Ms in Data Science for MIT MicroMaster's Graduates, Curriculum & Specializations | Northwestern University School of Professional Studies - Northwestern School of Professional Studies

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

View MSDS 420-DL Sections

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.

MSDS 485-DL Data Governance, Ethics, and Law

View MSDS 485-DL Sections

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.

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

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.

Prerequisites:  None

This course is required for the Technology Entrepreneurship specialization.  Students in that specialization cannot take this course to fulfill both the core and specialization requirement.

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 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 is required for the Technology Entrepreneurship and Analytics Management specializations.  Students in either of those specializations cannot take this course to fulfill both the core and specialization requirement.

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.

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 is required for the Analytics Management specialization.  Students in that specialization cannot take this course to fulfill both the core and specialization requirement.

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 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 Data Scientists and MSDS 401-DL Applied Statistics with R.

View MSDS 410-DL Sections

This course introduces data modeling for studies in which there is no clear distinction between explanatory and response variables. The objective may be to explain relationships among many continuous variables in terms of underlying dimensions, latent variables, or factors, as with principal components and factor analysis. The objective may be to find a lower-dimensional representation for multivariate cross-classified data, as with log-linear models. The objective may be to construct a visualization of variables or objects, as with traditional multidimensional scaling and t-distributed stochastic neighbor embedding. Or the objective may be to identify groups of variables and/or objects that are similar to one another, as with cluster analysis and biclustering. Students work on research and programming assignments, exploring multivariate data and methods.

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

Prerequisites: MSDS 410-DL Data Modeling for Supervised Learning.

View MSDS 411-DL Sections

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 Data Modeling for Unsupervised Learning. 

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

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.

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 computer architecture and scalable systems for data science. They learn how to package and distribute containerized computer software. They apply tools of systems analysis, evaluating end-to-end performance and resource utilization in batch, interactive, and streaming environments. Students review formats and protocols for application programming interfaces. They compare data models, resource requirements, and performance of applications implemented with relational versus graph database systems.

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.

This course will run starting in fall 2019.

This course introduces analytics application engineering within an integrated environment and full-stack development process. Students learn how to implement client-side, web-based applications using a model-view-controller framework. Students work with server-side systems for responding to website requests and database queries. Students learn how to prepare indices for efficient and relevant search across large document collections. They learn how to implement analytics applications for processing real-time data streams, finding information from databases and document collections, making service and product recommendations, and detecting anomalies or security violations. 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.

This course will run starting in fall 2019.

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 Data Modeling for Unsupervised Learning.

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

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.

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. The course introduces the mathematics of network science, including random graph, small world, and preferential attachment models. Students use methods of network science with applications to information and social networks. They compute a variety of network metrics as they analyze software systems, website structure, and user interactions through electronic communications and 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.

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.

This is a required course for the Artificial Intelligence specialization.

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.

Drawing upon previous course work in data science, this course builds 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 Data Modeling for Unsupervised Learning.

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.

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.

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

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.

This is a required course for the Artificial Intelligence specialization.

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.

This course introduces knowledge representation as a subfield of artificial intelligence. It reviews methods for developing knowledge-based systems, providing examples of their use in intelligent applications and agents. The course makes heavy use of graph databases for storing information about words in semantic networks and for storing information about relationships among people, places, events, and things. Students learn how to encode and access knowledge on the World Wide Web. They learn how to use knowledge bases for automated reasoning and question answering. This is a project-based course with extensive programming assignments. Recommended prior courses: MSDS 453 Natural Language Processing and MSDS 458 Artificial Intelligence and Deep Learning.

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.

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

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

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.

This course introduces reinforcement learning as an approach to intelligent systems, emphasizing applications such as robotic processes automation, conversational agents and robotics that mimic human behavior. Students implement intelligent agents to solve both discrete- and continuous-valued sequential decision-making problems. Students develop, debug, train, visualize, and customize programs in a variety of learning environments. The course reviews Markov decision processes, dynamic programming, temporal difference learning, Monte Carlo reinforcement learning, eligibility traces, the role of function approximation, and the integration of learning and planning. This is a case study and project-based course with a substantial programming component.

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

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.

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

View MSDS 470-DL Sections

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

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.

Topics vary from term to term.

Prerequisites: Vary by topic.

Topics vary from term to term. Prerequisites: Vary by topic.

Topics vary from term to term.

Prerequisites: Vary by topic.

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

 View MSDS 410-DL Sections

This course introduces data modeling for studies in which there is no clear distinction between explanatory and response variables. The objective may be to explain relationships among many continuous variables in terms of underlying dimensions, latent variables, or factors, as with principal components and factor analysis. The objective may be to find a lower-dimensional representation for multivariate cross-classified data, as with log-linear models. The objective may be to construct a visualization of variables or objects, as with traditional multidimensional scaling and t-distributed stochastic neighbor embedding. Or the objective may be to identify groups of variables and/or objects that are similar to one another, as with cluster analysis and biclustering. Students work on research and programming assignments, exploring multivariate data and methods.

Prerequisites: MSDS 410-DL Data Modeling for Supervised Learning

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.

View MSDS 474-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.

Prerequisites:  None

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 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.

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  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.

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. 

 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.

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.

Prerequisites: None

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

Choose from four electives above.

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.

Sections 55, 56, 57: These capstone sections are appropriate for any student in the MSDS program. Students work on group projects that reflect  learning objectives across the MSDS program: business, modeling, and information technology.

Section 58:  This capstone section is designed for students in the Analytics & Modeling specialization. While it draws on learning objectives across the MSDS program (business, modeling, and information technology), students work individually on projects with an Analytics & Modeling focus.

Section 59:   This capstone section is designed for students in the Analytics Management specialization. While it draws on learning objectives across the MSDS program (business, modeling, and information technology), students work individually on projects with an Analytics Management  focus.

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

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 adviser.

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