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

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 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 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 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.

View MSDS 476-DL Sections

This course explores cutting-edge developments in computational linguistics and machine learning, with a focus on deep learning techniques. Students work with unstructured and semi-structured text, transforming text into numerical vectors and converting higher-dimensional vectors into lower-dimensional ones for analysis and modeling. The course covers parts-of-speech parsing, information extraction, semantic processing, text classification, sentiment analysis, text embeddings, topic modeling, text summarization and generation, and question answering. Students explore large-scale language models, particularly generative pretrained transformers (GPTs). 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.

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 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 Data Engineering with Go.

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

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 Data Engineering with Go, (B) MSDS 432 Foundations of Data Engineering, and (C) MSDS 422 Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.

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

 View MSDS 434-DL Sections

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

View MSDS 474-DL Sections

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.

Prerequisites: None

View MSDS 400-DL Sections

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.

Prerequisite: None

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.

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

View MSDS 485-DL Sections

MSDS 498 Capstone

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.

This course introduces the scientific method and research design for data science. It distinguishes between primary and secondary research, drawing on survey, observational, and experimental studies. Students learn about sampling techniques and ways of obtaining relevant data. They see how to prepare data for modeling and analysis. They employ feature engineering, constructing new measures from original measures. They learn how to assess the reliability and validity of measures, construct valid research designs, and build trustworthy models. Numerous case studies illustrate rational decision making guided by science.

Prerequisites: None.

View MSDS 402-DL Sections

This is a case study course that gives students an opportunity to gain experience solving business problems and applying core skills needed for data science technical and leadership roles. The course introduces digital transformation, industry use cases, designing and measuring analytics projects, data considerations, data governance, digital trust and ethics, enterprise architecture and technology platforms, and organizational change management. Students act as data scientists, as strategists and leaders, evaluating alternative analytics projects and solving digital transformation challenges. Students learn how to apply a step-by-step development process, creating digital transformation roadmaps and addressing real-world business problems.

Prerequisites: None.

View MSDS 403-DL Sections

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. 

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

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

View MSDS 410-DL Sections

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.

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

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

View MSDS 411-DL Sections

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 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 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.

 View MSDS 431-DL Sections

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.

This is a required course for the Data Engineering Specialization.

Recommended prior course: MSDS 431 Data Engineering with Go.

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

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.

This is a required course for the Data Engineering specialization.

Recommended prior courses: (1) MSDS 432 Foundations of Data Engineering and (2) MSDS 422 Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.

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

 View MSDS 434-DL Sections

This course introduces design principles and best practices for implementing large-scale systems for data ingestion, processing, storage, and analytics. Students learn about cloud-based computing, including infrastructure-, platform-, software-, and database-as-a-service systems for data science. They evaluate system performance and resource utilization in batch, interactive, and streaming environments. They create and run performance benchmarks comparing browser-based and desktop applications. The evaluate key-value stores, relational, document, graph, and graph-relational databases.

Recommended prior course: MSDS 430 Python for Data Science or MSDS 431 Data Engineering with Go.

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 course introduces the full-stack development process for data science. Students learn how to implement end-to-end applications using web-based technologies and the model-view-controller framework. They build real-time application servers, backend databases, and front-end interfaces. They create microservices that deploy machine learning algorithms. The course shows how to extract information from online resources. Graph theory, information retrieval, social media, and text analytics are discussed and employed in real-world applications. Students also learn design principles for implementing relational, document, and graph databases. This is a project-based course with a strong programming component.

Prior to fall 2023, this course was titled Real-­Time Interactive Processing and Analytics.

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.

View MSDS 440-DL Sections

This application engineering and analytics course introduces stream processing and the end-to-end data pipeline. Real-time data sources include electronic monitoring of continuous processes, observing digital communications and social interaction, and tracing the movement of goods through production lines, warehouses, and distribution channels. The course demonstrates a stream-processing technology stack designed for high throughput and low latency. Students analyze business transactions and processes, event logs, workflows, and consumer behavior. They learn about operations, logistics, and supply chain management. This is a case study and project-based course with a strong programming component.

Prior to fall 2023, this course was titled Real-Time Stream Processing and Analytics.

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.

View MSDS 442-DL Sections

This course reviews applications of data science in marketing, the strategic marketing process, and the design of marketing surveys and experiments. Students explore methods for understanding consumer preferences, market segments, and competitive brands and products. Students address problems in new product design and pricing. They study the marketing mix, highlighting the effects of advertising and promotion. And they are introduced to algorithms and methods for digital marketing.

Recommended prior courses: 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.

View MSDS 450-DL Sections

This course introduces applications of machine learning techniques to finance. Financial data presents special challenges to standard machine learning techniques, engendering significant adaptations. Topics include a basic introduction to finance, nuances of financial features engineering, techniques to avoid various biases during model training, and example applications such as meta-labeling.

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

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 course shows how to acquire and analyze information from the web and reviews web analytics and search performance metrics. It introduces the mathematics of network science, including random graph, small world, and preferential attachment models. Students compute network metrics, analyzing structure and connections in information and social networks. They study user interactions through electronic communications and social media. They work with graph algorithms and graph databases. This is a case study and project-based course with a strong programming component.

