Curriculum

The 30-unit master of information systems management program is available for both part-time and full-time students.

A 15-unit graduate certificate is also offered and can be transferred into the degree program at any time. The certificate focuses on the managerial and technical acumen required to lead an IT enterprise including governance, IT architecture and infrastructure, service delivery, data management strategy, risk management, and fundamentals of cybersecurity.

*These courses are required to earn a Graduate Certificate in Information Systems Management.

**This program qualifies as a STEM program.

Pam Struttmann
Director of Student Recruiting
314-935-5484
sever@wustl.edu

Schedule a meeting with an adviser
Registration, Tuition Fees & Payment Policies

Qualified veterans: WashU McKelvey School of Engineering and the VA will cover 100 percent of your graduate tuition.

Although certificate programs do not qualify for federal loan programs, loans are still available for the master's degrees. For more information, contact Johanna Sengheiser (jsengheiser@wustl.edu).

Courses

Required

Operational Excellence and Service Delivery (T81-517)*
3 Units
Required

This course examines needed management skills and processes for the efficient and effective functioning of IT infrastructure and operational environments to deliver the right set of services, at the right quality, and at the right costs for internal and external users and customers. Specific emphasis is placed on understanding the roles of IT operations including system administration, network administration, help desk services, asset management, DevOps, and reporting. Students will study the application of industry best practice frameworks for the management of information technology infrastructure, operations, and development. Frameworks covered include the Information Technology Infrastructure Library (ITIL) and Control Objectives for Information and related Technology (COBIT). Through the application of continuous service improvement, students will understand the IT service lifecycle and will be able to assess the effectiveness of processes and services.


IT Architecture and Infrastructure (T81-540)*
3 Units
Required

This course will demonstrate the importance of understanding organizational strategy and goals, then designing and deploying an IT infrastructure that supports that strategy and those goals. The course will showcase how fundamental IT building blocks are integrated in meaningful ways in order to support IT services that drive core business outcomes. Through a hands-on enterprise architecture design project, students will learn to design IT infrastructure in a rational, innovative, and cost-effective manner. We will cover a range of enterprise architecture design considerations commonly faced by organizations as they enhance their services, launch new products, or expand to new markets.


IT Governance and Risk Management (T81-563)*
3 Units
Required

Firms with superior IT governance designed to support the organization's strategy achieve better performance—and higher profits—than firm with poor (or no) governance. Just as corporate governance aims to ensure quality decisions about all corporate assets, IT governance links IT decisions with company objectives and monitors performance and accountability. This course shows how the design and implementation of an IT governance system can transform IT from an expense to a profitable investment. Essential to IT governance is risk management. In this regard, students will learn key aspects of managing risk including risk identification, risk quantification, risk monitoring, risk control, and risk mitigation. Particular focus is placed on project risk management and understanding the process of risk identification, assessment, prevention, mitigation, and recovery; the roles of IT governance, auditing, and control of the confidentiality, integrity, and availability of data.


Enterprise Data Management (T81-575)*
3 Units
Required

Organizations have begun generating, collecting and accumulating more data at a faster pace than ever before. The advent of "Big Data" has proven to be both opportunity and challenge for contemporary organizations who are awash—even drowning—in data but starved for knowledge. Unfortunately, organizations have not developed comprehensive enterprise data strategy and management (EDM) practices that treat data as a strategic imperative. EDM is a comprehensive approach to defining, governing, securing, and maintaining the quality of all data involved in the business processes of an organization. EDM enables data-driven applications and decision-making by establishing policies and ownership of key data types and sources. The ultimate goal is to create a strategic context for the technology underpinnings of data life cycle management and ensure good stewardship of an organization's data. This course will cover the critical components of building an enterprise data strategy including, but not limited to, data strategy, data governance, data security, data architecture, data quality, data ownership, and metadata management.


Introduction to Cybersecurity (T83-559)*
3 Units
Required

This course is intended as a comprehensive introduction to the cybersecurity field. It covers a broad range of cyber security terms, definitions, historical perspectives, concepts, processes, technologies, and trends with a focus on managing risk and the employment of cybersecurity as an organizational enabler.


Capstone (T81-585)
3 Units
Required

The capstone course is the culmination of the Masters of Information Systems Management program. The capstone project provides the opportunity for students to employ the knowledge and skills they have gained from their coursework in a rigorous and systematic manner. Projects are sponsored by external corporate, government, and non-profit organizations and provide the opportunity to deliver meaningful research and recommendations for "real-world" IT challenges and problems.

