Get started in the non-credit version of a course while you wait for the next CU degrees on Coursera enrollment period. You can upgrade, pay tuition, and transfer your progress to the for-credit version during any future enrollment period.
Click on a course below to learn more and enroll in the non-credit version. Find more curriculum details for on the ME-EM on Coursera program website.
Pathway Courses
Finance for Technical Managers (Pathway Specialization)
- EMEA 5021 Product Cost and Investment Cash Flow Analysis
- EMEA 5022 Project Valuation and the Capital Budgeting Process
- EMEA 5023 Financial Forecasting and Reporting
Project Management (Pathway Specialization)
- EMEA 5031 Foundations and Initiation
- EMEA 5032 Project Planning and Execution
- EMEA 5033 Agile Project Management
Core Courses
Principles of Leadership: Leading Oneself
- EMEA 5051 Leading Oneself with Self-Knowledge
- EMEA 5052 Leading Oneself with Purpose and Meaning
- EMEA 5053 Leading Oneself with Personal Excellence
Technical Communication
- EMEA 5016 Communication as a Technical Leader
- EMEA 5017 Technical Managerial Written Skills
- EMEA 5018 Speaking to a Technical Group
Elective Courses
Global Perspectives of Diversity, Equity, and Inclusion (DEI) in the Workplace
- EMEA 5057 Your World and What Shapes It
- EMEA 5058 "Their" World and How You Define It
- EMEA 5059 Our World and How To Accept It
Principles of Leadership: Leading Technical Organizations
- EMEA 5054 Leadership Style and Building a High-Performance Team
- EMEA 5055 Accountability and Employee Engagement
- EMEA 5056 Value Creation and Building Enduring Relationships
Technology Entrepreneurship
- EMEA 5091 Getting Started with Startups
- EMEA 5092 Creating a Startup Company
- EMEA 5093 Forming, Funding, and Launching a Startup Company
Other Electives (Optional)
Up to 9 credits of courses from other CU degrees on Coursera programs may be applied as elective credits toward the ME-EM on Coursera. Courses must be graduate-level and meet all applicable academic standards. Courses may not be double counted toward two credentials of the same level. See the ME-EM on Coursera Student Handbook for details.
Click on a course below to learn more and enroll in the non-credit version. Find more curriculum details for on the MS-DS on Coursera program website.
This new degree program will launch in Fall 2023. Preview course content now with the non-credit version of one of these courses. Work you complete in the non-credit version will transfer to the for-credit version when you upgrade and pay tuition.
- Apr. 13: Preview opens for select non-credit courses
- Aug. 14: For-credit enrollment opens
- Aug. 28: For-credit courses open
Pathway Courses(Non-Credit)
Graduate Algorithms (Pathway Specialization)
- CSCA 5414: Dynamic Programming, Greedy Algorithms – Same as DTSA 5503
Software Architecture for Big Data (Pathway Specialization)
- CSCA 5008: Fundamentals of Software Architecture for Big Data – Same as DTSA 5507
Breadth Courses(Non-Credit)
Machine Learning: Theory & Hands-On Practice with Python
- CSCA 5622: Introduction to Machine Learning: Supervised Learning – Same as DTSA 5509
Click on a course below to learn more and enroll in the non-credit version. Find more curriculum details for on the MS-DS on Coursera program website.
