UTexas

Management Information Systems (MIS)

MIS 301  Introduction to Information Technology Management  3 Hours  

3 Lecture Hours  0 Lab Hours  
Explores how information technology helps to achieve competitive advantage and improve decision making, business processes, operations, and organizational design. Uses a cross-functional perspective to recognize the role of technology across business activities of management, finance, marketing, human resources, and operations. Management Information Systems 301 and 301H may not both be counted. Three lecture hours a week for one semester.
Core Curriculum: 093  
Restrictions: Restricted to students in the McCombs School of Business.  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Lower Division  

MIS 301H  Introduction to Information Technology Management: Honors  3 Hours  

3 Lecture Hours  0 Lab Hours  
Explores how information technology helps to achieve competitive advantage and improve decision making, business processes, operations, and organizational design. Uses a cross-functional perspective to recognize the role of technology across business activities of management, finance, marketing, human resources, and operations. Management Information Systems 301 and 301H may not both be counted. Three lecture hours a week for one semester.
Core Curriculum: 093  
Restrictions: Restricted to students admitted to the McCombs School of Business Honors Program.  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Lower Division  

MIS 302F  Foundations of Information Technology Management  3 Hours  

3 Lecture Hours  0 Lab Hours  
Explores how information technology helps to achieve competitive advantage and improve decision making, business processes, operations, and organizational design. Uses a cross-functional perspective to recognize the role of technology across business activities of management, finance, marketing, human resources, and operations. Management Information Systems 301 and 302F may not both be counted. Three lecture hours a week for one semester.
Core Curriculum: 093  
Restrictions: Restricted to non-McCombs School of Business majors.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Lower Division  

MIS 304  Introduction to Problem Solving and Programming  3 Hours  

3 Lecture Hours  0 Lab Hours  
Programming skills for creating easy-to-maintain systems for business applications. Object-oriented and structured methodologies with Python. Only one of the following may be counted: Business Analytics 304, 305, or Management Information Systems 304. Three lecture hours a week for one semester.
Same as: BAX 304  
Restrictions: Restricted to students in the McCombs School of Business.  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Lower Division  

MIS 310  Introduction to Management Information Systems  3 Hours  

(TCCN: BCIS 1305, BCIS 1405)  
3 Lecture Hours  0 Lab Hours  
Basic computer terminology, hardware and software, communications technology, graphics, systems analysis and design, and issues arising out of the rapidly evolving field of information systems. Students are expected to achieve a working knowledge of personal computer software, including operating system software and environments, as well as spreadsheets, analytical graphics, databases, and presentation software. Hands-on experience with the Internet and use of electronic mail. Three lecture hours a week for one semester.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Lower Division  

MIS 119S, 219S, 319S, 419S, 519S, 619S, 719S, 819S, 919S  Topics in Management Information Systems  1-9 Hours  

0 Lecture Hours  0 Lab Hours  
This course is used to record credit the student earns while enrolled at another institution in a program administered by the University's Study Abroad Office. Credit is recorded as assigned by the study abroad adviser in the Department of Information, Risk, and Operations Management. University credit is awarded for work in an exchange program; it may be counted as coursework taken in residence. Transfer credit is awarded for work in an affiliated studies program.
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Lower Division  

MIS 325  Database Management  3 Hours  

3 Lecture Hours  0 Lab Hours  
Beginning and intermediate topics in data modeling for relational database management systems. Only one of the following may be counted: Business Analytics 325, 327, or Management Information Systems 325. Three lecture hours a week for one semester.
Same as: BAX 325  
Restrictions: Restricted to students in the McCombs School of Business.  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 129S, 229S, 329S, 429S, 529S, 629S, 729S, 829S, 929S  Topics in Management Information Systems  1-9 Hours  

0 Lecture Hours  0 Lab Hours  
This course is used to record credit the student earns while enrolled at another institution in a program administered by the University's Study Abroad Office. Credit is recorded as assigned by the study abroad adviser in the Department of Information, Risk, and Operations Management. University credit is awarded for work in an exchange program; it may be counted as coursework taken in residence. Transfer credit is awarded for work in an affiliated studies program.
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Upper Division  

MIS 332F  Topics in Foundations of Management Information Systems  3 Hours  

3 Lecture Hours  0 Lab Hours  
Three lecture hours a week for one semester.
Restrictions: Restricted to non-McCombs School of Business majors.  
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Upper Division  

MIS 333K  Web Application Development  3 Hours  

3 Lecture Hours  0 Lab Hours  
Concepts and practices of information systems. Advanced programming techniques used to generate menu-driven applications. Three lecture hours a week for one semester.
Pre/Corequisites: Business Analytics 304, 305, or Management Information Systems 304; and Business Analytics 325, 327, or Management Information Systems 325.  
Restrictions: Restricted to students in a business major.  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 140S, 240S, 340S, 440S, 540S, 640S, 740S, 840S, 940S  Topics in Management Information Systems  1-9 Hours  

0 Lecture Hours  0 Lab Hours  
This course is used to record credit the student earns while enrolled at another institution in a program administered by the University's Study Abroad Office or the school's BBA Exchange Programs. Credit is recorded as assigned by the study abroad adviser in the Department of Information, Risk, and Operations Management. University credit is awarded for work in an exchange program; it may be counted as coursework taken in residence.
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Upper Division  

MIS 366P  Management Information Systems Practicum  3 Hours  

3 Lecture Hours  0 Lab Hours  
Students apply skills in their major area and focus on additional project management skills through group projects conducted in a professional setting. Students may work with a private or a public enterprise. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Forty-five semester hours of college coursework and consent of instructor.  
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 171T  Topics in Management Information Systems  1 Hour  

1 Lecture Hours  0 Lab Hours  
One lecture hour a week for one semester.
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Upper Division  

MIS 372T  Topics in Management Information Systems  3 Hours  

3 Lecture Hours  0 Lab Hours  
Three lecture hours a week for one semester.
Pre/Corequisites: Additional prerequisites vary with the topic.  
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Upper Division  
Topic 1: Open Innovation. Management Information Systems 372T (Topic 1) and 373 (Topic 21) may not both be counted.

Topic 2: IT Audit and Security. Management Information Systems 372T (Topic 2) and 373 (Topic 22) may not both be counted.

Topic 3: Fundamentals of Health IT. Management Information Systems 372T (Topic 3) and 373 (Topic: Fundamentals of Health IT) may not both be counted.

Topic 4: Mobile Apps and Web Services. Explore mobile and web services technology models and architectures. Management Information Systems 372T (Topic 4) and 373 (Topic 24) may not both be counted. Additional prerequisite: Management Information Systems 333K.

Topic 9: Enterprise Computing. Management Information Systems 372T (Topic 9) and 373 (Topic 19) may not both be counted. Additional prerequisite: Management Information Systems 333K, or equivalent programming experience.

Topic 11: Advanced Analytics Programming. Focus on data analysis using the Python programming language. Explore machine learning tasks such as classification and clustering. Only one of the following may be counted: Business Analytics 372 (Topic 1), 372T (Topic 11), Management Information Systems 372T (Topic 11), 373 (Topic 11). Same as: BAX 372T.11 Additional prerequisite: Business Analytics 304 or Management Information Systems 304. Restrictions: Restricted to students in a business major.

Topic 12: Information Technology for Supply Chains. Learn how information technology is used to coordinate supply chain activities across different industries. Only one of the following may be counted: Management Information Systems 373 (Topic 13), 372T (Topic 12), Operations Management 337 (Topic 4), 372T (Topic 12). Same as: O M 372T.12 Additional prerequisite: Management Information Systems 301, 301H, or 302F.

