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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.

 COURSE SCHEDULE & REGISTRATION REGISTRATION POLICIES & CONTACTS

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. 

Prerequisites: None


View MSDS 400-DL Sections
Applied Statistics with R <> MSDS 401-DL

This course teaches fundamentals of statistical analysis. This includes evaluating statistical information, performing data analyses, and interpreting and communicating analytical results. Students will learn to use the R language for statistical analysis, data visualization, and report generation. Topics covered include descriptive statistics, central tendency, exploratory data analysis, probability theory, discrete and continuous distributions, statistical inference, correlation, multiple linear regression, contingency tables, and chi-square tests. Selected contemporary statistical concepts, such as bootstrapping, are introduced to supplement traditional statistical methods.

Recommended prior course: MSDS 400-DL Math for Data Scientists.


View MSDS 401-DL Sections
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, as well as organizational, implementation, and ethical issues. The course provides an overview of modeling methods, analytics software, and information systems. It discusses business problems and solutions for traditional and contemporary data management systems, and the selection of appropriate tools for data collection and analysis. The course also reviews approaches to business research, sampling, and survey design.

Prerequisites: None.


View MSDS 402-DL Sections
Database Systems and Data Preparation <> MSDS 420-DL

In this course students explore the fundamental concepts of database management and data preparation. With a focus on applications in large-scale data analytics projects, the course introduces relational database systems, the relational model, normalization process, and structured query language (SQL). The course discusses topics related to data integration and cleaning, database programming for extract, transform, and load (ETL) operations. Students learn NoSQL technologies for working with unstructured data and document-oriented information retrieval systems. They learn how to index and score documents for effective and relevant responses to user queries. Students acquire hands-on programming experience for data preparation and data extraction using various data sources and file formats.

Prerequisite: MSDS 402-DL Introduction to Data Science.


View MSDS 420-DL Sections
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, 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, MSDS 401-DL Applied Statistics with R, and MSDS 402-DL Introduction to Data Science.

Fall 2018: ALL sections are taught in Python

 


View MSDS 422-DL Sections
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 Applied Statistics with R.


View MSDS 460-DL Sections
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.


View MSDS 475-DL Sections
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.


View MSDS 480-DL Sections
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.

Prerequisites: Completion of all core courses in the student's graduate program and specialization.


View MSDS 498-DL Sections
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.


Prerequisites: Completion of all core courses in the student's graduate program and specialization.


There is no available section.
Elective Courses:Course Detail
Regression 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 Applied Statistics with R.

SECTION-SPECIFIC LANGUAGES:

Sections 55, 56, 57 - R

Section 58 - Python


View MSDS 410-DL Sections
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.

SECTION-SPECIFIC LANGUAGES:

Sections 55, 56, 57 - R

Section 58 - Python



View MSDS 411-DL Sections
Time Series Analysis and Forecasting <> MSDS 413-DL

The objective of this course is to cover 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 autoregressive integrated moving average (ARIMA) models, seasonal models, Box-Jenkins methodology, regression models with ARIMA errors, transfer function modeling, intervention analysis, and multivariate time series analysis.

Recommended prior course: 411-DL Generalized Linear Models. 

Prerequisites: MSDS 420-DL Database Systems and Data Preparation and MSDS 422-DL Practical Machine Learning.


View MSDS 413-DL Sections
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.

Prerequisites: None.


View MSDS 430-DL Sections
Foundations of Data Engineering <> MSDS 432-DL

This course provides an overview of the discipline of data engineering. It introduces software and systems for data science and software development as required in the design of data-intensive applications. Students learn about algorithms, data structures, and technologies or storing and processing data. Students gain experience with open-source software, text editors, and integrated development environments. Students employ best practices in software development, utilizing tools for syntax checking, testing, debugging, and version control. The course also introduces formal models, simulations, and benchmark experiments for evaluating software, systems, and processes. 

This is a required course for the Data Engineering Specialization.

Prerequisites: MSDS 420-DL Database Systems and Data Preparation or CIS 417 Database Systems Design.


View MSDS 432-DL Sections
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: (1) MSDS 432-DL Foundations of Data Engineering and (2) MSDS 422-DL Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.


There is no available section.
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.


There is no available section.
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.

Recommended prior course: 411-DL Generalized Linear Models.

Prerequisites: MSDS 420-DL Database Systems and Data Preparation and MSDS 422-DL Practical Machine Learning.


View MSDS 450-DL Sections
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.

Recommended prior course: MSDS 413-DL Time Series Analysis and Forecasting.

Prerequisites: MSDS 420-DL Database Systems and Data Preparation and MSDS 422-DL Practical Machine Learning.


