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Program Overview

Data Science

Master's in Data Science Online

Data science has become an integral part of every major industry, changing the way organizations collect information, analyze data, and strategize for the future. Students in Northwestern’s online MS in Data Science program build the technical, analytical, and managerial expertise needed to address the practical problems of today's data-driven world. Find out more about online learning at SPS.

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Evolving Opportunities

As this interdisciplinary field continues to evolve, data scientists are defining new areas of focus to play key roles at various stages of the business cycle through modeling, engineering, and management. Consequently, professionals with expertise in data analysis, mathematics, machine learning, object-oriented programming, computer science, and business management are in demand across a wide range of industries.

Critical Skills

MSDS students gain critical skills for succeeding in today's data-intensive world. They learn how to utilize relational and document database systems and analytics software built upon open-source systems such as R, Python, and TensorFlow. They learn how to make trustworthy predictions using traditional statistics and machine learning methods.

MSDS faculty members include data scientists, professional researchers and consultants, social scientists, mathematicians, statisticians, and computer scientists. Most faculty members hold terminal doctoral degrees and have extensive teaching and business experience.

Students coming from non-technical backgrounds are welcome in MSDS program. Many courses are designed to help students make the transition from non-technical to technical studies. Early courses in the program include math for data scientists, statistical analysis, Python programming essentials, and introduction to data science.

The Master of Science in Data Science program is an extension of an already successful graduate program, the Master of Science in Predictive Analytics (MSPA) program, which was established in 2011. The MSPA program was one of the first programs in the world to offer dedicated graduate training in data science. The Master of Science in Data Science program includes all courses from the MSPA program, while adding seven new courses and three specializations.

About the MS in Data Science

Master's in Data Science Program Goals

  • Articulate analytics as a core strategy of data science
  • Transform data into actionable insights
  • Develop statistically sound and robust analytic solutions
  • Demonstrate leadership
  • Formulate and manage plans to address business issues
  • Evaluate constraints on the use of data
  • Assess data structure and data lifecycle

Data Science at Northwestern

Northwestern University offers two master’s degree programs in analytics that prepare students to meet the growing demand for data-driven leadership and problem solving. In addition to the online MSDS program, Northwestern's McCormick School of Engineering offers a full-time on-campus program.

Master of Science in Data Science (Northwestern University School of Professional Studies)
  • Online, part-time program
  • Builds expertise in advanced analytics, data mining, database management, financial analysis, predictive modeling, quantitative reasoning and web analytics, as well as advanced communication and leadership

Master of Science in Analytics (McCormick School of Engineering and Applied Science)
  • 15-month, full-time, on-campus program
  • Integrates data science, information technology and business applications into three areas: predictive (forecasting), descriptive (business intelligence and data mining) and prescriptive (optimization and simulation)

MS in Data Science Online Courses

Courses range from statistical analysis to database systems to practical machine learning. Explore all the MS in Data Science Online Courses for full detail on the program's offerings.

Data Science Curriculum

The Master's in Data Science requires the successful completion of 12 courses to obtain a degree. These requirements cover six core courses, a leadership or project management course, two required courses corresponding to a declared specialization, two electives, and a capstone project (498) or thesis (590). Review Data Science Curriculum details and elective choices while you consider applying to this program.

Master's in Data Science Admission

A variety of factors are considered when your application is reviewed. Background and experience vary from student to student. For a complete list of requirements, see the Master's in Data Science Admission page.

Tuition and Financial Aid for Data Science

Tuition for the Master's in Data Science program at Northwestern is comparable to other competitive online analytics programs across the nation. Financial aid opportunities exist for students at Northwestern. Complete details can be found on the Tuition and Financial Aid for Data Science web page.

Registration Information for Data Science

Already accepted into the Master's in Data Science program? Get ahead and register for your classes as soon as possible to ensure maximum efficiency in your trajectory.

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Data Science Careers

Careers for those with a Master's in Data Science degree are ever expanding. Nearly every industry is looking for trained professionals in managing and interrupting big data to improve business solutions.  Explore the never-ending possibilities on our Data Science Career Options page.

Data Science Program Faculty

Instructors in the Master's in Data Science program at Northwestern are world-renowned experts in the field. They bring practical real-world experiences to the online classroom and engage with students on an interpersonal level. Get to know the instructors on our Data Science Program Faculty page.