Topic 17: Health Care Analytics. Explore key management challenges and how data may be leveraged to guide decisions and improve operations, with the unifying theme of providing health care services in a manner that leads to lower cost and higher quality. Only one of the following may be counted: Business Analytics 372 (Topic 17), 372T (Topic 17), Management Information Systems 373 (Topic 26), 372T (Topic 17) Operations Management 337 (Topic 8), 372T (Topic 17). Same as: BAX 372T.17, O M 372T.17

Topic 22: Predictive Analytics and Data Mining. Introduction to data mining problems and tools to enhance managerial decision making at all levels of the organization. Discuss scenarios, including the use of data mining to support customer relationship management (CRM) decisions, decisions in the entertainment industry, financial trading, and even professional sports teams. Only one of the following may be counted: Business Analytics 372 (Topic 2), 372T (Topic 22), Management Information Systems 373 (Topic 17), 372T (Topic 22), Marketing 372 (Topic 22), 372T (Topic 22). Same as: BAX 372T.22, MKT 372T.22 Additional prerequisite: Statistics 301 or 301H.

Topic 23: Social Media Analytics. Introduction to social network analysis for business value using statistical optimization and decision theory. Analyze online search and conversation data for market sensing, sentiments, product quality, reputation, recommendations, and brand awareness. Only one of the following may be counted: Business Analytics 372 (Topic 23), 372T (Topic 23), Management Information Systems 372T (Topic 23), 373 (Topic 23). Same as: BAX 372T.23 Restrictions: Restricted to students in a business major.

Topic 24: Data-Driven Revenue Management. Use data-driven tactics and strategies to predict consumer demand and optimize price and capacity/inventory availability to maximize revenue or profit. Explore demand analytics and revenue management techniques, produce rigorous and practical insights, and develop data analysis and optimization skills, based on business cases, datasets, and exercises. Examine diverse contexts and industries, such as conventional retailing, e-commerce, airline/hotel, and contemporary platform and cloud services (e.g., Uber, Dropbox, Netflix, AWS), by utilizing software (Excel, R, or Python) to build models, conduct optimization, and develop insights for a variety of revenue management examples. Only one of the following may be counted: Business Analytics 372T (Topic 24), Management Information Systems 372T (Topic 24), Operations Management 372T (Topic 24). Same as: BAX 372T.24, O M 372T.24

Topic 25: User Generated Content Analytics. Focus on a gamut of questions ranging from strategic to operational matters pertaining to a firm's social media initiatives, metrics to capture relevant outcomes, and predictive analysis to link social media chatter to business performance. Only one of the following may be counted: Business Analytics 372 (Topic 4), 372T (Topic 25), Management Information Systems 372T (Topic 25), 373 (Topic 25). Same as: BAX 372T.25
  

MIS 372T.1  Open Innovation  3 Hours  

3 Lecture Hours  0 Lab Hours  
Management Information Systems 372T (Topic 1) and 373 (Topic 21) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 372T.11  Advanced Analytics Programming  3 Hours  

3 Lecture Hours  0 Lab Hours  
Focus on data analysis using the Python programming language. Explore machine learning tasks such as classification and clustering. Only one of the following may be counted: Business Analytics 372 (Topic 1), 372T (Topic 11), Management Information Systems 372T (Topic 11), 373 (Topic 11).
Same as: BAX 372T.11  
Pre/Corequisites: Business Analytics 304 or Management Information Systems 304.  
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 372T.12  Information Technology for Supply Chains  3 Hours  

3 Lecture Hours  0 Lab Hours  
Learn how information technology is used to coordinate supply chain activities across different industries. Only one of the following may be counted: Management Information Systems 373 (Topic 13), 372T (Topic 12), Operations Management 337 (Topic 4), 372T (Topic 12).
Same as: O M 372T.12  
Pre/Corequisites: Management Information Systems 301, 301H, or 302F.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 372T.17  Health Care Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Explore key management challenges and how data may be leveraged to guide decisions and improve operations, with the unifying theme of providing health care services in a manner that leads to lower cost and higher quality. Only one of the following may be counted: Business Analytics 372 (Topic 17), 372T (Topic 17), Management Information Systems 373 (Topic 26), 372T (Topic 17) Operations Management 337 (Topic 8), 372T (Topic 17).
Same as: BAX 372T.17, O M 372T.17  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 372T.2  IT Audit and Security  3 Hours  

3 Lecture Hours  0 Lab Hours  
Management Information Systems 372T (Topic 2) and 373 (Topic 22) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 372T.22  Predictive Analytics and Data Mining  3 Hours  

3 Lecture Hours  0 Lab Hours  
Introduction to data mining problems and tools to enhance managerial decision making at all levels of the organization. Discuss scenarios, including the use of data mining to support customer relationship management (CRM) decisions, decisions in the entertainment industry, financial trading, and even professional sports teams. Only one of the following may be counted: Business Analytics 372 (Topic 2), 372T (Topic 22), Management Information Systems 373 (Topic 17), 372T (Topic 22), Marketing 372 (Topic 22), 372T (Topic 22).
Same as: BAX 372T.22, MKT 372T.22  
Pre/Corequisites: Statistics 301 or 301H.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 372T.23  Social Media Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Introduction to social network analysis for business value using statistical optimization and decision theory. Analyze online search and conversation data for market sensing, sentiments, product quality, reputation, recommendations, and brand awareness. Only one of the following may be counted: Business Analytics 372 (Topic 23), 372T (Topic 23), Management Information Systems 372T (Topic 23), 373 (Topic 23).
Same as: BAX 372T.23  
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 372T.24  Data-Driven Revenue Management  3 Hours  

3 Lecture Hours  0 Lab Hours  
Use data-driven tactics and strategies to predict consumer demand and optimize price and capacity/inventory availability to maximize revenue or profit. Explore demand analytics and revenue management techniques, produce rigorous and practical insights, and develop data analysis and optimization skills, based on business cases, datasets, and exercises. Examine diverse contexts and industries, such as conventional retailing, e-commerce, airline/hotel, and contemporary platform and cloud services (e.g., Uber, Dropbox, Netflix, AWS), by utilizing software (Excel, R, or Python) to build models, conduct optimization, and develop insights for a variety of revenue management examples. Only one of the following may be counted: Business Analytics 372T (Topic 24), Management Information Systems 372T (Topic 24), Operations Management 372T (Topic 24).
Same as: BAX 372T.24, O M 372T.24  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 372T.25  User Generated Content Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Focus on a gamut of questions ranging from strategic to operational matters pertaining to a firm's social media initiatives, metrics to capture relevant outcomes, and predictive analysis to link social media chatter to business performance. Only one of the following may be counted: Business Analytics 372 (Topic 4), 372T (Topic 25), Management Information Systems 372T (Topic 25), 373 (Topic 25).
Same as: BAX 372T.25  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 372T.3  Fundamentals of Health IT  3 Hours  

3 Lecture Hours  0 Lab Hours  
Management Information Systems 372T (Topic 3) and 373 (Topic: Fundamentals of Health IT) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 372T.4  Mobile Apps and Web Services  3 Hours  

3 Lecture Hours  0 Lab Hours  
Explore mobile and web services technology models and architectures. Management Information Systems 372T (Topic 4) and 373 (Topic 24) may not both be counted.
Pre/Corequisites: Management Information Systems 333K.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 372T.9  Enterprise Computing  3 Hours  

3 Lecture Hours  0 Lab Hours  
Management Information Systems 372T (Topic 9) and 373 (Topic 19) may not both be counted.
Pre/Corequisites: Management Information Systems 333K, or equivalent programming experience.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 374  Business System Development  3 Hours  

3 Lecture Hours  0 Lab Hours  
Provides foundation in business system analysis, project management, planning, design, and implementation using basic business knowledge and computer skills. Three lecture hours a week for one semester.
Pre/Corequisites: Seventy-five semester hours of college coursework, including Management Information Systems 333K.  
Restrictions: Restricted to students in a business major.  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 375  Strategic Information Technology Management  3 Hours  

3 Lecture Hours  0 Lab Hours  
Designed to develop an understanding and appreciation for the role of information technology in the context of a firm's strategy. Explores the impact of information technology on the economy and business performance, the emergence of electronic business applications and organizational and market transformation, and the nature of technology-driven business models and strategies. Three lecture hours a week for one semester.
Core Curriculum: 010  
Pre/Corequisites: Upper-division standing.  
Restrictions: Restricted to students in a business major.  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 179, 379  Independent Research in Management Information Systems  1,3 Hours  

1,3 Lecture Hours  0 Lab Hours  
Only one of the following may be counted: Accounting 379C, Business Administration Honors 379, Business, Government, and Society 379, Finance 379C, International Business 379C, Legal Environment of Business 379, Management 379C, Management Information Systems 379, Marketing 379C, Operations Management 379, Risk Management 379. Conference course.
Pre/Corequisites: Eighteen hours of coursework in business and economics, six of which must be upper-division; Business Analytics 304, 305, or Management Information Systems 304 and Business Analytics 325, 327, or Management Information Systems 325, and consent of instructor; and written approval before the first meeting of the course from the department chair's office, on forms provided for that purpose.  
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

MIS 380  Topics in Seminar in Organizational Communication  3 Hours  

3 Lecture Hours  0 Lab Hours  
Selected topics in organizational communication, written and oral. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.  
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Doctoral  
Topic 1: Research Methodology in Business and Organizational Communication.

