high level programming language.
CS 534 - Artificial Intelligence
This course gives a broad survey of artificial intelligence. The course will cover methods from search, probabilistic reasoning, and learning, among other topics. Selected topics involving the applications of these tools are investigated. Such topics might include natural language understanding, scene understanding, game playing, and planning.
CS 539 - Machine Learning
The focus of this course is machine learning for knowledge-based systems. It will include reviews of work on similarity-based learning (induction), explanation-based learning, analogical and case-based reasoning and learning, and knowledge compilation. It will also consider other approaches to automated knowledge acquisition as well as connectionist learning.
CS 573 - Data Visualization
This course exposes students to the field of data visualization, i.e., the graphical communication of data and information for the purposes of presentation, confirmation, and exploration. The course introduces the stages of the visualization pipeline. This includes data modeling, mapping data attributes to graphical attributes, visual display techniques, tools, paradigms, and perceptual issues. Students learn to evaluate the effectiveness of visualizations for specific data, task, and user types. Students implement visualization algorithms and undertake projects involving the use of commercial and public-domain visualization tools. Students also read papers from the current visualization literature and do classroom presentations.
DS 503 - Big Data Management
Emerging applications in science and engineering disciplines generate and collect data at unprecedented speed, scale, and complexity that need to be managed and analyzed efficiently. This course introduces the emerging techniques and infrastructures developed for big data management including parallel and distributed database systems, map-reduce infrastructures, scalable platforms for complex data types, stream processing systems, and cloud-based computing. Query processing, optimization, access methods, storage layouts, and energy management techniques developed on these infrastructures will be covered. Students are expected to engage in hands-on projects using one or more of these technologies.
DS 504 - Big Data Analytics
Innovation and discoveries are no longer hindered by the ability to collect data, but the ability to summarize, analyze, and discover knowledge from the collected data in a scalable fashion. This course covers computational techniques and algorithms for analyzing and mining patterns in large-scale datasets. Techniques studied address data analysis issues related to data volume (scalable and distributed analysis), data velocity (high-speed data streams), data variety (complex, heterogeneous, or unstructured data), and data veracity (data uncertainty). Techniques include mining and machine learning techniques for complex data types, and scaleup and scale-out strategies that leverage big data infrastructures. Real-world applications using these techniques, for instance social media analysis and scientific data mining, are selectively discussed. Students are expected to engage in hands-on projects using one or more of these technologies.
This 3-credit graduate qualifying project, typically done in teams, is to be carried out in cooperation with a sponsor or industrial partner. It must be overseen by a faculty member affiliated with the Data Science Program. This offering integrates theory and practice of Data Science, and should include the utilization of tools and techniques acquired in the Data Science Program. In addition to a written report, this project must be presented in a formal presentation to faculty of the Data Science program and sponsors. Professional development skills, such as communication, teamwork, leadership, and collaboration, along with storytelling, will be practiced. Prerequisite: DS 501, completion of at least 24 credits of the DS degree, or consent of the instructor.
The foci of this class are the essential statistics and linear algebra skills required for Data Science students. The class builds the foundation for theoretical and computational abilities of the students to analyze high dimensional data sets. Topics covered include Bayes’ theorem, the central limit theorem, hypothesis testing, linear equations, linear transformations, matrix algebra, eigenvalues and eigenvectors, and sampling techniques, including Bootstrap and Markov chain Monte Carlo. Students will use these techniques while engaging in hands-on projects with real data. Prerequisites: Some knowledge of integral and differential calculus is recommended.
Choose CS 542 or DS 503
An introduction to the theory and design of database management systems. Topics covered include internals of database management systems, fundamental concepts in database theory, and database application design and development. In particular, logical design and conceptual modeling, physical d atabase design strategies, relational data model and query languages, query optimization, transaction management and distributed databases. Typically there are hands-on assignments and/or a course project. Selected topics from the current database research literature may be touched upon as well.
