MS in Data Science, Accelerated Program Curriculum | Northwestern University School of Professional Studies - Northwestern School of Professional Studies

Students learn how to build and interpret mathematical models of real-world phenomena in many areas. The course covers linear algebra, discrete mathematics, calculus and graph theory, with an emphasis on applications in data science and data engineering. It provides an introduction to these fields of mathematics prior to enrolling in courses that assume understanding of mathematical concepts.pts. 

Prerequisites: None

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 Math for Modelers.

This course introduces data management and data preparation with a focus on applications in large-scale analytics projects utilizing relational, document, graph, and graph-relational databases. Students learn about the relational model, the normalization process, and structured query language. They learn about data cleaning and integration, and database programming for extract, transform, and load operations. Students work with unstructured data, indexing and scoring documents for effective and relevant responses to user queries. They learn about graph data models and query processing. Students write programs for data preparation and extraction using various data sources and file formats.

Recommended:  Prior programming experience or MSDS 430 Python for Data Science.

The course introduces machine learning with business applications. It provides a survey of statistical and machine learning algorithms and techniques including the machine learning framework, regression, classification, regularization and reduction, tree-based methods, unsupervised learning, and fully-connected, convolutional, and recurrent neural networks. 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.

Recommended:  Prior programming experience or MSDS 430 Python for Data Science.

Prerequisites: MSDS 400 Math for Modelers and  MSDS 401 Applied Statistics with R.

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 Math for Modelers and MSDS 401 Applied Statistics with R.

This course introduces data-driven management methods, including business process workflows, mining, modeling, and simulation, activity-based costing, constrained optimization, and predictive analytics. Data from business operations, properly recorded in time-stamped logs of activities and their associated costs, represent essential information for business management. Analyzing business activities provides a guide to business intelligence and business process improvements, including those associated with robotic process automation and digital transformation. By reviewing detailed case studies and using commercial and open-source analytics platforms, students learn how data and models can be used to guide management decisions.

Prerequisites: None.

This course introduces data management concepts, including data quality, integrity, usability, consistency, and security. It considers the lineage of data, sometimes referred to as data provenance. It reviews ethical, legal, and technical issues relating to data acquisition and dissemination, as well as privacy protection. The course provides a management introduction to cybersecurity, including network, system, and database security, as well as encryption and blockchain technologies. The course covers laws relating to protecting intellectual property, with discussion of copyrights, patents, and contracts.

Prerequisites: None

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.

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

This course introduces traditional statistics and data modeling for supervised learning problems, as employed in observational and experimental research. With supervised learning there is a clear distinction between explanatory and response variables. The objective is to predict responses, whether they be quantitative as with multiple regression or categorical as with logistic regression and multinomial logit models. Students work on research and programming assignments, exploring data, identifying appropriate models, and validating models. They utilize techniques for observational and experimental research design, data visualization, variable transformation, model diagnostics, and model selection. 

Prerequisites: MSDS 400 Math for Modelers and MSDS 401 Applied Statistics with R.

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

This course introduces traditional and modern methods of unsupervised learning. Students see how to represent relationships among many continuous variables using principal components and factor analysis. They identify groups of individuals and groups of variables with cluster analysis and block clustering. They explore relationships among categorical variables with log-linear models and association rules. They visualize multivariate data with lattice displays, multidimensional scaling, and t-distributed stochastic neighbor embedding. And they detect anomalies using autoencoders and probabilistic deep learning. This is a project-based course with extensive programming assignments.

Prerequisites: MSDS 400 Math for Modelers and MSDS 401 Applied Statistics with R

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

This course covers analytical methods for time series analysis and forecasting. Specific topics include the role of forecasting in organizations, exploratory data analysis, 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, multivariate time series analysis, and non-linear time series modeling including exponential smoothing methods, random forest analysis, and hidden Markov modeling.

Recommended prior course: MSDS 410 Supervised Learning Methods and MSDS 411 Unsupervised Learning Methods. 

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

This is an elective course for the Analytics and Modeling specialization.

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.

This is an elective course for the Analytics and Modeling specialization.

This course introduces concepts, processes, tools, and techniques of management consulting. This includes winning consulting work, executing engagements, communicating with clients, and managing client relationships. Working in teams, students simulate a real-world consulting engagement, developing critical thinking, listening, speaking, and written communication skills. Students construct consulting presentations, communicating key findings and client impacts while employing data visualization best practices. The course is appropriate for students considering management consulting as a profession, as well as for students with internal expert or consultant roles.

Prerequisites: None.

 This is a required course for students in the Analytics Management specialization.

This course reviews corporate finance and managerial accounting with a focus on technology companies and projects. Technology managers and entrepreneurs need to secure adequate funding, coordinate with other organizations, employ specialized knowledge workers, and satisfy multiple stakeholders. Company success and sustainable growth depend on adequate cashflow and profitability. In this course, students learn how to read and analyze financial statements and evaluate risks. They learn how to conduct breakeven and return-on-investment analyses with special reference to technology projects. Students work in groups, analyzing cases and assessing the financial position of firms. They work with spreadsheet programs, setting the stage for subsequent financial modeling work.

Prerequisites: None.

This is a required course for the Analytics Management specialization.