Topic 2: Projects, Proposals, and Presentations. Communicating effectively in business using advanced writing and presentation concepts and techniques to increase individual and team effectiveness.

Topic 3: Advanced Report Writing, Professional Reports, and Other Scholarly
  

MIS 380.1  Research Methodology in Business and Organizational Communication  3 Hours  

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 380.2  Projects, Proposals, and Presentations  3 Hours  

3 Lecture Hours  0 Lab Hours  
Communicating effectively in business using advanced writing and presentation concepts and techniques to increase individual and team effectiveness.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 380.3  Advanced Report Writing, Professional Reports, and Other Scholarly Papers  3 Hours  

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 180D, 280D, 380D  Database Management  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Explore designing, building, and implementing a relational database to improve company operations and reporting. Examine how databases affect analytics and app development. For each semester hour of credit earned, the equivalent of one lecture hour a week for one semester.
Pre/Corequisites: Graduate standing and consent of instructor.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 380N  Topics in Information Management  3 Hours  

3 Lecture Hours  0 Lab Hours  
Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.  
Grading: Letter Grade  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Doctoral  
Topic 1: Information Technology Management. Technical architecture, including hardware/software platforms, operating systems, networking, and the Internet; development strategies; and management issues for the introduction of new technology. Management Information Systems 380 (Topic: Hardware, Software, and Telecommunications) and 380N (Topic 1) may not both be counted.

Topic 2: Managing Information. Understanding, designing, and controlling the information processing activities of an organization. Complements Business Administration 380C by focusing on information systems rather than information technology. Includes business intelligence, knowledge management, data modeling, group decision support systems, and electronic commerce. Additional prerequisite: Business Administration 380C.

Topic 3: Business Process Excellence. Emerging technology, data and process modeling (flow focus for integrated applications), reengineering, and change management. Additional prerequisite: Business Administration 380C.

Topic 4: Digital Economy and Commerce. Additional prerequisite: Management Information Systems 380N (Topic 2) and credit or registration for Management Information Systems 380N (Topic 3).
  

MIS 380N.2  Managing Information  3 Hours  

3 Lecture Hours  0 Lab Hours  
Understanding, designing, and controlling the information processing activities of an organization. Complements Business Administration 380C by focusing on information systems rather than information technology. Includes business intelligence, knowledge management, data modeling, group decision support systems, and electronic commerce.
Pre/Corequisites: Business Administration 380C.  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 380N.3  Business Process Excellence  3 Hours  

3 Lecture Hours  0 Lab Hours  
Emerging technology, data and process modeling (flow focus for integrated applications), reengineering, and change management.
Pre/Corequisites: Business Administration 380C.  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 380N.4  Digital Economy and Commerce  3 Hours  

3 Lecture Hours  0 Lab Hours  
Pre/Corequisites: Management Information Systems 380N (Topic 2) and credit or registration for Management Information Systems 380N (Topic 3).  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 180P, 280P, 380P  Problem Solving and Programming  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Explore programming skills for creating easy-to-maintain systems for business applications. Examine object-oriented and structured methodologies with Python. For each semester hour of credit earned, the equivalent of one lecture hour a week for one semester.
Pre/Corequisites: Graduate standing and consent of instructor.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 181N, 281N, 381N  Topics in Information Systems  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Selected topics in information technology and management of information systems development. For each semester hour of credit earned, one lecture hour a week for one semester.
Pre/Corequisites: Graduate standing.  
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Doctoral  
Topic 1: Introduction to Data Management.

Topic 2: Research in Information Systems: Organizational and Behavioral

Topic 3: Strategic Analysis for High-Tech Industries. Management 185, 285, 385 (Topic 9) and Management Information Systems 181N, 281N, 381N (Topic 3) may not both be counted. Additional prerequisite: Management Information Systems 380N (Topic 2: Managing Information), 380N (Topic 3: Managing Systems), and credit or registration for Management Information Systems 380N (Topic 4: Digital Economy and Commerce).

Topic 4: Decision Support Systems.

Topic 5: Information Systems Design and Implementation. Additional prerequisite: Management Information Systems 380N (Topic 2: Managing Information), 380N (Topic 3: Managing Systems), and credit or registration for Management Information Systems 380N (Topic 4: Digital Economy and Commerce).

Topic 6: Research Seminar.

Topic 7: Information and Knowledge Management. Additional prerequisite: Management Information Systems 380N (Topic 2: Managing Information), 380N (Topic 3: Managing Systems), and credit or registration for Management Information Systems 380N (Topic 4: Digital Economy and Commerce).

Topic 8: Managing Disruptive Innovations. Focuses on the management of disruptive technologies, including analyzing whether an emerging technology is sustaining or disruptive, identifying new markets for disruptive technologies, justifying investments in disruptive technologies, implementing disruptive technologies, and appropriating value from them.

Topic 9: Change Management Practicum I. Project-oriented course focusing on design of organizational change.

Topic 10: Change Management Practicum II. Project-oriented course focusing on implementation of organizational change. Additional prerequisite: Management Information Systems 381N (Topic 9).

Topic 11: Research in Information Technology.

Topic 12: Advanced Information Systems Readings.

Topic 13: Advanced Data Communications. Additional prerequisite: Management Information Systems 381N (Topic 8).

Topic 14: Global Information Technology Management.

Topic 15: Introduction to Electronic Commerce.

Topic 16: Information Systems Projects.

Topic 17: Client/Server Development.

Topic 18: Innovation, Technology, and Commercialization.

Topic 19: Technology Transfer: Theory and Practice.

Topic 20: Cross-Cultural Issues in Information Systems.

Topic 21: Seminar in Multimedia Systems.

Topic 22: Information Technology Strategy and Services. Additional prerequisite: Management Information Systems 380N (Topic 2: Managing Information), 380N (Topic 3: Managing Systems), and credit or registration for Management Information Systems 380N (Topic 4: Digital Economy and Commerce).

Topic 23: E-Business: Strategy and Policy. The responsibilities of the strategist for choosing, developing, and managing an overall e-business firm strategy in uncertain market, technology, and policy environments.

Topic 24: Global E-Business: Theory and Cases. Analysis of case studies, incorporating Oracle and other Web-based distributed computing solutions. Additional prerequisite: Consent of instructor.

Topic 25: E-Security and E-Forensic Frameworks. Discussion and hands-on use of current Web and distributed computing security software and e-forensic solutions. Additional prerequisite: Consent of instructor.

Topic 26: Research Methods in Information Systems. Overview of research methods used to study information systems problems. Fundamental concepts and criteria for use with and evaluation of quantitative and qualitative, positivist and interpretive research methods. Current state-of-the-art applications. Restrictions: Restricted to doctoral students.

Topic 27: Strategies for Networked Economy. Analyzes the competitive dynamics of platform-mediated networks; explores innovations like cloud computing in supporting network-based competition, the implications of information technology-enabled global sourcing, and business intelligence for business value and competitive advantage; and discusses the role of information technology in business transformation and making a case for information technology investments. Management Information Systems 381N (Topic 27) and 381N (Topic 29) may not both be counted.