Cybersecurity Emphasis (Choose 4)
Cybersecurity Technical Fundamentals (T83-560)
3 Units
Elective

This course presents a comprehensive survey of cybersecurity technology including basic theory and concepts. Students will gain hands-on familiarity of cybersecurity technology through lab exercises, in-class studios, and scenarios. Topics covered include security considerations surrounding operating systems, the web, email, databases, wireless, the cloud, and the Internet of Things. Also addressed are cryptography, secure software design, physical security, and human factors in cybersecurity.


Oversight for Excellence: Cybersecurity Management and Governance (T83-561)
3 Units
Elective

This course takes a comprehensive approach to the management of the organizational cybersecurity function. It also explores the principles of information technology governance. Coursework provides a deeper understanding of best practices for managing cyber security processes and meeting multiple needs of enterprise management by balancing the void between business risks, technical issues, control needs, and reporting metrics. Toward this end, the course addresses a range of topics necessary for success, including the elements of and how to establish a governance program, cybersecurity management frameworks, developing and implementing a cybersecurity strategy, deploying cybersecurity policy and controls, ensuring standards and regulatory compliance, functional and budgetary advocacy, interfacing with the C-Suite and Board, and talent acquisition and development.


Efficient and Effective Cybersecurity Operations (T83-562)
3 Units
Elective

In this course, students will gain understanding of what it takes to manage the people, process, and technology for effective and efficient day-to-day cybersecurity operations. Using the Cybersecurity Operations Center (CSOC) as the fundamental exemplar, students will learn the functions and processes that comprise a typical CSOC with an underlying focus on continually optimizing operations for agility and performance. Options for structuring the CSOC will be examined along with core CSOC functions and processes such as threat intelligence; monitoring, detection, and threat assessment; vulnerability management; incident response; prevention, including awareness training; partner and third-party coordination; analytics, metrics, and reporting; training; and CSOC technologies and instrumentation.


The Hacker Mindset: Cyber Attack Fundamentals (T83-567)
3 Units
Elective

This course is designed to provide an introductory understanding of how offensive security techniques practically operate. During this course students will use hacking techniques to compromise systems, collect data, and perform other tasks that fall under the generally understood use of the term "hacker." These techniques will be related to risk-based defensive security practices with a view toward enhancing the student's understanding of what it takes to be a successful "defender." By the conclusion of the course, students will have a baseline technical understanding of hacking techniques, will have executed offensive security operations, and will have increased technical understanding of what it takes to deal with cyber threats.

Management Emphasis (Choose 4)

Decision-Making & Optimization (T55-505)
3 Units
Required

Expand your ability to analyze and optimize complex business situations by leveraging the key data. Decision-making in today's complex world requires advanced analytical methods and tools, including mathematical modeling and quantitative techniques. Powerful tools for forecasting, finance, operations, production and logistics. Emerging technologies such as the Industrial Internet of Things (I-IoT) and Block Chain are enabling a whole new set of possibilities!


Developing Leadership for Professionals (T54-582)
3 Units
Elective

Provides knowledge about a variety of leadership approaches and how they may be effective in technological situations. The course concentrates on developing skills to actually lead in various situations. These include decision-making, problem solving, coaching, evaluating performance, selling ideas, and gaining commitment. Combines classroom, actual experiences, and reality-based feedback to hone skills resulting in a higher ability to lead.


Human Performance in the Organization (T55-583)
3 Units
Elective

Gain insights and practice with leading and managing people. This course addresses the management and leadership capabilities required to move into positions of greater responsibility, with a focus on technology-based organizations. Topics include leadership, culture, goals, motivation and performance, management of change, conflict and effectiveness, organizational development and work design. Because when a leader gets better, everyone gets better. (Note: Course delivery is compressed and largely online.)