View MSDS 451-DL Sections
Web and Network Data Science <> MSDS 452-DL

This course shows how to acquire and analyze information from the web. It provides a comprehensive review of web analytics, including website usage and search performance metrics. It shows how to analyze information from the web to understand user attitudes and behavior and to guide management decisions. Students explore natural language processing of text streams and documents collected from the web. Students use methods of network science, analyzing software systems, website structure, and user interactions through social media. This is a case-study- and project-based course with a strong programming component.

Prerequisites: (1) MSDS 420-DL Database Systems and Data Preparation or CIS 417 Database Systems Design and Implementation and (2) MSDS 422-DL Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.


View MSDS 452-DL Sections
Natural Language Processing <> MSDS 453-DL

A comprehensive review of text analytics and natural language processing with a focus on recent developments in computational linguistics and machine learning. Students work with unstructured and semi-structured text from online sources, document collections, and databases. Using methods of artificial intelligence and machine learning, students learn how to parse text into numeric vectors and to convert higher dimensional vectors into lower dimensional vectors for subsequent analysis and modeling. Applications include speech recognition, semantic processing, text classification, relevant search, recommendation systems, sentiment analysis, and topic modeling. This is a project-based course with extensive programming assignments.

Prerequisites:(1) MSDS 420-DL Database Systems and Data Preparation or CIS 417 Database Systems Design and Implementation and (2) MSDS 422-DL Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.


View MSDS 453-DL Sections
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 an 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.

Recommended prior course: 411-DL Generalized Linear Models.

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


View MSDS 454-DL Sections
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 Applied Statistics with R.


View MSDS 455-DL Sections
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 training regimen. The course reviews athletic performance measurements, including jumping ability, running speed, agility, and strength. Students work with player on-field and on-court performance measures. The course 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 Applied Statistics with R.


View MSDS 456-DL Sections
Sports Management Analytics <> MSDS 457-DL

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


View MSDS 457-DL Sections
Artificial Intelligence and Deep Learning <> MSDS 458-DL

An introduction to the field of artificial intelligence, this course illustrates probability-rule-based generative models as well as discriminative models that learn from training data. The course reviews applications of artificial intelligence and deep learning in vision and language processing. Students learn best practices for building supervised learning models and, in particular, deep neural networks for classification and regression. Students also learn about feature engineering, autoencoders, and strategies of unsupervised and semi-supervised learning, as well as reinforcement learning. This is a project-based course with extensive programming assignments.

Prerequisites: (1) MSDS 420-DL Database Systems and Data Preparation or CIS 417 Database Systems Design and Implementation and (2) MSDS 422-DL Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.


View MSDS 458-DL Sections
Information Retrieval and Real-Time Analytics <> MSDS 459-DL

This course reviews systems for searching and retrieving information from the web and from document collections. Students learn how to process logs of streaming, real-time data relating to physical sensors, network traffic, operations, communications, and business transactions. Students learn how to prepare indices for efficient and relevant search across large document collections. They also explore neural network sequence models and natural language processing of computer logs and document collections. This is a case-study- and project-based course with a strong programming component.

Prerequisites: (1) MSDS 420-DL Database Systems and Data Preparation or CIS 417 Database Systems Design and Implementation and (2) MSDS 422-DL Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.


There is no available section.
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 essential elements of business plans in order to present a final business pitch for starting an entrepreneurial data science venture.

Prerequisites: MSDS 400-DL Math for Data Scientists and MSDS 401-DL Applied Statistics with R.


View MSDS 470-DL Sections
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. The course is structured around an analytics consulting simulation and students work in teams. Students learn how to identify and meet business requirements through gathering appropriate information, developing reports, and managing client relationships. The course also addresses ethical issues and career challenges associated with analytics consulting. The course is appropriate for students interested in or currently acting as either an internal or external consultant. Considerations and challenges associated with operating your own consulting company are also addressed.

Prerequisites: MSDS 400-DL Math for Data Scientists and MSDS 401-DL Applied Statistics with R.


View MSDS 472-DL Sections
Accounting and Finance for Analytics Managers <> MSDS 474-DL

This course reviews principles of corporate finance and managerial accounting with a focus on the work of analytics managers. Analytics managers are often responsible for the profit-and-loss (P&L) of their projects and divisions which have certain unique needs in terms of workflow, co-working with other businesses, cooperating with multiple stakeholders (especially IT), and also employing highly specialized knowledge professionals. To support these responsibilities, students learn how to conduct breakeven (cost-volume-profit) analysis, apply discounted cash flow analysis, and compute return on investments. Students also learn how to read balance sheets, income statements, and cash flow statements and infer risks related to companies. 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.


View MSDS 474-DL Sections
Special Topics in Data Science MSDS 490-DL

Special Topics in Data Science


There is no available section.
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