Topic 28: Data Management. Explore general database concepts such as E-R modeling, relational database design, and advanced SQL. Design and develop mission-critical web-based business applications using databases. Explore data warehouse design and advanced analytics functions within SQL. Management Information Systems 181N, 281N, 381N (Topic 28) and 284N (Topic: Data Management) may not both be counted.

Topic 29: Digital Strategies for Enterprise Transformation. Analyze the competitive dynamics of platform-mediated business models; explore innovations such as cloud computing and Internet of things in supporting platform-based competition; study the implications of information technology-enabled disruption; understand applications of artificial intelligence and machine learning to develop competitive advantage; and discuss the role of information technology in business transformation. Management Information Systems 381N (Topic 27) and 381N (Topic 29) may not both be counted.

Topic 30: Privacy Preserving Analytics. Management Information Systems 381N (Topic 30) and 285N (Topic: Privacy Preserving Analytics) may not both be counted. Restrictions: Restricted to students in the Master of Science program.

Topic 31: Artificial Intelligence and Machine Learning for Managers. Discuss artificial intelligence (AI) and machine learning (ML) through a review of AI and ML concepts, tools, and the complete AI/ML lifecycle in the context of tackling business and organizational challenges.

Topic 32: Artificial Intelligence Strategies for Digital Transformation.

Topic 33: Artificial Intelligence in Business and Society.

Topic 34: Artificial Intelligence in Healthcare.

Topic 35: Modern Data Management. Explore modern data management practices, with a strong focus on design and implementation of scalable, cloud-native data systems. Learn to manage diverse data types--structured, semi-structured, and unstructured--across their full lifecycle. Examine the role of cloud infrastructure in enabling agile, secure, and scalable data solutions. Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.
  

MIS 181N.1, 281N.1, 381N.1  Introduction to Data Management  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.10, 281N.10, 381N.10  Change Management Practicum II  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Project-oriented course focusing on implementation of organizational change.
Pre/Corequisites: Management Information Systems 381N (Topic 9).  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.11, 281N.11, 381N.11  Research in Information Technology  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.12, 281N.12, 381N.12  Advanced Information Systems Readings  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.13, 281N.13, 381N.13  Advanced Data Communications  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Pre/Corequisites: Management Information Systems 381N (Topic 8).  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.14, 281N.14, 381N.14  Global Information Technology Management  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.15, 281N.15, 381N.15  Introduction to Electronic Commerce  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.16, 281N.16, 381N.16  Information Systems Projects  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.17, 281N.17, 381N.17  Client/Server Development  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.18, 281N.18, 381N.18  Innovation, Technology, and Commercialization  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.19, 281N.19, 381N.19  Technology Transfer: Theory and Practice  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.2, 281N.2, 381N.2  Research in Information Systems: Organizational and Behavioral Perspectives  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.20, 281N.20, 381N.20  Cross-Cultural Issues in Information Systems  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.21, 281N.21, 381N.21  Seminar in Multimedia Systems  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.22, 281N.22, 381N.22  Information Technology Strategy and Services  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Pre/Corequisites: Management Information Systems 380N (Topic 2: Managing Information), 380N (Topic 3: Managing Systems), and credit or registration for Management Information Systems 380N (Topic 4: Digital Economy and Commerce).  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.23, 281N.23, 381N.23  E-Business: Strategy and Policy  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
The responsibilities of the strategist for choosing, developing, and managing an overall e-business firm strategy in uncertain market, technology, and policy environments.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.24, 281N.24, 381N.24  Global E-Business: Theory and Cases  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Analysis of case studies, incorporating Oracle and other Web-based distributed computing solutions.
Pre/Corequisites: Consent of instructor.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.25, 281N.25, 381N.25  E-Security and E-Forensic Frameworks  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Discussion and hands-on use of current Web and distributed computing security software and e-forensic solutions.
Pre/Corequisites: Consent of instructor.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.26, 281N.26, 381N.26  Research Methods in Information Systems  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Overview of research methods used to study information systems problems. Fundamental concepts and criteria for use with and evaluation of quantitative and qualitative, positivist and interpretive research methods. Current state-of-the-art applications.
Restrictions: Restricted to doctoral students.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.27, 281N.27, 381N.27  Strategies for Networked Economy  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Analyzes the competitive dynamics of platform-mediated networks; explores innovations like cloud computing in supporting network-based competition, the implications of information technology-enabled global sourcing, and business intelligence for business value and competitive advantage; and discusses the role of information technology in business transformation and making a case for information technology investments. Management Information Systems 381N (Topic 27) and 381N (Topic 29) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.28, 281N.28, 381N.28  Data Management  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Explore general database concepts such as E-R modeling, relational database design, and advanced SQL. Design and develop mission-critical web-based business applications using databases. Explore data warehouse design and advanced analytics functions within SQL. Management Information Systems 181N, 281N, 381N (Topic 28) and 284N (Topic: Data Management) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.29, 281N.29, 381N.29  Digital Strategies for Enterprise Transformation  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Analyze the competitive dynamics of platform-mediated business models; explore innovations such as cloud computing and Internet of things in supporting platform-based competition; study the implications of information technology-enabled disruption; understand applications of artificial intelligence and machine learning to develop competitive advantage; and discuss the role of information technology in business transformation. Management Information Systems 381N (Topic 27) and 381N (Topic 29) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.3, 281N.3, 381N.3  Strategic Analysis for High-Tech Industries  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Management 185, 285, 385 (Topic 9) and Management Information Systems 181N, 281N, 381N (Topic 3) may not both be counted.
Pre/Corequisites: Management Information Systems 380N (Topic 2: Managing Information), 380N (Topic 3: Managing Systems), and credit or registration for Management Information Systems 380N (Topic 4: Digital Economy and Commerce).  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.30, 281N.30, 381N.30  Privacy Preserving Analytics  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Management Information Systems 381N (Topic 30) and 285N (Topic: Privacy Preserving Analytics) may not both be counted.
Restrictions: Restricted to students in the Master of Science program.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.31, 281N.31, 381N.31  Artificial Intelligence and Machine Learning for Managers  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Discuss artificial intelligence (AI) and machine learning (ML) through a review of AI and ML concepts, tools, and the complete AI/ML lifecycle in the context of tackling business and organizational challenges.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.32, 281N.32, 381N.32  Artificial Intelligence Strategies for Digital Transformation  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.33, 281N.33, 381N.33  Artificial Intelligence in Business and Society  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.34, 281N.34, 381N.34  Artificial Intelligence in Healthcare  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.35, 281N.35, 381N.35  Modern Data Management  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Explore modern data management practices, with a strong focus on design and implementation of scalable, cloud-native data systems. Learn to manage diverse data types--structured, semi-structured, and unstructured--across their full lifecycle. Examine the role of cloud infrastructure in enabling agile, secure, and scalable data solutions.
Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.4, 281N.4, 381N.4  Decision Support Systems  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.5, 281N.5, 381N.5  Information Systems Design and Implementation  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Pre/Corequisites: Management Information Systems 380N (Topic 2: Managing Information), 380N (Topic 3: Managing Systems), and credit or registration for Management Information Systems 380N (Topic 4: Digital Economy and Commerce).  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.6, 281N.6, 381N.6  Research Seminar  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.7, 281N.7, 381N.7  Information and Knowledge Management  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Pre/Corequisites: Management Information Systems 380N (Topic 2: Managing Information), 380N (Topic 3: Managing Systems), and credit or registration for Management Information Systems 380N (Topic 4: Digital Economy and Commerce).  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.8, 281N.8, 381N.8  Managing Disruptive Innovations  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Focuses on the management of disruptive technologies, including analyzing whether an emerging technology is sustaining or disruptive, identifying new markets for disruptive technologies, justifying investments in disruptive technologies, implementing disruptive technologies, and appropriating value from them.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 181N.9, 281N.9, 381N.9  Change Management Practicum I  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Project-oriented course focusing on design of organizational change.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N  Topics in Information Management  3 Hours  

3 Lecture Hours  0 Lab Hours  
Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.  
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Doctoral  
Topic 1: Managing Financial Information. Data modeling and information management for investment analysis and financial systems.

Topic 2: E-Business Change.

