The Professional Master of Science (MSCPS) is a degree program that offers possibilities for a wide range of students. Whether the student is a working engineer or an undergraduate considering a career in industry, there are program options to meet their needs.
The department offers nine degree tracks, each of which result in a Professional Master of Science in Computer Science:
- Algorithms, Network and Optimization (ANO)
- Artificial Intelligence (AIG)
- Data Science and Engineering (DSE)
- Human-Centered Computing (HCC)
- Numerical Computation (NUM)
- Robotics (RBT)
- Software Systems and Cloud Computing (SSC)
- Security (SEC)
- General Track
The Department of Computer Science has embraced this degree as an ideal opportunity to expand the high quality courses in the fields above into a wide array of courses leading to a full master's degree. The goal of the MSCPS program is to produce creative, workforce-ready graduates equipped with versatile specialized skills and technical leadership.
Adding several new subplan courses to the program now enables greater options for earning professional MS degree with a these subplans, while also offering plenty of courses to complete a full master's degree, principally with a subplan focus. Students pursuing this degree will also have access to many excellent graduate-level courses offered by the department's highly reputed faculty.
Subplans
General Track
Students opting for this track have the option to select classes from an approved list for the degree. The jobs these students get are also similar to other subplans. However, specializing in a subplan is more beneficial.
Algorithms, Network and Optimization
The subplan enables students to employ powerful mathematical tools and techniques from algorithms, graph theory, computational complexity theory and mathematical optimization to solve problems that may arise in research and development of cutting edge computing systems. Skills include: design and analysis of algorithms, understanding inherent problem complexity and deploying optimization-based tools and techniques. We expect graduates to fill software development roles with an emphasis on algorithms design, data analysis and solution design.
Potential job titles include: graph theorist, optimization analyst, software developer on algorithms design and data analysts on algorithms design. Potential employers include Twitter, Google, Facebook, Amazon, Oracle, Uber, Microsoft and Apple.
Data Science and Engineering
This subplan provides the skills to develop computer solutions that require expertise in data science and engineering. Students who complete the program receive both a master's degree in computer science and a specialization within data science and engineering. This combination is very attractive as technology companies are looking for developers that have experience in data science. Students complete both a set of core courses for the degree in addition to a set of data science courses.
Potential job titles include: Hadoop developer, BI developer, quantitative data engineer, search engineer, technical architect, big data analyst, solutions architect, data warehouse engineer, data science software engineer and ETL developer. Potential employers include Twitter, Google, Facebook, Amazon, Oracle, Uber, Microsoft and Apple.
Human-Centered Computing
In this track, students learn how to design, implement and evaluate user interfaces for a range of computing technology, and gain skills related to designing technologies to support the needs of real people. Topics covered include user-centered design, information visualization, universally accessible design and computer-supported cooperative work. Students will gain experience with the entire user-centered design process, from requirements gathering, prototyping, and qualitative and quantitative user evaluation. Many courses in this concentration are project-based and will involve user-centered research in the lab and in the field.
Potential job titles for graduates of this program include user experience researcher, user interface engineer, data scientist, interaction designer, front-end developer, accessibility specialist, mobile application developer. Potential employers include Facebook, Google, Microsoft, Twitter, Adobe, Autodesk, Sphero, Snap and Oculus.
Artificial Intelligence and Robotics
The subplans in artificial intelligence and robotics build expertise in algorithms and methods for developing autonomous systems, including robotics and cyber-physical systems. As part of this program, students will design and analyze systems which leverage computation to interact with the world around them through sensors and actuators. Machine learning, signal processing and control theory are all components to this program, where students become experts in creating the software for devices ranging from climate control systems to automobiles.
Potential job titles for graduates of this program focusing on artificial intelligence include: software engineer, perception engineer, data scientist and research engineer. Potential employers include Lockheed-Martin, Amazon, Microsoft, Google and Facebook.
Potential job titles for graduates of this program focusing on robotics include: robotics engineer, perception engineer, control engineer and robotics scientist. Potential employers include: Amazon Robotics, Uber, Google, iRobot and DJI.
Numerical Computation
Ongoing improvements in computational capability and memory performance have increased the importance of high-fidelity simulations, optimization and data-driven science and engineering applications. Students in this subplan develop the skills to design robust and high-performance numerical methods for addressing real-world problems and develop production-grade implementations using state of the art software tools to target modern architectures and large-scale parallel computers.