Communication Excellence for Influential Leadership (T54-584)
3 Units
Elective

Exceptional communicators become extraordinary leaders. This course will guide students to learn to exceptionally communicate their message by applying refined nuances that inspire and transform those with whom they converse. Through a proven communicative process, students will acquire skills necessary to differentiate them as leaders. Students will learn how to communicate across a variety of settings using strategies that result in clear, vivid, and engaging exchanges. Students will practice: storytelling; creating and using clear visuals; engaging listeners; demonstrating passion when speaking; responding to questions with clarity and brevity, and, using their distinctive voice as a leadership asset. Each student will learn how to assess his or her own communication capabilities, adjust to different listeners, and how to evaluate speaker effectiveness and provide valuable feedback to others. Video recordings will be used to demonstrate incremental communicative changes throughout the course, and to show how these strategies bring about outstanding leadership. 


Leadership Seminar for Technology Professionals (T81-570)
3 Units
Elective

This seminar is designed to develop the leadership capacity of professionals working in the information technology and cybersecurity fields. Although domain expertise plays an important role in the success of a technology professional, it's when this expertise is integrated with the ability to lead people that transforms the merely competent into multi-dimensional force multipliers for the organization. In this course, students will participate in an immersive, seminar-based learning experience targeted toward professional and personal development on a range of essential leadership skills. Students will benefit from interaction with industry experts in the IT and cybersecurity fields and receive coaching support to achieve professional and personal goals. Each student will complete a series of self- and multi-rater assessments as well as a personal leadership development plan to gain insight and build competencies critical to effective leadership. Topics include creating a shared vision, strategy development, building and sustaining a healthy culture, essentials of finance and budgeting, driving results, energizing people for performance, innovation, emotional intelligence, navigating organizational politics, managing up, negotiations, stress resilience, talent coaching and development, effective communication, and time management.

 

Applied Data Analytics and Machine Learning Emphasis (Choose 4)
Applications of Deep Neural Networks (T81-558)
3 Units
Elective

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction.


Foundations of Analytics (T81-574)
3 Units
Elective

The steeply decreasing costs to gather, store, and process data has created a strong motivation for organizations to move toward "data driven" approaches to problem solving. As such, data analytics continues to grow rapidly in importance across industry, government, and non-profit organizations. This course seeks to equip students with a wide range of data analytics techniques that serve as the foundation for a broad range of applications including descriptive, inferential, predictive, and prescriptive analytics. Students will learn the process of building a data model as well as a variety of analytics techniques and under what situations they are best employed. Through lectures and practical exercises, students will become familiar with the computational mathematics that underpin analytics; the elements of statistical modeling and machine learning; model interpretation and assessment; and structured and unstructured data analysis. Students will also undertake a project to build an analytical model using a "real-world" data set.


Analytics Applications (T81-576)
3 Units
Elective

This course builds on the content taught in Enterprise Data Management and Foundations of Data Analytics. It focuses on the strategic, operational, tactical, and practical use of data analytics to inform decisions within an organization across a range of industry and government sector as well as within organizational functions. Students will be introduced to specific analytics techniques that are used currently by practitioners in areas of diagnostic, descriptive, predictive, and prescriptive analytics. Students will learn the critical phases of analytics including data preparation, model development, evaluation, validation, selection, and deployment. In so doing, students will learn to apply data analytics in order to optimize organizational processes, improve performance, and inform decision-making.


Applied Data Science for Practitioners (T81-577)
3 Units
Elective

Organizations are rapidly transforming the way they ingest, integrate, store, serve data, and perform analytics. In this course, students will learn the steps involved with designing and implementing data science projects. Topics addressed include: ingesting and parsing data from various sources, dealing with messy and missing data, transforming and engineering features, building and evaluating models, and visualizing results. Using Python based tools such as Numpy, Pandas, and Scikit-learn, students will complete a practical data science project that addresses the entire design and implementation process. Students will also become familiar with the best practices and current trends in data science including writing elegant code, documenting and version controlling, creating reproducible research in container platforms, and working in a cloud environment. Upon completion of the course, students will emerge equipped with data science knowledge and skills that can be applied from day one on the job.

Mathematical Data Analytics Emphasis (Choose 4)
Mathematical Statistics (L24 MATH 494)
3 Units
Elective
Theory of estimation, minimum variance and unbiased estimators, maximum likelihood theory, Bayesian estimation, prior and posterior distributions, confidence intervals for general estimators, standard estimators and distributions such as the Student-t and F-distribution from a more advanced viewpoint, hypothesis testing, the Neymann-Pearson Lemma (about best possible tests), linear models, and other topics as time permits. Prerequisite: CSE 131 or 200, Math 3200 and 493, or permission of the instructor.
Optimization (E35 ESE 415)
3 Units
Elective

Optimization problems with and without constraints. The projection theorem. Convexity, separating hyperplane theorems; Lagrange multipliers, Kuhn-Tucker-type conditions, duality; computational procedures. Optimal control of linear dynamic systems; maximum principles. Use of optimization techniques in engineering design. Prerequisites: Math 309 and ESE 318 or permission of instructor.