Topic 3: E-Business Application Development.

Topic 4: Cross-Functional Systems Integration. Additional prerequisite: Management Information Systems 380N (Topic 2), 380N (Topic 3), and credit or registration for Management Information Systems 380N (Topic 4).

Topic 5: Managing Complexity.

Topic 6: Computer Auditing and Systems Security.

Topic 7: Project Management in Fast-Cycle Environments.

Topic 8: Balanced Scoreboard: An Information Systems Perspective. Theory and tools that support the design and implementation of balanced scoreboard evaluation systems.

Topic 9: Business Data Analytics with Data Mining.

Topic 10: Data Mining for Marketing.

Topic 11: Business Intelligence Capstone. Explores foundations of business analytics related to database management, data analysis techniques, and business decision making to solve a business problem of a client. Additional prerequisite: Consent of instructor.

Topic 12: Social Media Analytics. An introduction to social network analysis for business value using statistical optimization and decision theory, including the foundation for analyzing online search and conversation data for market sensing, sentiments, product quality, reputation, recommendations, and brand awareness. Additional prerequisite: Consent of instructor.

Topic 13: Predictive Analytics and Data Mining. Management Information Systems 382N (Topic 9: Business Data Analytics with Data Mining) and and 382N (Topic 13) may not both be counted.

Topic 14: Business Data Science. An introduction to basic concepts, methodology, algorithms, and technology used in business analytics and decision making. Explore concepts from probabilistic modeling, analysis and experimental design. Examine the basics of modern regression and classification, clustering, visualization, dimensionality reduction, A/B Testing and an introduction to deep learning. Management Information Systems 382N (Topic: Business Data Science) and 382N (Topic 14) may not both be counted.

Topic 15: Business Analytics Capstone. Restrictions: Restricted to students in the Master of Science in Business Analytics program.

Topic 16: Fundamentals of Neural Networks and Machine Learning. Explore an introduction to fundamental concepts, methodology, algorithms, and technology used in business analytics and decision making covering the most powerful concepts and algorithms from probabilistic modeling, analysis and machine learning. Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.

Topic 18: Digital Health Innovation. Focus on digital innovation in the healthcare industry that is driven by advances in information technology and artificial intelligence. Learn about different types of IT-enabled innovations that are revolutionizing healthcare delivery, develop technology-enabled strategies to enhance innovation, and evaluate how new types of AI-enabled innovations create value for patients and other stakeholders. Management Information Systems 382N (Topic: Digital Health Innovation) and 382N (Topic 18) may not both be counted.
  

MIS 382N.1  Managing Financial Information  3 Hours  

3 Lecture Hours  0 Lab Hours  
Data modeling and information management for investment analysis and financial systems.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N.10  Data Mining for Marketing  3 Hours  

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N.11  Business Intelligence Capstone  3 Hours  

3 Lecture Hours  0 Lab Hours  
Explores foundations of business analytics related to database management, data analysis techniques, and business decision making to solve a business problem of a client.
Pre/Corequisites: Consent of instructor.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N.12  Social Media Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
An introduction to social network analysis for business value using statistical optimization and decision theory, including the foundation for analyzing online search and conversation data for market sensing, sentiments, product quality, reputation, recommendations, and brand awareness.
Pre/Corequisites: Consent of instructor.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N.13  Predictive Analytics and Data Mining  3 Hours  

3 Lecture Hours  0 Lab Hours  
Management Information Systems 382N (Topic 9: Business Data Analytics with Data Mining) and and 382N (Topic 13) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N.14  Business Data Science  3 Hours  

3 Lecture Hours  0 Lab Hours  
An introduction to basic concepts, methodology, algorithms, and technology used in business analytics and decision making. Explore concepts from probabilistic modeling, analysis and experimental design. Examine the basics of modern regression and classification, clustering, visualization, dimensionality reduction, A/B Testing and an introduction to deep learning. Management Information Systems 382N (Topic: Business Data Science) and 382N (Topic 14) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N.15  Business Analytics Capstone  3 Hours  

3 Lecture Hours  0 Lab Hours  
Restrictions: Restricted to students in the Master of Science in Business Analytics program.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N.16  Fundamentals of Neural Networks and Machine Learning  3 Hours  

3 Lecture Hours  0 Lab Hours  
Explore an introduction to fundamental concepts, methodology, algorithms, and technology used in business analytics and decision making covering the most powerful concepts and algorithms from probabilistic modeling, analysis and machine learning.
Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N.18  Digital Health Innovation  3 Hours  

3 Lecture Hours  0 Lab Hours  
Focus on digital innovation in the healthcare industry that is driven by advances in information technology and artificial intelligence. Learn about different types of IT-enabled innovations that are revolutionizing healthcare delivery, develop technology-enabled strategies to enhance innovation, and evaluate how new types of AI-enabled innovations create value for patients and other stakeholders. Management Information Systems 382N (Topic: Digital Health Innovation) and 382N (Topic 18) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N.2  E-Business Change  3 Hours  

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N.3  E-Business Application Development  3 Hours  

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N.4  Cross-Functional Systems Integration  3 Hours  

3 Lecture Hours  0 Lab Hours  
Pre/Corequisites: Management Information Systems 380N (Topic 2), 380N (Topic 3), and credit or registration for Management Information Systems 380N (Topic 4).  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N.5  Managing Complexity  3 Hours  

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N.6  Computer Auditing and Systems Security  3 Hours  

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N.7  Project Management in Fast-Cycle Environments  3 Hours  

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 382N.8  Balanced Scoreboard: An Information Systems Perspective  3 Hours  

3 Lecture Hours  0 Lab Hours  
Theory and tools that support the design and implementation of balanced scoreboard evaluation systems.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 283M, 383M  Supply Chain Management for Executives  2-3 Hours  

2,3 Lecture Hours  0 Lab Hours  
Examine frameworks for understanding how to transform supply chain strategy in the face of pressures from technological, social, and political disruption. For each semester hour of credit earned, one lecture hour a week for one semester.
Pre/Corequisites: Graduate standing.  
Restrictions: Restricted to students in the Executive MBA Program.  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Master's  

MIS 383N  Topics in Information-Intensive Business Processes  3 Hours  

3 Lecture Hours  0 Lab Hours  
Topics in management of information in specific industries or application areas. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.  
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Doctoral  
Topic 1: Health Care Management.

Topic 2: Health Services Seminar.

Topic 3: Customer Insights.

Topic 4: Supply Chain Management.

Topic 5: Computer Tools for Investment Science.

Topic 6: Trading-Floor Technology.

Topic 7: Computational Finance.

Topic 8: Financial Modeling and Optimization.

Topic 9: Statistics for Finance.

Topic 10: Practicum in Multimedia Systems Development. Additional prerequisite: Business Administration 380C and consent of instructor. Restrictions: Restricted to MBA and MPA students who have chosen the information management concentration.

Topic 11: Financial Engineering.

Topic 12: E-Business Innovation.

Topic 13: Managing Innovation in a Global Company. Examines innovation-based business strategies that rely on internal and external sources, processes in different organization forms, and market structures.
  