Potential job titles: Computational scientist/engineer, numerical/data analyst, research scientist, software engineer, HPC developer and quantitative software engineer. Potential employers include national labs, universities, engineering ISVs (ANSYS, MSC, CD-adapco), aerospace (NASA, Boeing, ULA, SpaceX, Lockheed), exploration (Shell, Schlumberger, CMG), manufacturing (P&G, GE), technology (Amazon, Google, IBM, Motorola) and finance (HFT, mutual funds, credit card).
Software Systems and Cloud Computing
In this subplan, students learn about software systems and how they are applied to real world problems. They'll also discover how emerging cloud computing technologies can be used to implement some of the world’s most popular services and applications.
Security
This sub-plan provides students with opportunities to explore cybersecurity and related challenges in the protection of critical systems and information from digital attacks. Security challenges impact many aspects of computer architecture, networks, applications, data, devices, and other sensitive infrastructure and information assets. Students will gain a broad spectrum of practical knowledge and experience, an understanding of theoretical underpinnings, and awareness of policy/standards that are related to computer and network security.
Potential job titles include—but are not limited to—security engineer, information security analyst, penetration tester, network security engineer, and data recovery professional. Companies that typically hire security-related positions range throughout industry, software, and government agencies including Meta, Google, Amazon, Splunk, Accenture, NSA, Salesforce, Mastercard, and many others.
For more information, visit the department's Professional MS Degree Program Requirements webpage.
Bachelor's–Accelerated Master's Degree Program
Students may earn this degree as part of the bachelor's–accelerated master's (BAM) degree program, which allows currently enrolled CU Boulder undergraduate students the opportunity to earn a bachelor's and master's degree in a shorter period of time.
For more information, see the Accelerated Master's tab for the associated bachelor's degree(s):
- Applied Computer Science - Post-Baccalaureate Bachelor of Science (BSACS)
- Computer Science - Bachelor of Arts (BA)
- Computer Science - Bachelor of Science (BSCS)
Distance Education Option
Students can take individual courses toward a master's degree or graduate certificate through distance education (online). For more information, connect with the individual graduate program directly.
Requirements
Admission Requirements
Applicants for graduate study in computer science must hold at least a bachelor's degree or its equivalent from an accredited institution. They should have programming experience, a number of computer science courses and sufficient mathematical maturity to understand pure mathematics courses at the upper division (junior/senior) level. A minimum undergraduate GPA of 3.0 (on a scale of 4.0) is required for admission to the master's program.
Applicants are not required to submit GRE scores.
Mathematics Courses
A student's academic background should include at least three semesters of mathematics at the level of sophistication of calculus or above. Examples of such courses include calculus, differential equations, linear algebra, probability, statistics and abstract algebra. The courses should indicate that the student has achieved the mathematical maturity expected of an upper-level science, engineering or mathematics undergraduate.
Computer Science Courses
At least four one-semester courses in computer science that are beyond the introductory level are required for admissions. These are intended to demonstrate breadth of basic computer science knowledge in the areas of computer hardware, software and theory. The courses should include the equivalent of the following CU Boulder offerings:
- Hardware requirement: CSCI 2400 Computer Systems (Computer Systems)
- Software requirement: Either CSCI 3155 Principles of Programming Languages or CSCI 3753 Design and Analysis of Operating Systems
- Theory requirement: CSCI 2270 Computer Science 2: Data Structures and either CSCI 3104 Algorithms or CSCI 3434 Theory of Computation
Required Courses and Credits
The following requirements are subject to change; for the most current information, visit the department's Professional MS Degree Program Requirements webpage.