Introduction to Machine Learning (E81 CSE 417T)
3 Units
Elective

The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience. This course is a broad introduction to machine learning, covering the foundations of supervised learning and important supervised learning algorithms. Topics to be covered are the theory of generalization (including VC-dimension, the bias-variance tradeoff, validation, and regularization) and linear and non-linear learning models (including linear and logistic regression, decision trees, ensemble methods, neural networks, nearest-neighbor methods, and support vector machines). There will be two in-class exams, one in early October (tentatively October 10th), and one on the last day of class, December 5th. Prerequisites: CSE 247, ESE 326, Math 233, and Math 309 (can be taken concurrently).


Introduction to Artificial Intelligence (E81 CSE 511A)
3 Units
Elective

The discipline of artificial intelligence (AI) is concerned with building systems that think and act like humans or rationally on some absolute scale. This course is an introduction to the field, with special emphasis on sound modern methods. The topics include knowledge representation, problem solving via search, game playing, logical and probabilistic reasoning, planning, dynamic programming, and reinforcement learning. Programming exercises concretize the key methods. The course targets graduate students and advanced undergraduates. Evaluation is based on written and programming assignments, a midterm exam and a final exam. Prerequisites: CSE 347, ESE 326, Math 233


Data Mining (E81 CSE 514A)
3 Units
Elective

With the vast advancement in science and technology, data acquisition in large quantities are routinely done in many fields. Examples of large data include various types of data on the internet, high-throughput sequencing data in biology and medicine, extraterrestrial data from telescopes in astronomy, and images from surveillance camera in security. Mining a large amount of data through data mining has become an effective means to extracting knowledge from data. This course introduces the basic concepts and methods for data mining and provides hands-on experience for processing, analyzing and modeling structured and unstructured data. Homework problems, examines and programming assignments will be administrated throughout the course to enhance the learning. Prerequisites: CSE 247 and ESE 326 (or Math 320) or their equivalent, or permission of the instructor.

AI & Machine Learning Emphasis (Choose 4)

Introduction to Artificial Intelligence (E81 CSE 511A)
3 Units
Elective

The discipline of artificial intelligence (AI) is concerned with building systems that think and act like humans or rationally on some absolute scale. This course is an introduction to the field, with special emphasis on sound modern methods. The topics include knowledge representation, problem solving via search, game playing, logical and probabilistic reasoning, planning, dynamic programming, and reinforcement learning. Programming exercises concretize the key methods. The course targets graduate students and advanced undergraduates. Evaluation is based on written and programming assignments, a midterm exam and a final exam. Prerequisites: CSE 347, ESE 326, Math 233


Introduction to Machine Learning (E81 CSE 417T)
3 Units
Elective

The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience. This course is a broad introduction to machine learning, covering the foundations of supervised learning and important supervised learning algorithms. Topics to be covered are the theory of generalization (including VC-dimension, the bias-variance tradeoff, validation, and regularization) and linear and non-linear learning models (including linear and logistic regression, decision trees, ensemble methods, neural networks, nearest-neighbor methods, and support vector machines). There will be two in-class exams, one in early October (tentatively October 10th), and one on the last day of class, December 5th. Prerequisites: CSE 247, ESE 326, Math 233, and Math 309 (can be taken concurrently).


Data Mining (E81 CSE 514A)
3 Units
Elective

With the vast advancement in science and technology, data acquisition in large quantities are routinely done in many fields. Examples of large data include various types of data on the internet, high-throughput sequencing data in biology and medicine, extraterrestrial data from telescopes in astronomy, and images from surveillance camera in security. Mining a large amount of data through data mining has become an effective means to extracting knowledge from data. This course introduces the basic concepts and methods for data mining and provides hands-on experience for processing, analyzing and modeling structured and unstructured data. Homework problems, examines and programming assignments will be administrated throughout the course to enhance the learning. Prerequisites: CSE 247 and ESE 326 (or Math 320) or their equivalent, or permission of the instructor.