MIS 383N.1  Health Care Management  3 Hours  

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 383N.10  Practicum in Multimedia Systems Development  3 Hours  

3 Lecture Hours  0 Lab Hours  
Pre/Corequisites: Business Administration 380C and consent of instructor.  
Restrictions: Restricted to MBA and MPA students who have chosen the information management concentration.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 383N.12  E-Business Innovation  3 Hours  

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 383N.13  Managing Innovation in a Global Company  3 Hours  

3 Lecture Hours  0 Lab Hours  
Examines innovation-based business strategies that rely on internal and external sources, processes in different organization forms, and market structures.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 383N.2  Health Services Seminar  3 Hours  

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 383N.3  Customer Insights  3 Hours  

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 383N.4  Supply Chain Management  3 Hours  

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 383N.5  Computer Tools for Investment Science  3 Hours  

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 383N.6  Trading-Floor Technology  3 Hours  

3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 184N, 284N, 384N  Topics in Business Analytics  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Selected topics in business analytics. One, two, or three lecture hours a week for one semester.
Pre/Corequisites: Graduate Standing; additional prerequisites vary with the topic.  
Restrictions: Restricted to students admitted to the Master of Science in Information, Risk, and Operations Management.  
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Master's  

MIS 385  Management Information Systems  3 Hours  

3 Lecture Hours  0 Lab Hours  
Overview of hardware and software life cycles; in-depth considerations of program design, including experience programming for large-scale computer systems in COBOL, FORTRAN, and/or BASIC. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.  
Restrictions: Restricted to students in the MS in Information Technology and Management Program.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 185G, 285G, 385G  Machine Learning and Artificial Intelligence for Executives  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Introduction to artificial intelligence (AI) and machine learning (ML) through a review of AI and ML concepts, tools, and the complete AI/ML lifecycle in the context of tackling business and organizational challenges. For each semester hour of credit earned, one lecture hour a week for one semester.
Pre/Corequisites: Graduate standing.  
Restrictions: Restricted to students in the Executive MBA Program.  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N, 285N, 385N  Topics in Information Technology and Management  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Selected topics in information technology and management. For each semester hour of credit earned, the equivalent of one lecture hour a week for one semester.
Pre/Corequisites: Graduate standing; additional prerequisites vary with the topic.  
Restrictions: Restricted to students admitted to the Master of Science in Information Technology and Management program.  
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Master's  
Topic 1: Big Data and Distributed Programming. Explore a range of subjects required for developing modern applications that operate over vast data sets that are potentially distributed in nature. Consider alternative technologies and architectures for working with big data, examining the pros and cons of the different approaches. Management Information Systems 284N (Topic: Big Data/Distr Programming) and 185N, 285N, 385N (Topic 1) may not both be counted.

Topic 2: Emerging Technologies I. Explore all aspects of the Internet of Things (IoT) product life-cycle. Interface with the devices (sensors/actuators) that collect data and affect the environment. Explore network protocols for communication with these constrained devices. Examine programming of the back-end services that host, manipulate and disseminate the collected data. Study the development of apps that facilitate human interaction with these devices and the analysis of the data they produce. Examine security, privacy and performance considerations specific to IoT. Management Information Systems 284N (Topic: Emerging Technologies I) and 185N, 285N, 385N (Topic 2) may not both be counted.

Topic 3: IT Security, Policy, and Compliance. Explore the prevention and mitigation of data security and privacy risks in newly designed digital artifacts through IT governance, risk, and control frameworks as well as relevant laws, regulations, and industry standards. Management Information Systems 382N (Topic: IT/Secur/Policy/Compliance) and 185N, 285N, 385N (Topic 3) may not both be counted.

Topic 4: IT Capstone. Develop real-life business and social solutions using emerging information technologies. Engage with industry partners to explore business context for IT Capstone projects. Management Information Systems 382N (Topic: IT Capstone) and 185N, 285N, 385N (Topic 4) may not both be counted.

Topic 5: Cognitive Computing. An overview of convolutional neural networks, recurrent neural networks and generative adversarial networks. Explore deep learning and artificial neural networks. Applications include computer vision, image and time series modeling as well as computational aspects of deep learning over big datasets. Utilize Python and Tensorflow, among other tools. Management Information Systems 284N (Topic: Cognitive Computing) and 185N, 285N, 385N (Topic 5) may not both be counted.

Topic 6: Strategic IT and Change Management. Explore the strategic management of new IT-embedded product and service innovations and their incorporation into the digital business ecosystems of organizations. Management Information Systems 284N (Topic: Strat IT and Change Mgmt) and 185N, 285N, 385N (Topic 6) may not both be counted.

Topic 7: IT and Supply Chain Management. Examine the role of Information Technology in managing Supply Chains. Explore the IT capabilities needed by firms to coordinate their operations, collaborate with business partners and manage uncertainty. Illustrate the role of technologies and tools like ERP platform, ABAP programming, XML, web services, distributed computing and machine learning to improve the performance of supply chains. Management Information Systems 284N (Topic: IT and Supply Chain Mgmt) and 185N, 285N, 385N (Topic 7) may not both be counted.

Topic 8: Design Methods. Utilize design tools and methods to understand user needs, frame business opportunities, and design solutions. Examine design from both organizational and technical perspectives. Conduct research with end users, synthesize data, prototype solution ideas, and communicate compelling stories. Undertake design challenges that focus on emerging information technologies, including the internet of things (IoT), cognitive computing, AI, cloud, mobile, and 3D/4D printing. Management Information Systems 382N (Topic: Design Methods) and 185N, 285N, 385N (Topic 8) may not both be counted.

Topic 9: Advanced Programming and App Development. Explore various approaches to modern app development, including required advanced programming and software engineering concepts. Explore approaches to app development ranging from native platform programming through programming frameworks that allow cross-platform development, to high-level approaches based on web frameworks. Management Information Systems 382N (Topic: Adv Programming/App Devel) and 185N, 285N, 385N (Topic 9) may not both be counted.

Topic 10: User Generated Content Analytics. Generate business and social insights from user-generated content (e.g., text, images, video, etc.) through the use of text analytics, sentiment analysis, visualization techniques, etc. Management Information Systems 381N (Topic: User Genrtd Content Anlytcs) and 185N, 285N, 385N (Topic 10) may not both be counted.

Topic 11: Advanced Data Mining and Web Analytics. Examine a variety of data mining and machine learning techniques for descriptive, predictive and prescriptive analytics. Explore approaches to analyzing different types of information from the Web (web structure, content, usage). Management Information Systems 382N (Topic: Advanced Mining/Web Analytics) and 185N, 285N, 385N (Topic 11) may not both be counted.

Topic 12: Healthcare IT and Analytics. Design new healthcare solutions using emerging information technologies such as Internet of Things, cognitive computing, artificial intelligence, the cloud, mobile, and 3D and 4D printing.

Topic 13: Emerging Technologies II. Build a strong business and technical foundation for blockchain. Discuss business inefficiencies that can be addressed by blockchain technology. Examine cryptology and distributed computing that blockchain systems rely on, including an overview of Bitcoin. Explore current research problems, perform programming assignments, lead discussions, and hear presentations from industry and academic researchers. Management Information Systems 382N (Topic: Emerging Technologies II) and 185N, 285N, 385N (Topic 13) may not both be counted.

Topic 14: Programming Blockchain. Cover the development of the bitcoin protocol from scratch. Explore basic libraries to build the protocol using a step-by-step approach. Examine the primary and secondary aspects of scaling, mining and operating a bitcoin protocol based system. Management Information Systems 284N (Topic: Programming Blockchain) and 185N, 285N, 385N (Topic 14) may not both be counted.

Topic 15: Unstructured Data Analytics. Generate business and social insights from user generated content (e.g., text, images, video, etc.) through the use of text analytics, sentiment analysis, visualization techniques, etc. Management Information Systems 385N (Topic: Unstructured Data Analytics) and 385N (Topic 15) may not both be counted.

Topic 16: Math Foundations of Artificial Intelligence. Explore the mathematical foundations of AI including probability theory and linear algebra, concepts necessary as foundational knowledge for other AI related courses in program. Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.

Topic 17: Advanced Programming and Application Development II. Learn fundamentals of modern cloud native applications, including how to design, develop, and operate them, as well as distributed programming fundamentals. Examine machine learning design patterns for big data and distributed programming concepts. Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.

Topic 18: Problem Solving Unstructured Data Generative Artificial Intelligence. Explore how modern enterprises can harness the combined power of GenAI and traditional unstructured data analytics to unlock business value. Learn a broad range of business questions and analytics driven use cases, spanning decision-making, process optimization, and innovation. Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.

Topic 19: Strategic Information Technology and Digital Transformation. Examine information technology-enabled digital transformation and explore how digitalization impacts firm competition and enables industry transformation. Learn the role of digital business models, how firms can compete in disruptive ecosystems, to think critically about the benefit and challenge of strategic AI implementations and change management in real-world applications. Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.