Degree Requirements
Students must complete a total of 30 credit hours of approved graduate level course work with a grade of C or better and a cumulative GPA of at least 3.00. Students are allowed to take two non-CS courses as long as these are 5000 level and above and are offered at CU Boulder main campus. Students under this option are not allowed to take research hours or thesis option.
| Code | Title | Credit Hours |
|---|---|---|
| Breadth Courses | ||
| Students must complete one breadth course from each of the three bins listed below, for a total of 9 credits of breadth courses. Students must earn a grade of B or better in each of the three breadth courses. | ||
| Bin One | ||
| Choose one: | 3 | |
| Computer Graphics | ||
| Convex Optimization and Its Applications | ||
| Probability for Computer Science | ||
| Introduction to Theory of Computation | ||
| Chaotic Dynamics | ||
| Design and Analysis of Algorithms | ||
| High-Performance Scientific Computing | ||
| Principles of Numerical Computation | ||
| Numerical Solution of Partial Differential Equations | ||
| Numerical Linear Algebra | ||
| Linear Programming | ||
| Numerical Optimization | ||
| Bin Two | ||
| Choose one: | 3 | |
| Introduction to Robotics | ||
| Advanced Robotics | ||
| Algorithmic Human-Robot Interaction | ||
| Network Analysis and Modeling | ||
| Research Methods in Human-Robot Interaction | ||
| Data Mining | ||
| Introduction to Virtual Reality | ||
| Machine Learning | ||
| Computer Vision | ||
| Probabilistic and Causal Modeling in Computer Science | ||
| Natural Language Processing | ||
| User-Centered Design and Development 1 | ||
| Input, Interaction, and Accessibility | ||
| Fundamentals of Neural Networks and Deep Learning | ||
| Bin Three | ||
| Choose one: | 3 | |
| Computer-Aided Verification | ||
| Datacenter Scale Computing - Methods, Systems and Techniques | ||
| Network Systems | ||
| Introduction to Computing Security | ||
| Computer Security and Ethical Hacking | ||
| Object-Oriented Analysis and Design | ||
| Modern Offense and Defense in Cybersecurity | ||
| Compiler Construction | ||
| Fundamental Concepts of Programming Languages | ||
| Advanced Operating Systems | ||
| Distributed Systems | ||
| Computer Performance Modeling | ||
| Database Systems | ||
| Foundations of Software Engineering | ||
| Theoretical Foundations of Autonomous Systems | ||
| Project Courses | ||
| Complete six credits of projects class from either of the following two options. All students must earn a B or better (not B-) in these courses. | 6 | |
| Startup Essentials: Entrepreneurial Projects in Computing and Entrepreneurial Projects II | ||
| Professional Masters Project 1 and Professional Masters Project 2 | ||
| Additional Coursework | ||
| An additional 15 credits are required to complete the degree, with restrictions. 1 | 15 | |
| Total Credit Hours | 30 | |
| 1 | Additional coursework may consist of no more than two non-CS classes, except for classes under subplans. (Classes listed under required sub-plans will not be considered against the two non-CS.) |
Time Limit
All degree requirements must be completed within four years of the date of commencing coursework.
Subplan Tracks
In addition to the above mentioned required courses, students enrolled in any the following subplans must also complete the required subplan courses as listed below.
Students must earn a grade of B or better all subplan courses. Students may count the same course towards their subplan and breadth requirements.
Algorithms, Network and Optimization (ANO)
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Choose four: | ||
| Practical Algorithmic Complexity | ||
| Convex Optimization and Its Applications | ||
| Network Analysis and Modeling | ||
| Probability for Computer Science | ||
| Linear Programming | ||
| Numerical Optimization | ||
| Computational Complexity Theory | ||
| Randomized Algorithms | ||
| Algorithmic Economics | ||
Data Science and Engineering (DSE)
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Choose four: | ||
| Datacenter Scale Computing - Methods, Systems and Techniques | ||
| Convex Optimization and Its Applications | ||
| Network Analysis and Modeling | ||
| Probability for Computer Science | ||
| Data Mining | ||
| High-Performance Scientific Computing | ||
| Machine Learning | ||
| Linear Programming | ||
| Numerical Optimization | ||
| Computer Vision | ||
| Natural Language Processing | ||
| Fundamentals of Neural Networks and Deep Learning | ||
| Big Data Analytics: Systems, Algorithms, and Applications | ||
| Big Data Architecture | ||
Human-Centered Computing (HCC)
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Choose four: | ||