This course introduces concepts of leadership and organizational behavior. It builds on the premise that leadership is learned and discusses how to drive change in organizations at stages of conception, growth, and evolution. Students spend three weeks on technology-specific project management, in which they design a project plan using an agile approach. They learn how to incorporate the cross-industry standard processes for information system design, data analysis, and modeling. They practice executing plans in simulated business settings. Working on case studies and theory-based assignments, students see how to address leadership challenges unique to technology organizations. The course focuses on developing effective communication strategies and presentations that resonate across business and technical teams to emphasize vision and organizational acceptance.

Prerequisites:  None

This is an elective course for the Analytics Management specialization.

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

This is an elective course for the Analytics Management specialization.

This course reviews 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. 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, search, recommendation systems, sentiment analysis, and topic modeling. This is a project-based course with extensive programming assignments.

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

This is a required course for the Artificial Intelligence specialization.

An introduction to artificial intelligence, this course illustrates probability-rule-based generative models as well as discriminative models for learning from data. It reviews applications of artificial intelligence and deep learning in vision and language processing. Students learn best practices for building deep learning models for classification and regression. The 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 Database Systems or CIS 417 Database Systems Design and Implementation and (2) MSDS 422 Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.

This is a required course for the Artificial Intelligence specialization.

This course introduces traditional statistics and data modeling for supervised learning problems, as employed in observational and experimental research. With supervised learning there is a clear distinction between explanatory and response variables. The objective is to predict responses, whether they be quantitative as with multiple regression or categorical as with logistic regression and multinomial logit models. Students work on research and programming assignments, exploring data, identifying appropriate models, and validating models. They utilize techniques for observational and experimental research design, data visualization, variable transformation, model diagnostics, and model selection. 

Prerequisites: MSDS 400 Math for Modelers and MSDS 401 Applied Statistics with R

This is an elective course for the Artificial Intelligence specialization.

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.

This is an elective course for the Artificial Intelligence specialization.

This course introduces data engineering concepts and technologies relevant to development and operations (DevOps). It reviews design principles and development processes for data pipelines in analytics applications, focusing on containerized microservices and cloud-native applications. It reviews data exchange formats, process concurrency control, communication protocols, application programming interfaces, distributed processing, and systems architecture. Students learn about automated deployment and scaling of batch, interactive, and streaming data pipelines. They learn how to design, implement, and maintain applications in cloud and on-premises environments. This is a programming-intensive course that includes a full-stack development project.

Recommended prior course: MSDS 431-DL Data Engineering with Go.

Prerequisites: (1) MSDS 400 Math for Modelers and (2) MSDS 420 Database Systems or CIS 417 Database Systems Design.

This is a required course for the Data Engineering specialization.

This course introduces technologies and systems for developing and implementing data science solutions. It takes a cloud-native approach to delivering analytics applications that are scalable, highly available, and easy to maintain. Students work on systems integration projects, automating stages of application development and using open-source programming languages and systems. They learn about continuous integration and continuous delivery (CI/CD) in the cloud, employing best practices in software engineering.

Recommended prior courses: (A) MSDS 431-DL Data Engineering with Go, (B) MSDS 432-DL Foundations of Data Engineering, and (C) MSDS 422-DL Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.

Prerequisites: (1) MSDS 400-DL Math for Modelers and (2) MSDS 420-DL Database Systems or CIS 417 Database Systems Design.

This is a required course for the Data Engineering specialization.

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.

This is an elective course for the Data Engineering specialization.

This comprehensive introduction to the Go programming language reviews data structures and algorithms, the Go standard library, and packages for communications, database access, analytics, and modeling. Students learn how to work within the Go programming environment, employing best practices in software engineering. They design, develop, and test programs for data science. They implement database servers and clients. And they learn how to run concurrent processes, as needed in distributed and parallel processing environments.

Prerequisites: None.

This is an elective course in the Data Engineering specialization.

This course reviews corporate finance and managerial accounting with a focus on technology companies and projects. Technology managers and entrepreneurs need to secure adequate funding, coordinate with other organizations, employ specialized knowledge workers, and satisfy multiple stakeholders. Company success and sustainable growth depend on adequate cashflow and profitability. In this course, students learn how to read and analyze financial statements and evaluate risks. They learn how to conduct breakeven and return-on-investment analyses with special reference to technology projects. Students work in groups, analyzing cases and assessing the financial position of firms. They work with spreadsheet programs, setting the stage for subsequent financial modeling work.

Prerequisite: None

This is a required course for the Technology Entrepreneurship specialization.

This course introduces concepts of leadership and organizational behavior. It builds on the premise that leadership is learned and discusses how to drive change in organizations at stages of conception, growth, and evolution. Students spend three weeks on technology-specific project management, in which they design a project plan using an agile approach. They learn how to incorporate the cross-industry standard processes for information system design, data analysis, and modeling. They practice executing plans in simulated business settings. Working on case studies and theory-based assignments, students see how to address leadership challenges unique to technology organizations. The course focuses on developing effective communication strategies and presentations that resonate across business and technical teams to emphasize vision and organizational acceptance.

Prerequisites: None.

This is an elective course for the Technology Entrepreneurship specialization.

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

Prerequisites: None

This is an elective course for the Technology Entrepreneurship specialization.