Find out more about Northwestern's MS in Data Science

Core Courses:Course Detail
Math for Data Scientists <> MSDS 400-DL

Students learn techniques for building and interpreting mathematical models of real-world phenomena in and across multiple disciplines, including linear algebra, discrete mathematics, probability, and calculus, with an emphasis on applications in data science and data engineering. This is for students who want a firm understanding or review of these fields of mathematics prior to enrolling in courses that assume understanding of mathematical concepts. Counts as an elective for students admitted prior to fall 2014. Required as a core course for students admitted for fall 2014 and after.


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Statistical Analysis <> MSDS 401-DL

This course reviews fundamentals of statistical reasoning, exploring data, performing statistical analysis, interpreting and communicating analytical results. Topics include descriptive statistics, data display, central tendency, dispersion, probability theory, discrete and continuous distributions, point estimation, confidence interval estimation, hypothesis testing, correlation, linear regression, and contingency table analysis. Students utilize an open-source environment for programming with data.


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Introduction to Data Science <> MSDS 402-DL

This course introduces the field of data science, which combines business strategy, information technology, and modeling methods. The course reviews the benefits and opportunities of data science, organizational and implementation issues, ethical, regulatory, and compliance issues, while providing an overview of modeling methods, analytics software, and information systems. It discusses business problems and solutions regarding traditional and contemporary data management systems and the selection of appropriate tools for data collection and analysis. It also reviews approaches to business research, sampling, and survey design.


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Database Systems and Data Preparation <> MSDS 420-DL

Behind every analytics project is an analytical data source. In this course, students explore the fundamentals of data management and data preparation. Students acquire hands-on experience with various data file formats, working with quantitative data and text, relational database systems, and document database systems. They access, organize, clean, prepare, transform, and explore data, using database shells, query and scripting languages, and analytical software. This is a case-study and project-based course with a strong programming component.

Prerequisites: MSDS 402-DL Introduction to Data Science.


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Practical Machine Learning <> MSDS 422-DL

The course introduces machine learning with business applications. It provides a survey of machine learning techniques, including traditional statistical methods, resampling techniques, model selection and regularization, tree-based methods, support vector machines and kernel methods, principal components analysis, cluster analysis, artificial neural networks, and deep learning. Students implement machine learning models with open-source software for data science. They explore data and learn from data, finding underlying patterns useful for data reduction, feature analysis, prediction, and classification.

Prerequisites: MSDS 400-DL Math for Data Scientists and MSDS 401-DL Statistical Analysis.


SECTIONS 55-56 taught in R, SECTIONS 59-60 taught in Python


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Decision Analytics <> MSDS 460-DL

This course covers fundamental concepts, solution techniques, modeling approaches, and applications of decision analytics. It introduces commonly used methods of optimization, simulation and decision analysis techniques for prescriptive analytics in business. Students explore linear programming, network optimization, integer linear programming, goal programming, multiple objective optimization, nonlinear programming, metaheuristic algorithms, stochastic simulation, queuing modeling, decision analysis, and Markov decision processes. Students develop a contextual understanding of techniques useful for managerial decision support. They implement decision-analytic techniques using a state-of-the-art analytical modeling platform. This is a problem and project-based course.

Prerequisites: MSDS 400-DL Math for Data Scientists and MSDS 401-DL Statistical Analysis.


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Project Management <> MSDS 475-DL

This course introduces best practices in project management, covering the full project life cycle with a focus on globally accepted standards. It reviews traditional methods, including: integration, portfolio and stakeholder management, chartering, scope definition, estimating, the Delphi method and project evaluation and review technique, precedence diagramming and the critical path method, scheduling, risk analysis and management, resource loading and leveling, Gantt charts, earned value analysis and performance indices of project cost/schedule control systems criteria. It shows how the project management maturity model, leadership, team development, and principles of negotiation apply to organizations of various types: hierarchical and matrix organizations, international teams, and virtual teams. Options in project management software systems are included. Using methods and models from this course, analytics managers and team leaders should experience greater project definition and structure. They should be able to execute data science and data engineering projects more effectively.

This is a required course for the Analytics Management specialization.


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Business Leadership and Communications <> MSDS 480-DL

This course introduces fundamental leadership theory and associated behaviors to enable students to excel in their analytics careers. The course builds from the basic premise that leadership is learned. It examines the theory and practice of leadership at the individual and organizational levels, and discusses how to drive effective change in organizations at various stages in an enterprise analytics transformation process. Students spend three weeks on analytics-specific project management, in which they design an analytics project plan using an agile approach. They incorporate the cross-industry standard process for data mining (CRISP-DM) methodology, and execute that plan in a simulated business setting. Leadership challenges unique to analytics departments in various company sizes are addressed through the use of case studies and theory-based assignments. The course focuses on developing effective communication strategies and presentations that resonate across business and technical teams.