Machine Learning (E81 CSE 517A)
3 Units
Elective

This course assumes a basic understanding of machine learning and covers advanced topics at the frontier of the field in-depth. Topics to be covered include kernel methods (support vector machines, Gaussian processes), neural networks (deep learning), and unsupervised learning. Depending on developments in the field, the course will also cover some advanced topics, which may include learning from structured data, active learning, and practical machine learning (feature selection, dimensionality reduction). Prerequisites: CSE 247, CSE 417T, ESE 326, Math 233 and Math 309


Advanced Machine Learning (E81 CSE 519T)
3 Units
Elective

This course provides a close look at advanced machine learning algorithms -- their theoretical guarantees (computational learning theory) and tricks to make them work in practice. In addition, this course focuses on more specialized learning settings, including unsupervised learning, semi-supervised learning, domain adaptation, multi-task learning, structured prediction, metric learning and learning of data representations. Learning approaches may include graphical models, non-parametric Bayesian statistics, and technical topics such as sampling, approximate inference and non-linear function optimization. Mathematical maturity and general familiarity of machine learning is required. Prerequisites: CSE 517A, CSE 511A, and CSE 571A

Bridge Course (Choose 1)

Fundamentals of Information Technology (T81-506)
3 Units
Elective

This course is designed to provide a comprehensive survey of the Information Technology field. The enterprise relies heavily on information technology to generate value, efficiency, and effectiveness. As such, organizational leaders must ensure that the enterprise transforms to keep pace in the competitive environment. Globalization, mergers and acquisitions, and proliferation of new business and operating models require management to continuously reconsider technology infrastructures, organizational structures, process re-engineering, outsourcing, innovation, technology effectiveness, and the creation and management of data and knowledge. Given these challenges and opportunities, the IT professional has never been more crucial to organizational success. In this context, students will become familiar with core IT concepts, processes, and technology and gain an increased understanding of the crucial role of IT in the modern enterprise.

Meet our faculty

John Bailey Cloud computing professional at WashU, 18+ years IT industry experience

John Bailey

  • Adjunct Instructor
Asim Banskota

Asim Banskota

  • Adjunct Instructor
Mark Brooks Executive Vice President and Chief Information Officer of a Global Fortune 50 Healthcare Enterprise

Mark Brooks

  • Adjunct Instructor
Don Lang Retired - Division Director Boeing Military Aircraft (BMA) IT

Don Lang

  • Adjunct Instructor
Jeromey Farmer Practice Area Lead for Information Management & Analytics at Slalom. Helps companies solve business problems and build for the future through strategy, data science and innovative big data solutions.

Jeromey Farmer

  • Adjunct Instructor
Jeff Heaton Vice President, Data Scientist, Reinsurance Group of America (RGA), Senior Member of IEEE, and author of several books on machine learning and artificial intelligence.

Jeff Heaton

  • Adjunct Instructor
Rick Hermann Leads End-User Services in the Office of the Chief Information Officer at Washington University

Rick Hermann

  • Adjunct Instructor
Mike Jenkins Former Chief Information Security Officer at the United States Transportation Command. ISC2, CISSP, ISSEP, ISSMP and ITIL certified

Mike Jenkins

  • Adjunct Instructor
Dihui Lai

Dihui Lai

  • Adjunct Instructor
Ozzie Lomax Project Management (PMP) and Risk Management Professional (RMP) Certification 38 years Energy, Operations, IOT and Engineering SME CEO Lomax Consulting Group

Ozzie Lomax

  • Adjunct Instructor
Larry McLean The United States Transportation Command Subject Matter Expert for ITIL approach to IT Service Management

Larry McLean

  • Adjunct Instructor

Graduate Tuition

Full-time student 
(9-21 units)

$28,150/semester ($56,300/year)

Enrolled in more than 21 units

$28,150 (plus $2,346 per unit over 21 units)

Full-time student, 
enrolled in 8 or fewer units

$2,346/unit

Part-time student, 
enrolled in 8 or fewer units 

$1,994/unit (applies to SI and TG Prime, not GR)

Graduate Student Activity Fee 
(full-time students)

$15/semester

Health & Wellness Fee 
(full-time students)

$524/year

 

Contact

Johanna Sengheiser
Graduate Financial Aid Analyst & Accountant
314-935-6183

Engineering Graduate Admissions
314-935-5830
engineeringgradadmissions@wustl.edu

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