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 course explores cutting-edge developments in computational linguistics and machine learning, with a focus on deep learning techniques. Students work with unstructured and semi-structured text, transforming text into numerical vectors and converting higher-dimensional vectors into lower-dimensional ones for analysis and modeling. The course covers parts-of-speech parsing, information extraction, semantic processing, text classification, sentiment analysis, text embeddings, topic modeling, text summarization and generation, and question answering. Students explore large-scale language models, particularly generative pretrained transformers (GPTs). This is a project-based course with extensive programming assignments.

This is a required course for the Artificial Intelligence specialization.

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 advanced modeling course begins by reviewing probability theory and models. Students learn principals of random number generation and Monte Carlo methods for classical and Bayesian statistics. They are introduced to applied probability models and stochastic processes, including Markov Chains, exploring applications in business and scientific research. Students work with open-source and proprietary systems, implementing discrete event and agent-based simulations. This is a case study and project-based course with an extensive programming component.

Recommended prior course: MSDS 460 Decision Analytics.

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 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 Math for Modelers and MSDS 401 Applied Statistics with R.

An introduction to sports performance measurement and analytics, this course reviews the 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 Math for Modelers and MSDS 401 Applied Statistics with R.

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 Math for Modelers and MSDS 401 Applied Statistics with R.

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.nts.

This is a required course for the Artificial Intelligence specialization.

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 course reviews knowledge-based systems, intelligent applications, and conversational agents. It uses knowledge graphs to store information about entities and their relationships, where entities represent words, documents, people, organizations, products, places, or other things. Students design graph data models and implement knowledge bases in graph-relational databases. Drawing on these knowledge bases, as well as large-scale language models and inference algorithms, students build recommendation systems and end-to-end applications for information retrieval, information extraction, and question answering.

Recommended prior courses: MSDS 431 Data Engineering with Go and MSDS 453 Natural Language Processing.

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.

View MSDS 459-DL Sections

This course reviews deep learning methods for vision. Students work with raw image files, including digital representations of photographs, hand-written documents, x-rays, and sensor images. They process image data, converting pixels into numeric tensors for subsequent analysis and modeling. The course illustrates real-world applications for visual exploration, object recognition, image classification, facial recognition, remote sensing, navigation, and medical diagnostics. This is a project-based course with extensive programming assignments.

Recommended prior course: MSDS 458 Artificial Intelligence and Deep Learning.

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.

 View MSDS 462-DL Sections

This course introduces reinforcement learning as an approach to intelligent systems. It reviews Markov decision processes, dynamic programming, temporal difference learning, Monte Carlo and deep reinforcement learning, eligibility traces, and function approximation. Students implement intelligent agents, solving sequential decision-making problems. They develop, debug, train, and visualize the results of programs. They see how to integrate learning and planning. This is a case study and project-based course with a substantial programming component.

Recommended prior course: MSDS 458 Artificial Intelligence and Deep Learning.

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.

  View MSDS 464-DL Sections

This course prepares students to establish and run a technology-focused entrepreneurial organization. It identifies opportunities for technology products and services, including opportunities in data science, machine learning, and artificial intelligence. Students review methods of industry and market analysis to guide competitive strategy. They learn how to transform ideas into successful businesses, identifying the right data, information technology, and human resources, and aligning with unmet market demand. They learn how to deploy efficient operating models for independent and enterprise startups. They learn about growing a network of people and obtaining capital assets, creating innovative intellectual property, sharpening unique competitiveness, and making product development and marketing choices. Students develop business plans and make presentations for starting entrepreneurial ventures.

This is a required course for the Technology Entrepreneurship specialization.

Prerequisites: None.

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 course reviews corporate finance and managerial accounting with a focus on technology projects. It shows how the cycle of accounting, valuation, financial markets, cost of capital, and the real economy affect firm performance. Technology managers and entrepreneurs need to assess company needs in terms of workflow, coordination with other organizations, satisfying multiple stakeholders, and employing highly specialized knowledge professionals. Students learn how to read financial statements and evaluate risks associated with technology projects. They learn how to conduct breakeven and return-on-investment analyses. The course provides in-depth coverage of spreadsheet programming methods, setting the stage for subsequent financial modeling work. Students create business plans for technology firms, evaluating new ventures and justifying capital investments.

This is a required course for the Analytics Management specialization.

Prerequisites: None

This course introduces best practices in project management, covering the full project life cycle with a focus on globally accepted standards. The course introduces traditional/waterfall, hybrid, and iterative/agile approaches to project management. Regarding traditional methods, the course reviews project integration management, portfolio and stakeholder management, chartering, scope definition, estimation, precedence diagrams, and the critical path method. It also reviews scheduling, risk analysis and management, resource loading and leveling, Gantt charts, earned value analysis and performance indices for project cost and schedule control. By applying methods discussed in this course, students will be able to execute information systems and data science projects more effectively.

Prerequisites: None.  

View MSDS 475-DL Sections

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.

This is a required course for the Analytics Management specialization.

Prerequisites: None. 

View MSDS 476-DL Sections

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.

View MSDS 480-DL Sections