This is a required course for the Analytics Management specialization.


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Capstone Project <> MSDS 498-DL

The capstone course focuses upon the practice of data science. This course is the culmination of the data science program. It gives students an opportunity to demonstrate their business strategic thinking, communication, and consulting skills. Business cases across various industries and application areas illustrate strategic advantages of analytics, as well as organizational issues in implementing systems for data science. Students work in project teams, generating business plans and project implementation plans. Students may choose this course or the master's thesis to fulfill their capstone requirement.

Prerequisite: Students may take one other course simultaenously with MSDS 498-DL but must complete all other program requirements prior to commencement of the course.


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Thesis Research <> MSDS 590-DL

This final project is meant to represent the culmination of students’ experience in the program and must demonstrate mastery of the curriculum and ability to conduct sustained independent research and analysis. The project may be applied or may be a traditional scholarly paper, in both cases a write-up following the paper’s program-specific guidelines is required. Students must submit a proposal and secure a first reader in order to register; for further details students are advised to review the student handbook and contact their academic advisor.


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Elective Courses:Course Detail
Regression and Multivariate Analysis <> MSDS 410-DL

This course develops the foundations of predictive modeling by: introducing the conceptual foundations of regression and multivariate analysis; developing statistical modeling as a process that includes exploratory data analysis, model identification, and model validation; and discussing the difference between the uses of statistical models for statistical inference versus predictive modeling. The high level topics covered in the course include: exploratory data analysis, statistical graphics, linear regression, automated variable selection, principal components analysis, exploratory factor analysis, and cluster analysis.

This is a required course for the Analytics and Modeling specialization.

Prerequisites: MSDS 400-DL Math for Data Scientists and MSDS 401-DL Statistical Analysis


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Generalized Linear Models <> MSDS 411-DL

This course extends linear ordinary least-squares regression, introducing the concept of the generalized linear model and its use in making predictions. The course reviews traditional linear regression as a special case of generalized linear models, and then continues with logistic regression, Poisson regression, and survival analysis. The course is heavily weighted towards practical application with large data sets containing missing values and outliers. It addresses issues of data preparation, model development, validation, and deployment.

This is a required course for the Analytics and Modeling specialization.

Prerequisites: MSDS 410-DL Regression and Multivariate Analysis.


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Time Series Analysis and Forecasting <> MSDS 413-DL

This course covers key analytical techniques used in the analysis and forecasting of time series data. Specific topics include the role of forecasting in organizations, exponential smoothing methods, stationary and non-stationary time series, autocorrelation and partial autocorrelation functions, Univariate ARIMA models, seasonal models, Box-Jenkins methodology, Regression Models with ARIMA errors, Transfer Function modeling, Intervention Analysis, and multivariate time series analysis.

Prerequisites: MSDS 411-DL Generalized Linear Models.


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Python for Data Science <> MSDS 430-DL

This course introduces core features of the Python programming language, demonstrating fundamental concepts in computer science. It provides an in-depth discussion of data representation strategies, showing how data structures are implemented in Python and demonstrating tools for data science and software engineering. Working on data analysis problems, students employ various programming paradigms, including functional programming, object-oriented programming, and data stream processing. Special attention is paid to the standard Python library and packages for analytics and modeling.


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Foundations of Data Engineering <> MSDS 432-DL

This course provides an overview of the discipline of data engineering and introduces software and systems for data science as well as methods of software development. Students learn about computer languages while working on data and text analysis projects. Students gain experience with open-source software, text editors, and integrated development environments. They employ best practices in software development, utilizing tools for syntax checking, testing, debugging, and version control.

This is a required course for the Data Engineering Specialization.

Prerequisites: MSDS 402-DL Introduction to Data Science.


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Analytics Application Development <> MSDS 434-DL

This course covers programming components essential to the development of analytics applications. The focus is analytics software engineering. Students learn to develop single-system/desktop solutions as well as client-server solutions. They learn about web-based, client-server solutions employing a variety of front-end and back-end system components. Students develop software, working on open-source programming, database, and systems integration projects. They employ best practices in software development.

This is a required course for the Data Engineering specialization.

