Master of Science in Computer Science (MSCS)

• Advanced Specialization and Expertise
  • Develop deep expertise in specific areas like AI and cloud computing.
  • Gain access to research opportunities and emerging technologies.
  • Take advantage of the opportunity to work on complex projects that aren't typically available in undergraduate programs.
  • Stay current with rapidly evolving trends.
• Networking and Professional Development
  • Experience direct interaction with experienced faculty.
  • Build lasting relationships with peers who share similar professional interests.
  • Explore opportunities for collaborative research and group projects.
  • Take advantage of face-to-face mentorship opportunities with faculty and industry professionals.
• Enhanced Career Opportunities
  • Capitalize on higher earning potential with an advanced degree.
  • Position yourself for leadership and specialized technical roles.
  • Access UWRF's extensive alumni network and industry connections.
  • Strengthen your resume with hands-on research experience.

Requirements for admission include:

• A completed application
• A baccalaureate degree from an accredited institution
• A major in computer science, STEM or related field

Required Prerequisites- All students must have a basic foundation in the following undergraduate computer science courses:

• Programming I (CIDS 161 at UWRF)
• Programming II (CIDS 162)
• Object Oriented Programming (CIDS 235)
• Database Management (CIDS 333)
• Software Engineering (CIDS 343)

Students who enter the MSCS program lacking some of the foundation courses or all of the foundation courses must complete them satisfactorily with a grade of C or higher.  The coursework may be completed at UWRF or at another institution or online. If students are completing the foundation coursework through UWRF, graduate tuition levels and campus fees will apply if they have already been admitted to the MSCS program as graduate students.  Students needing to fulfill the foundation requirement should consult with the CIDS Chair for recommendations on how to best complete this portion of the program.

Preferred Prerequisites:

• Introductory CS course on Computer Networks
• Experience using a Linux or Unix shell

• Apply online with the electronic Universities of Wisconsin application.
• Submit a CV/resume and a 500-word personal statement of purpose that addresses:
  • Why you are interested in the MSCS program at UWRF.
  • What you feel you can contribute to the program.
  • What your expectations are and what you want to gain from the program.
• Submit two letters of recommendation. At least one should be from a job supervisor or a faculty member in the field of computer science.
• Submit your resume and Letters of Recommendation by uploading them in Slate. If you are unable to upload them, email them to graduateadmissions@uwrf.edu.  
• Submit official transcripts from all accredited colleges or universities attended.
  • ALL college and/or university transcripts, including any high school courses taken for college credit. Transcripts MUST be sent directly to UW admissions either electronically or by mail.
  • Electronic transcripts can be sent through Credentials Solutions, Parchment or the National Student Clearinghouse.
  • Be sure to order transcripts EARLY to ensure they are delivered on time.
• International students can find more information through this link.
• Incomplete applications will not be reviewed. Deferral of admission is possible.

Applications are accepted on a rolling basis. Once admitted, you can begin in the very next semester!

Total credits required: 30 credits

Required (27 cr)

• CIDS 630 Enterprise and Cloud Computing
• CIDS 631 Distributed and Mobile Computing
• CIDS 634 Software Engineering and Design Patterns
• CIDS 733 Computing for Data Sciences and Data Analysis
• CIDS 734 Designing and Managing Artificial Intelligence Systems (New)
• CIDS 735 Machine Learning and Knowledge Discovery
• MNGT 745 Artificial Intelligence for Business
• MNGT 750 Business Analytics
• CIDS 738 Practicum

Elective (3 cr), choose one of:

• MNGT 706 Project Management
• MNGT 725 Organizing for Innovation
• CIDS 789 Special Topics in Computer Science