Topic 20: Risk Governance Artificial Intelligence and Intelligent Automation. Explore how to govern and control the complex cybersecurity, privacy, and IT risk organizations face in an era increasingly shaped by artificial intelligence (AI) and intelligent automation technologies. Discuss privacy-enhancing technology and ethical decision-making framework that help address the novel challenge posed by intelligent systems. Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.

Topic 21: Artificial Intelligence in the Enterprise. Explore how to design and deploy artificial-intelligence (AI) capability across the enterprise, including mapping AI solutions to specific business requirements and align them with strategic goals. Learn how to identify, assess, and prepare high-quality data essential for AI success and how to select and scale a suitable model and integrate with existing systems and processes. Examine governance and continuous-improvement practices that keep AI initiative reliable and effective over the long term. Management Information Systems 284N (Topic: AI in The Enterprise) and 285N (Topic 21) may not both be counted. Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.

Topic 22: Generative Artificial Intelligence I. Explore a basic working knowledge of the latest algorithms, business applications and limitations of what data science and AI can accomplish. Learn about attention mechanism and the transformer, image and language embeddings, large language models, multi-modal models, and the important latest idea in reasoning, as well as some of the key emerging issues in security and reliability. Additional prerequisite: Basic knowledge of machine learning and data science; basic knowledge of Python and Pytorch. Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.

Topic 23: Generative Artificial Intelligence II. Learn about state-of-art Generative AI (GenAI) models for image, video and language generation and how they are used in a business setting. Explore their architecture and mathematical foundation. Learn about machine learning models for decision-making. Additional prerequisite: Basic knowledge of machine learning and data science; basic knowledge of Python and Pytorch. Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.

Topic 24: Managing Data Production For Artificial Intelligence. Explore the emerging discipline of managing data products designed to power GenAI applications. Learn how to curate, package, and deliver reusable datasets and APIs optimized for retrieval-augmented generation, synthetic data generation, and LLM prompt engineering. Examine fitness for use, economic value, and continuous improvement of data assets. Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.

Topic 25: Data Governance and Responsible Artificial Intelligence. Apply data governance framework to ensure the integrity, transparency, and fairness of GenAI system. Learn how to embed observability into AI workflows--tracking provenance, bias, quality metrics, and access rights across the AI lifecycle. Connect the socio-technical principles of data governance with the operational reality of GenAI to implement trust-by-design practices within enterprise AI programs. Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.

Topic 26: Human Dimensions Of Cybersecurity. Examine emerging security and privacy vulnerability in AI systems, with a focus on human manipulation vectors: prompt injection, phishing via AI-generated content, identity spoofing, and malicious feedback loops. Learn how adversaries exploit both model behavior and user trust, and how to counteract the risk through red teaming, user training, behavior-aware safeguards, and ethical AI design. Evaluate human-AI interaction security and apply layered defense that reinforce public trust in AI systems. Management Information Systems 185 (Topic: Human Dimensions Cybersecurity) and 185N (Topic 26) may not both be counted. Restrictions: Restricted to students in the Master of Science in Information Technology and Management program

Topic 27: Technical Foundation Artificial Intelligence Driven Cybersecurity. Investigate the technical foundation of cybersecurity, with a particular focus on an emerging threat and challenge at the intersection of cybersecurity and artificial intelligence (AI). Explore how AI functions both as an enabler of a cyberattack and as a vulnerable target, requiring robust security defenses. Restrictions: Restricted to students in the Master of Science in Information Technology and Management program

Topic 28: Advanced Program and Application Development I. Learn advanced programming and foundational software engineering concepts essential for developing robust, scalable web applications through practical, hands-on exploration using a traditional approach. Emphasis is on mastering the most popular full-stack framework, while reinforcing critical topics such as source code management, agile methodology, the software development life cycle (SDLC), and common design patterns. Restrictions: Restricted to students in the Master of Science in Information Technology and Management program
  