Prerequisites: MSDS 400-DL Math for Data Scientists, MSDS 401-DL Statistical Analysis, MSDS 420-DL Database Systems and Data Preparation, and MSDS 432-DL Foundations of Data Engineering.


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Analytics Systems Analysis <> MSDS 436-DL

This course provides a detailed treatment of software and systems for data science as well as methods for testing and evaluating software and systems. Students learn about the systems architecture, alternative software stacks, design of scalable systems, and computer system security. Students gain experience with methods for benchmarking analytics software in production environments, testing system performance across various system loads. The learn tools of systems analysis as applied to stand-alone and distributed systems. They evaluate benefits and risks associated with in-house versus cloud-based distributed systems.

Prerequisites: MSDS 434-DL Analytics Application Development.


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Marketing Analytics <> MSDS 450-DL

This course provides a comprehensive review of data science as it relates to marketing management and business strategy. The course gives students an opportunity to work with data relating to customer demographics, marketing communications, and purchasing behavior. Students perform data cleansing, aggregation, and analysis, exploring alternative segmentation schemes for targeted marketing. They design tools for reporting research results to management, including information about consumer purchasing behavior and the effectiveness of marketing campaigns. Conjoint analysis and choice studies are introduced as tools for consumer preference measurement, product design, and pricing research. The course also reviews methods for product positioning and brand equity assessment. This is a case-study and project-based course involving extensive data analysis.

Prerequisites: MSDS 411-DL Generalized Linear Models.


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Financial and Risk Analytics <> MSDS 451-DL

Building upon probability theory and inferential statistics, this course provides an introduction to risk analytics. Examples from economics and finance show how to incorporate risk within regression and time series models. Monte Carlo simulation is used to demonstrate how variability in data affects uncertainty about model parameters. Additional topics include subjectivity in risk analysis, causal modeling, stochastic optimization, portfolio analysis, and risk model evaluation.

Prerequisites: MSDS 411-DL Generalized Linear Models.


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Web and Network Data Science <> MSDS 452-DL

This is an introduction to data science with applications to web analytics and network science. The web represents a key information resource in today’s data-driven, data-intensive world. This course shows how to acquire and use data from the web. It provides a comprehensive review of web analytics, including website usage and search performance metrics. It shows how to use websites and information on the web to understand web user behavior and to guide management decisions. The course also demonstrates network science and text analytics methods, working with data from social media. This is a case-study and project-based course with a strong programming component.

Prerequisites: MSDS 400-DL Math for Data Scientists, MSDS 401-DL Statistical Analysis, and MSDS 420-DL Database Systems and Data Preparation.


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Text Analytics <> MSDS 453-DL

This course is focused on incorporating text data from a wide range of sources and utilizing those data to guide management decisions. Topics covered include extracting key concepts from text, organizing extracted information into meaningful categories, linking concepts together, and creating structured data elements from extracted concepts. Students identify an area of interest and collect relevant text documents, building a document corpus for in-depth analysis using methods of natural language processing and text analytics. This is a case-study and project-based course with a strong programming component.

Prerequisites: MSDS 400-DL Math for Data Scientists, MSDS 401-DL Statistical Analysis, and MSDS 420-DL Database Systems and Data Preparation.


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Advanced Modeling Techniques <> MSDS 454-DL

Drawing upon previous course work in data science, this course build on earlier courses in analytics and modeling, providing a advanced review of traditional statistics and machine learning. It explores computer-intensive methods for parameter and error estimation, model selection, and model validation. Example topics include ordinary least squares regression, logistic regression, multinomial logistic regression, classification and regression trees, neural networks, support vector machines, naïve Bayes methods, principal components analysis, cluster analysis, and regularization techniques. Students work on individual and team assignments using open-source programming tools.

Prerequisites: MSDS 411-DL Generalized Linear Models and MSDS 422-DL Practical Machine Learning.


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Data Visualization <> MSDS 455-DL

This course begins with a review of human perception and cognition, drawing upon psychological studies of perceptual accuracy and preferences. The course reviews principles of graphic design, what makes for a good graph, and why some data visualizations effectively present information and others do not. It considers visualization as a component of systems for data science and presents examples of exploratory data analysis, visualizing time, networks, and maps. It reviews methods for static and interactive graphics and introduces tools for building web-browser-based presentations. This is a project-based course with programming assignments.

Prerequisites: MSDS 400-DL Math for Data Scientists and MSDS 401-DL Statistical Analysis.