Course Descriptions

• CIDS 630 Enterprise and Cloud Computing
  • This course provides an advanced-level coverage of concepts in Cloud Computing and the application of these concepts in the setting of an enterprise. Students will learn about the rationale for virtualization and the differences between software as a service (SaaS), platform as a service (PaaS), infrastructure as a service (IaaS) and other cloud services. Students will gain practice solving typical enterprise problems using these cloud computing techniques. The course will cover enterprise information technology requirements and strategies for solving problems at the enterprise level. Topics may include: Service Oriented Architecture, System Administration and Total Cost of Ownership, Virtualization, Cloud Computing. Prerequisites: Admission to MSCS. (Offering Term: Fall)
• CIDS 631 Distributed and Mobile Computing
  • This course will explore theoretical and practical aspects of distributed and mobile computing. It will examine the main issues in mobile software development and the features and limitations of mobile hardware. Students will compare software development approaches for desktop and mobile platforms and gain hands-on experience in writing mobile applications using native application frameworks as well as cross-platform tools. Students will work in teams to undertake one or more mobile application programming project(s). Prerequisites: Admission to the Masters of Science in Computer Science. (Offering Term: Spring)
• CIDS 634 Software Engineering and Design Patterns
  • This course provides students with a software architect's view of software projects. Students will learn about the use of design patterns to simplify and reuse code design. Students will gain practice solving typical software construction issues. A significant component of this course is a team software design project. Topics may include: requirements analysis, Unified Modeling Language, feasibility analysis, design patterns. Prerequisites: Admission to the Masters of Science in Computer Science. (Offering Term: Fall)
• CIDS 733 Computing for Data Sciences and Data Analysis
  • This course provides an advanced coverage of computing techniques used for Data Science and Big Data Analysis. Students will learn fundamental computing skills necessary for effective data analysis. Technologies and techniques for efficient and effective data collection, conversion, analysis, visualization, interpretation, storage and search will be discussed. Prerequisites: Admission to the Masters of Science in Computer Science (Offering Term: Sp).
• CIDS 734 Designing and Managing Artificial Intelligence Systems
  • This course will explore the creation and use of artificial intelligence systems to solve problems in science, education, and business. Students will learn to design, implement, and manage AI systems using cloud-based resources, with emphasis on large language models and multi-agent systems. Topics include infrastructure setup, programming interfaces and engineering, model fine-tuning, data pipeline development, and system optimization. Prerequisites: Admission to MSCS or other graduate programs Offering Term(s): As Offered
• CIDS 735 Machine Learning and Knowledge Discovery
  • This course provides advanced coverage of Machine Learning theory, concepts, techniques and their application to Knowledge Discovery and pattern recognition problems. Topics include: Supervised learning (parametric/non-parametric, support vector machines and neural networks), Unsupervised learning (dimensionality reduction, recommender systems and clustering) and best practices in machine learning (bias/variance and model selection). Prerequisites: Admission to the Masters of Science in Computer Science (Offering Term: F).
• CIDS 738 Practicum
  • This course provides students the opportunity to employ one or more of the concepts developed throughout the graduate program to develop a practical project or explore a new area of research. Students will work with the business community to identify and develop a practical project; or will work with a faculty to identify and work on a research project. Prerequisites: Admission to the Masters of Science in Computer Science (Offering Term: F, Sp, SS).
• CIDS 789 Special Topics in Computer Science
  • Topics in computer science beyond the coursework offered. determined by instructor, varies by term.
• MNGT 706 Project Management
  • This course provides an introduction to the project management process. A project is a unique, one-time operation designed to accomplish a one-of-a-kind objective in a limited time frame. Management plays a crucial role in executing and completing projects efficiently. Content will include project selection, organization, definition, estimating completion time and cost, schedule development and execution, risk minimization, and general project management techniques for managing teams, timelines, evaluation of performance, and project closure. The topics covered mirror the Project Management Body of Knowledge (PMBOK) needed as background for those who intend to take the CAPM exam. Prerequisites: Admission to a UWRF graduate program or graduate certificate program and successful completion of an introductory statistics course. Online. As Offered.
• MNGT 725 Organizing for Innovation
  • This course focuses on developing skill in designing processes that support scaled innovation in organizations. Grounded in the systemic nature of innovation, students learn collaborative approaches for developing strategic innovation opportunities, how to gain insight from industry analysis, structuring digital funding streams, appropriately use innovation metrics, and design agile structures that are responsive to disruptive environments. Online. Term: As offered.
• MNGT 745 Artificial Intelligence for Business
  • This course will explore the applications of artificial intelligence (AI) and its sub-domains to current and future business problems. Students will learn the terminology common to the AI field as well as uses and limitations of artificial intelligence, machine learning, and deep learning in business settings. Students will also consider the ethical ramifications of AI technologies in business and society. Prerequisites: Admission to a UWRF graduate program or graduate certificate program and successful completion of an introductory statistics course. (Online. As offered.
• MNGT 750 Business Analytics
  • This course synthesizes concepts from statistics, data visualization and other analytical tools to prepare the student to turn data into actionable business intelligence and then communicate these findings and recommendations to the top management team of an organization. Emphasis is placed on using business analytics to solve problems in varied business functions such as finance, accounting, marketing, and operations. Prerequisites: Admission to a UWRF graduate program or graduate certificate program and successful completion of an introductory statistics course. As Offered.