| Computer Graphics | ||
| Advanced Computer Graphics | ||
| Algorithmic Human-Robot Interaction | ||
| Research Methods in Human-Robot Interaction | ||
| Introduction to Virtual Reality | ||
| Computer Animation | ||
| User-Centered Design and Development 1 | ||
| Input, Interaction, and Accessibility | ||
| HCC Survey and Synthesis: Foundations and Trajectories | ||
| HCC Survey and Synthesis: New Disciplinary Directions | ||
| Issues and Methods in Cognitive Science | ||
| Topics in Cognitive Science | ||
| Online Communities | ||
| Information Ethics and Policy | ||
| Information Visualization | ||
| Survey Research Design | ||
| Ethnographic Research in Applied Settings | ||
| User-Centered Design | ||
| Ubiquitous Computing Experience Design | ||
Artificial Intelligence (AIG)
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Choose four: | 12 | |
| Convex Optimization and Its Applications | ||
| Introduction to Robotics | ||
| Advanced Robotics | ||
| Algorithmic Human-Robot Interaction | ||
| Network Analysis and Modeling | ||
| Probability for Computer Science | ||
| Data Mining | ||
| Machine Learning | ||
| Distributed Systems | ||
| Computer Vision | ||
| Probabilistic and Causal Modeling in Computer Science | ||
| Natural Language Processing | ||
| Fundamentals of Neural Networks and Deep Learning | ||
| Statistics, Optimization and Machine Learning Seminar | ||
Numerical Computation (NUM)
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Choose four: | ||
| Computer Graphics | ||
| Advanced Computer Graphics | ||
| Chaotic Dynamics | ||
| High-Performance Scientific Computing | ||
| Principles of Numerical Computation | ||
| Numerical Solution of Partial Differential Equations | ||
| Numerical Linear Algebra | ||
| Linear Programming | ||
| Numerical Optimization | ||
Robotics (RBT)
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Choose four: | ||
| Introduction to Robotics | ||
| Convex Optimization and Its Applications | ||
| Advanced Robotics | ||
| Algorithmic Human-Robot Interaction | ||
| Probability for Computer Science | ||
| Machine Learning | ||
| Computer Vision | ||
| Theoretical Foundations of Autonomous Systems | ||
| Fundamentals of Neural Networks and Deep Learning | ||
| Math Methods in Dynamics | ||
| Optimal Trajectories | ||
| Uncertainty Quantification | ||
Software Systems and Cloud Computing (SSC)
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Choose four: | ||
| Computer-Aided Verification | ||
| Datacenter Scale Computing - Methods, Systems and Techniques | ||
| Network Systems | ||
| Computer Security and Ethical Hacking | ||
| Object-Oriented Analysis and Design | ||
| Data Mining | ||
| Compiler Construction | ||
| Fundamental Concepts of Programming Languages | ||
| Advanced Operating Systems | ||
| Distributed Systems | ||
| Database Systems | ||
| Foundations of Software Engineering | ||
Security (SEC)
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Choose four: | ||
| Linux System Administration | ||
| Network Systems | ||
| Introduction to Computing Security | ||
| Computer Security and Ethical Hacking | ||
| Modern Offense and Defense in Cybersecurity | ||
| Cybersecurity Network Analytics | ||
| Digital Forensics | ||
| Security Auditing and Penetration Testing | ||
| Fundamentals of Computer Security | ||
| Secure Computer Architecture | ||
Learning Outcomes
By the completion of the program, students will be able to:
- Solve technical problems in computer science through writing code, pseudocode, technical writing and/or applying foundational concepts from a variety of subfields.
- Cast large, societal and/or complex problems as computational problems.
- Communicate clearly about their ideas and their capstone project.
- Successfully complete the required course work with a minimum GPA of 3.0. (Evaluation Method 1)
- Successfully complete a capstone project as part of the required project classes. (Evaluation Method 2)
Dual Degree
MSCPS/EMEN in Computer Science and Engineering Management
Computer Science and Engineering Management have teamed up to offer an exciting dual degree for MSCPS students. Student complete a total of 45 credits of graduate-level coursework. Of those, 24 credits are in CS courses and 21 credits are in EMEN courses. All degree requirements must be completed within four years of the date of commencing coursework.
Students filling out the candidacy application for CSMSCPS/EMEN dual degree during the semester they graduate, will have to include the 24 credits of CS coursework plus six credits of approved EMEN courses to come up with the required 30 credits of the CSMSCPS degree within the dual degree.