Emerging applications in science and engineering disciplines generate and collect data at unprecedented speed, scale, and complexity that need to be managed and analyzed efficiently. This course introduces the emerging techniques and infrastructures developed for big data management including parallel and distributed database systems, map-reduce infrastructures, scalable platforms for complex data types, stream processing systems, and cloud-based computing. Query processing, optimization, access methods, storage layouts, and energy management techniques developed on these infrastructures will be covered. Students are expected to engage in hands-on projects using one or more of these technologies.
Choose MIS 584, MKT 568, or MIS 587
Today’s business computing infrastructures are producing the large volumes of data organizations need to make better plans and decisions. This course provides an introduction to 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 provides students with the key concepts and tools 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.
This course explores how Machine Learning (ML) and Artificial Intelligence (AI) is 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 AutoML 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 life cycle starting with defining ML project objectives, acquiring and exploring data, modeling using AutoML tools, interpretation of models and communication of outcomes, and implementation and deployment of predictive models in organizations.
Choose CS 548, CS 539, or DS 504
This course presents current research in Knowledge Discovery in Databases (KDD) dealing with data integration, mining, and interpretation of patterns in large collections of data. Topics include data warehousing and data preprocessing techniques; data mining techniques for classification, regression, clustering, deviation detection, and association analysis; and evaluation of patterns minded from data. Industrial and scientific applications are discussed. Recommended background: Background in artificial intelligence, databases, and statistics at the undergraduate level, or permission of the instructor. Proficiency in a high level programming language.
The focus of this course is machine learning for knowledge-based systems. It will include reviews of work on similarity-based learning (induction), explanation-based learning, analogical and case-based reasoning and learning, and knowledge compilation. It will also consider other approaches to automated knowledge acquisition as well as connectionist learning. (Prerequisite: CS 534 or equivalent, or permission of the instructor.)
Innovation and discoveries are no longer hindered by the ability to collect data, but the ability to summarize, analyze, and discover knowledge from the collected data in a scalable fashion. This course covers computational techniques and algorithms for analyzing and mining patterns in large-scale datasets. Techniques studied address data analysis issues related to data volume (scalable and distributed analysis), data velocity (high-speed data streams), data variety (complex, heterogeneous, or unstructured data), and data veracity (data uncertainty). Techniques include mining and machine learning techniques for complex data types, and scaleup and scale-out strategies that leverage big data infrastructures. Real-world applications using these techniques, for instance social media analysis and scientific data mining, are selectively discussed. Students are expected to engage in hands-on projects using one or more of these technologies. Prerequisites: A beginning course in databases and a beginning course in data mining, or equivalent knowledge, and programming experience.
MIS 584 - Business Intelligence
Today’s business computing infrastructures are producing the large volumes of data organizations need to make better plans and decisions. This course provides an introduction to 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.
MKT 568 - Data Mining Business Applications
This course provides students with the key concepts and tools 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.
Worcester Polytechnic Institute’s (WPI) Master of Data Science online program carries significant prestige and opens doors to in-demand jobs. Our knowledgeable and supportive enrollment advisors are here to help and support you from application through graduation. Access your digital program guide to get started.
Getting your online master of science in data science degree from WPI delivers experiential learning in the theories and practical applications of database management and analytics. The skills and experience you’ll gain from this program are broadly applicable and will allow you to excel in a variety of industries along many different types of career paths.
Postdoc Applied Statistics Massachusetts Institute of Technology (MIT), 2009
I continually innovate in my courses to ensure students enjoy the learning experience, learn the key concepts and skills related to information systems through real-world examples, have an opportunity to learn from each other, learn to present themselves as professionals, and most importantly, learn to use or develop technology to make a difference.
15% job growth from 2019 to 2029
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10% job growth from 2019 to 2029
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10% job growth from 2019 to 2029
$93,750 national average salary
7% job growth from 2019 to 2029
$90,920 national average salary
10% job growth from 2019 to 2029
$93,750 national average salary
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