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Sports Performance Analytics <> MSDS 456-DL

An introduction to sports performance measurement and analytics, this course reviews roles of athletes at each position in sports selected by the instructor. With a focus on the individual athlete, the course discusses the development and use of accurate assessments and variability due to factors such as body type, climate, and playing surface. The course reviews athletic performance measurements, including jumping ability, running speed, agility, and strength. It utilizes exploratory data analysis, predictive modeling, and presentation graphics, showing real-world implications for athletes, coaches, team managers, and the sports industry.

Prerequisites: MSDS 400-DL Math for Data Scientists and MSDS 401-DL Statistical Analysis.


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Sports Management Analytics <> MSDS 457-DL

This course provides a comprehensive review of financial, statistical, and mathematical models as they relate to sports team administration, marketing, and business management. The course gives students an opportunity to work with data and models relating to sports business tactics and strategy. Students employ modeling methods in studying sports team media, ticket pricing and game-day events, loyalty and sponsorship program development, player and team valuation, and customer relationship management. The course makes extensive use of sports business case studies.

Prerequisites: MSDS 400-DL Math for Data Scientists and MSDS 401-DL Statistical Analysis.


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Artificial Intelligence and Deep Learning <> MSDS 458-DL

Deep learning has yielded multiple successful artificial intelligence applications (search, vision, translation, drug synthesis), receiving major investments from leading technology companies. It works by combining (typically neural network-based) machine learning methods with multiple representation levels, so that data are transformed through a series of processes yielding useful results. The course reviews generative and descriptive methods, where generative models learn joint probability distributions and make Bayes-rule predictions, and descriptive models learn from training examples. Topics include the Boltzmann machine, deep neural networks, hidden Markov models, and other clustering and classification methods. Students learn how to select deep learning methods based on dataset size, number of known exemplars for training, classification granularity, and other factors. This is a project-based course with individual and team assignments involving extensive programming.

Prerequisites: MSDS 422-DL Practical Machine Learning.


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Information Retrieval and Real-Time Analytics <> MSDS 459-DL

This course reviews information retrieval and sampling across databases. Students learn how to develop production systems for searching and retrieving information from large document collections, with applications to the World Wide Web and social media, as well as business transactions, network traffic, operations analysis. This is a project-based course with individual and team assignments involving extensive programming. This is a case-study and project-based course with a strong programming component.

Prerequisites: MSDS 422-DL Practical Machine Learning.


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Analytics Entrepreneurship <> MSDS 470-DL

This course prepares students for establishing and running a data-sciences-oriented entrepreneurial organization. Topics include evaluating preparedness for entrepreneurial work, activities that would help transform an idea into a running organization, identifying the right data, analytics tools, and resources platform, and aligning with unmet market demands. Students learn about growing a network of people and obtaining capital assets, creating innovative intellectual property and sharpening unique competitiveness, and making product development and marketing choices. Analytic product and consulting services opportunities are reviewed. Students develop business plans and present a business pitch for starting an entrepreneurial organization.

Prerequisites: MSDS 400-DL Math for Data Scientists and MSDS 401-DL Statistical Analysis.


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Analytics Consulting <> MSDS 472-DL

Analytics consulting brings together consultative processes and tools for creating a trusted advisor relationship with clients. This course covers concepts, processes, tools, and techniques for developing consulting proposals, selling, contracting, bringing together teams and resources, servicing, managing projects, and creating recommendations. In the process, students learn how to meet business requirements, gathering appropriate data, developing research reports, and managing client engagements. They utilize performance measures and project management tools. The course also addresses ethical issues and career challenges associated with running a consulting practice.

Prerequisites: MSDS 400-DL Math for Data Scientists and MSDS 401-DL Statistical Analysis.


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Accounting and Finance for Analytics Managers <> MSDS 474-DL

This course reviews principles of financial and managerial accounting with a focus on the work of analytics managers. Students learn how to read balance sheets, income statements, and cash flow statements. They learn how to conduct cost-volume-profit (breakeven) analysis. In finance, special attention is paid to investment in analytics projects, applying discounted cash flow analysis and computing return on investment. The course provides in-depth coverage of spreadsheet programming methods, setting the stage for subsequent financial modeling work.

This is a required course for the Analytics Management specialization.

Prerequisites: MSDS 402-DL Introduction to Data Science.


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Special Topics in Data Science MSDS 490-DL

Special Topics in Data Science


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