MIS 185N.1, 285N.1, 385N.1  Big Data and Distributed Programming  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Explore a range of subjects required for developing modern applications that operate over vast data sets that are potentially distributed in nature. Consider alternative technologies and architectures for working with big data, examining the pros and cons of the different approaches. Management Information Systems 284N (Topic: Big Data/Distr Programming) and 185N, 285N, 385N (Topic 1) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.10, 285N.10, 385N.10  User Generated Content Analytics  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Generate business and social insights from user-generated content (e.g., text, images, video, etc.) through the use of text analytics, sentiment analysis, visualization techniques, etc. Management Information Systems 381N (Topic: User Genrtd Content Anlytcs) and 185N, 285N, 385N (Topic 10) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.11, 285N.11, 385N.11  Advanced Data Mining and Web Analytics  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Examine a variety of data mining and machine learning techniques for descriptive, predictive and prescriptive analytics. Explore approaches to analyzing different types of information from the Web (web structure, content, usage). Management Information Systems 382N (Topic: Advanced Mining/Web Analytics) and 185N, 285N, 385N (Topic 11) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.12, 285N.12, 385N.12  Healthcare IT and Analytics  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Design new healthcare solutions using emerging information technologies such as Internet of Things, cognitive computing, artificial intelligence, the cloud, mobile, and 3D and 4D printing.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.13, 285N.13, 385N.13  Emerging Technologies II  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Build a strong business and technical foundation for blockchain. Discuss business inefficiencies that can be addressed by blockchain technology. Examine cryptology and distributed computing that blockchain systems rely on, including an overview of Bitcoin. Explore current research problems, perform programming assignments, lead discussions, and hear presentations from industry and academic researchers. Management Information Systems 382N (Topic: Emerging Technologies II) and 185N, 285N, 385N (Topic 13) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.14, 285N.14, 385N.14  Programming Blockchain  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Cover the development of the bitcoin protocol from scratch. Explore basic libraries to build the protocol using a step-by-step approach. Examine the primary and secondary aspects of scaling, mining and operating a bitcoin protocol based system. Management Information Systems 284N (Topic: Programming Blockchain) and 185N, 285N, 385N (Topic 14) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.15, 285N.15, 385N.15  Unstructured Data Analytics  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Generate business and social insights from user generated content (e.g., text, images, video, etc.) through the use of text analytics, sentiment analysis, visualization techniques, etc. Management Information Systems 385N (Topic: Unstructured Data Analytics) and 385N (Topic 15) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.16, 285N.16, 385N.16  Math Foundations of Artificial Intelligence  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Explore the mathematical foundations of AI including probability theory and linear algebra, concepts necessary as foundational knowledge for other AI related courses in program.
Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.17, 285N.17, 385N.17  Advanced Programming and Application Development II  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Learn fundamentals of modern cloud native applications, including how to design, develop, and operate them, as well as distributed programming fundamentals. Examine machine learning design patterns for big data and distributed programming concepts.
Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.18, 285N.18, 385N.18  Problem Solving Unstructured Data Generative Artificial Intelligence  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Explore how modern enterprises can harness the combined power of GenAI and traditional unstructured data analytics to unlock business value. Learn a broad range of business questions and analytics driven use cases, spanning decision-making, process optimization, and innovation.
Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.19, 285N.19, 385N.19  Strategic Information Technology and Digital Transformation  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Examine information technology-enabled digital transformation and explore how digitalization impacts firm competition and enables industry transformation. Learn the role of digital business models, how firms can compete in disruptive ecosystems, to think critically about the benefit and challenge of strategic AI implementations and change management in real-world applications.
Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.2, 285N.2, 385N.2  Emerging Technologies I  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Explore all aspects of the Internet of Things (IoT) product life-cycle. Interface with the devices (sensors/actuators) that collect data and affect the environment. Explore network protocols for communication with these constrained devices. Examine programming of the back-end services that host, manipulate and disseminate the collected data. Study the development of apps that facilitate human interaction with these devices and the analysis of the data they produce. Examine security, privacy and performance considerations specific to IoT. Management Information Systems 284N (Topic: Emerging Technologies I) and 185N, 285N, 385N (Topic 2) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.20, 285N.20, 385N.20  Risk Governance Artificial Intelligence and Intelligent Automation  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Explore how to govern and control the complex cybersecurity, privacy, and IT risk organizations face in an era increasingly shaped by artificial intelligence (AI) and intelligent automation technologies. Discuss privacy-enhancing technology and ethical decision-making framework that help address the novel challenge posed by intelligent systems.
Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.21, 285N.21, 385N.21  Artificial Intelligence in the Enterprise  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Explore how to design and deploy artificial-intelligence (AI) capability across the enterprise, including mapping AI solutions to specific business requirements and align them with strategic goals. Learn how to identify, assess, and prepare high-quality data essential for AI success and how to select and scale a suitable model and integrate with existing systems and processes. Examine governance and continuous-improvement practices that keep AI initiative reliable and effective over the long term. Management Information Systems 284N (Topic: AI in The Enterprise) and 285N (Topic 21) may not both be counted.
Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.22, 285N.22, 385N.22  Generative Artificial Intelligence I  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Explore a basic working knowledge of the latest algorithms, business applications and limitations of what data science and AI can accomplish. Learn about attention mechanism and the transformer, image and language embeddings, large language models, multi-modal models, and the important latest idea in reasoning, as well as some of the key emerging issues in security and reliability.
Pre/Corequisites: Basic knowledge of machine learning and data science; basic knowledge of Python and Pytorch.  
Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.23, 285N.23, 385N.23  Generative Artificial Intelligence II  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Learn about state-of-art Generative AI (GenAI) models for image, video and language generation and how they are used in a business setting. Explore their architecture and mathematical foundation. Learn about machine learning models for decision-making.
Pre/Corequisites: Basic knowledge of machine learning and data science; basic knowledge of Python and Pytorch.  
Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.24, 285N.24, 385N.24  Managing Data Production For Artificial Intelligence  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Explore the emerging discipline of managing data products designed to power GenAI applications. Learn how to curate, package, and deliver reusable datasets and APIs optimized for retrieval-augmented generation, synthetic data generation, and LLM prompt engineering. Examine fitness for use, economic value, and continuous improvement of data assets.
Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.25, 285N.25, 385N.25  Data Governance and Responsible Artificial Intelligence  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Apply data governance framework to ensure the integrity, transparency, and fairness of GenAI system. Learn how to embed observability into AI workflows--tracking provenance, bias, quality metrics, and access rights across the AI lifecycle. Connect the socio-technical principles of data governance with the operational reality of GenAI to implement trust-by-design practices within enterprise AI programs.
Restrictions: Restricted to students in the Master of Science in Information Technology and Management program.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.26, 285N.26, 385N.26  Human Dimensions Of Cybersecurity  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Examine emerging security and privacy vulnerability in AI systems, with a focus on human manipulation vectors: prompt injection, phishing via AI-generated content, identity spoofing, and malicious feedback loops. Learn how adversaries exploit both model behavior and user trust, and how to counteract the risk through red teaming, user training, behavior-aware safeguards, and ethical AI design. Evaluate human-AI interaction security and apply layered defense that reinforce public trust in AI systems. Management Information Systems 185 (Topic: Human Dimensions Cybersecurity) and 185N (Topic 26) may not both be counted.
Restrictions: Restricted to students in the Master of Science in Information Technology and Management program  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.27, 285N.27, 385N.27  Technical Foundation Artificial Intelligence Driven Cybersecurity  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Investigate the technical foundation of cybersecurity, with a particular focus on an emerging threat and challenge at the intersection of cybersecurity and artificial intelligence (AI). Explore how AI functions both as an enabler of a cyberattack and as a vulnerable target, requiring robust security defenses.
Restrictions: Restricted to students in the Master of Science in Information Technology and Management program  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.28, 285N.28, 385N.28  Advanced Program and Application Development I  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Learn advanced programming and foundational software engineering concepts essential for developing robust, scalable web applications through practical, hands-on exploration using a traditional approach. Emphasis is on mastering the most popular full-stack framework, while reinforcing critical topics such as source code management, agile methodology, the software development life cycle (SDLC), and common design patterns.
Restrictions: Restricted to students in the Master of Science in Information Technology and Management program  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.3, 285N.3, 385N.3  IT Security, Policy, and Compliance  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Explore the prevention and mitigation of data security and privacy risks in newly designed digital artifacts through IT governance, risk, and control frameworks as well as relevant laws, regulations, and industry standards. Management Information Systems 382N (Topic: IT/Secur/Policy/Compliance) and 185N, 285N, 385N (Topic 3) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.4, 285N.4, 385N.4  IT Capstone  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Develop real-life business and social solutions using emerging information technologies. Engage with industry partners to explore business context for IT Capstone projects. Management Information Systems 382N (Topic: IT Capstone) and 185N, 285N, 385N (Topic 4) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.5, 285N.5, 385N.5  Cognitive Computing  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
An overview of convolutional neural networks, recurrent neural networks and generative adversarial networks. Explore deep learning and artificial neural networks. Applications include computer vision, image and time series modeling as well as computational aspects of deep learning over big datasets. Utilize Python and Tensorflow, among other tools. Management Information Systems 284N (Topic: Cognitive Computing) and 185N, 285N, 385N (Topic 5) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.6, 285N.6, 385N.6  Strategic IT and Change Management  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Explore the strategic management of new IT-embedded product and service innovations and their incorporation into the digital business ecosystems of organizations. Management Information Systems 284N (Topic: Strat IT and Change Mgmt) and 185N, 285N, 385N (Topic 6) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.7, 285N.7, 385N.7  IT and Supply Chain Management  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Examine the role of Information Technology in managing Supply Chains. Explore the IT capabilities needed by firms to coordinate their operations, collaborate with business partners and manage uncertainty. Illustrate the role of technologies and tools like ERP platform, ABAP programming, XML, web services, distributed computing and machine learning to improve the performance of supply chains. Management Information Systems 284N (Topic: IT and Supply Chain Mgmt) and 185N, 285N, 385N (Topic 7) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.8, 285N.8, 385N.8  Design Methods  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Utilize design tools and methods to understand user needs, frame business opportunities, and design solutions. Examine design from both organizational and technical perspectives. Conduct research with end users, synthesize data, prototype solution ideas, and communicate compelling stories. Undertake design challenges that focus on emerging information technologies, including the internet of things (IoT), cognitive computing, AI, cloud, mobile, and 3D/4D printing. Management Information Systems 382N (Topic: Design Methods) and 185N, 285N, 385N (Topic 8) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 185N.9, 285N.9, 385N.9  Advanced Programming and App Development  1-3 Hours  

1,2,3 Lecture Hours  0 Lab Hours  
Explore various approaches to modern app development, including required advanced programming and software engineering concepts. Explore approaches to app development ranging from native platform programming through programming frameworks that allow cross-platform development, to high-level approaches based on web frameworks. Management Information Systems 382N (Topic: Adv Programming/App Devel) and 185N, 285N, 385N (Topic 9) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Master's  

MIS 191S, 291S, 391S, 691S  Independent Study  1,2,3,6 Hours  

1,2,3,6 Lecture Hours  0 Lab Hours  
Conference course.
Pre/Corequisites: Graduate standing and consent of instructor.  
Grading: Student Option  
Repeatable for credit: Yes (Unlimited)  
Academic Level: Doctoral  

MIS 698  Thesis  6 Hours  

0 Lecture Hours  0 Lab Hours  
The equivalent of three lecture hours a week for two semesters.
Pre/Corequisites: For 698A, graduate standing in information, risk, and operations management and consent of the graduate adviser; for 698B, Management Information Systems 698A.  
Grading: Credit No Credit  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 398R  Master's Report  3 Hours  

3 Lecture Hours  0 Lab Hours  
Preparation of a report to fulfill the requirement for the master's degree under the report option. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing in information, risk, and operations management and consent of the supervising faculty member and the graduate adviser.  
Grading: Credit No Credit  
Repeatable for credit: No  
Academic Level: Doctoral  

MIS 399W, 699W, 999W  Dissertation  3,6,9 Hours  

0 Lecture Hours  0 Lab Hours  
Pre/Corequisites: Admission to candidacy for the doctoral degree.  
Grading: Credit No Credit  
Repeatable for credit: Yes (Unlimited)  
Academic Level: Doctoral