Expressway Courses (Non-Credit)
Expressway to Data Science: R Programming & Tidyverse
- Introduction to R Programming and Tidyverse – Non-credit only
- Data Analysis with Tidyverse – Non-credit only
- R Programming and Tidyverse Capstone Project – Non-credit only
Expressway to Data Science: Python Programming
- Introduction to Python Fundamentals – Non-credit only
- Introduction to Python Functions – Non-credit only
- Python Packages for Data Science – Non-credit only
Expressway to Data Science: Essential Math
- Algebra and Differential Calculus for Data Science – Non-credit only
- Essential Linear Algebra for Data Science – Non-credit only
- Integral Calculus and Numerical Analysis for Data Science – Non-credit only
Pathway Courses
Data Science Foundations: Data Structures and Algorithms (Pathway Specialization)
- DTSA 5501 Algorithms for Searching, Sorting, and Indexing
- DTSA 5502 Trees and Graphs: Basics
- DTSA 5503 Dynamic Programming, Greedy Algorithms – Same as CSCA 5414
Data Science Foundations: Statistical Inference (Pathway Specialization)
- DTSA 5001 Probability Theory: Foundation for Data Science
- DTSA 5002 Statistical Inference for Estimation in Data Science
- DTSA 5003 Hypothesis Testing for Data Science
Data Science Core Courses
Vital Skills for Data Scientists
- DTSA 5301 Data Science as a Field
- DTSA 5302 Cybersecurity for Data Science
- DTSA 5303 Ethical Issues in Data Science
- DTSA 5304 Fundamentals of Data Visualization
Computer Science Core Courses
Machine Learning: Theory & Hands-On Practice with Python
- DTSA 5509 Introduction to Machine Learning: Supervised Learning – Same as CSCA 5622 & DTSA 5900-11
- DTSA 5510 Unsupervised Algorithms in Machine Learning
- DTSA 5511 Introduction to Deep Learning
Data Mining Foundations and Practice
- DTSA 5504 Data Mining Pipeline
- DTSA 5505 Data Mining Methods
- DTSA 5506 Data Mining Project
Databases for Data Scientists
- DTSA 5733 Relational Database Design – Core Course
- DTSA 5734 The Structured Query Language (SQL) – Core Course
- DTSA 5735 Advanced Topics and Future Trends in Database Technologies – Elective Course
Statistics Core Courses
Statistical Modeling for Data Science
- DTSA 5011 Modern Regression Analysis in R
- DTSA 5012 ANOVA and Experimental Design
- DTSA 5013 Generalized Linear Models and Nonparametric Regression
Elective Courses
High-Performance and Parallel Computing
Data Science Methods for Quality Improvement
- DTSA 5704 Managing, Describing, and Analyzing Data – Same as DTSA 5900-1
- DTSA 5705 Stability and Capability in Quality Improvement
- DTSA 5706 Measurement Systems Analysis
Databases for Data Scientists
- DTSA 5733 Relational Database Design – Core Course
- DTSA 5734 The Structured Query Language (SQL) – Core Course
- DTSA 5735 Advanced Topics and Future Trends in Database Technologies – Elective Course
Deep Learning Applications for Computer Vision
Effective Communication
- DTSA 5842 Effective Communication: Writing, Design and Presentation
- DTSA 5843 Effective Communication Capstone Project
Statistical Learning for Data Science
- DTSA 5020 Regression and Classification
Software Architecture for Big Data
- DTSA 5507 Fundamentals of Software Architecture for Big Data – Same as CSCA 5008
- DTSA 5508 Big Data Architecture in Production
- DTSA 5714 Applications of Software Architecture for Big Data
Text Marketing Analytics
- DTSA 5798 Supervised Text Classification for Marketing Analytics
- DTSA 5799 Unsupervised Text Classification for Marketing Analytics
- DTSA 5800 Network Analysis for Marketing Analytics
Finance for Technical Managers
- EMEA 5021 Product Cost and Investment Cash Flow Analysis
- EMEA 5022 Project Valuation and the Capital Budgeting Process
- EMEA 5023 Financial Forecasting and Reporting
Project Management
- EMEA 5031 Foundations and Initiation
- EMEA 5032 Project Planning and Execution
- EMEA 5033 Agile Project Management
Click on a course below to learn more and enroll in the non-credit version. Find more curriculum details for on the MS-EE on Coursera program website.
Embedded Systems
Embedding Sensors and Motors (Pathway Specialization)
- ECEA 5340 Sensors and Sensor Circuit Design (0.8 credits)
- ECEA 5341 Motors and Motor Control Circuits (0.8 credits)
- ECEA 5342 Pressure, Force, Motion, and Humidity Sensors (0.8 credits)
- ECEA 5343 Sensor Manufacturing and Process Control (0.6 credits)
FPGA Design for Embedded Systems (Pathway Specialization)
- ECEA 5360 Introduction to FPGA Design for Embedded Systems (0.8 credits)
- ECEA 5361 Hardware Description Languages for FPGA Design (0.8 credits)
- ECEA 5362 FPGA Softcore Processors and IP Acquisition (0.8 credits)
- ECEA 5363 Building FPGA Projects (0.6 credits)
Developing Industrial Internet of Things
- ECEA 5385 Industrial IoT Markets and Security (1 credit)
- ECEA 5386 Project Planning and Machine Learning (1 credit)
- ECEA 5387 Modeling and Debugging Embedded Systems (1 credit)
Real-Time Embedded Systems
- ECEA 5315 Concept and Practices (0.6 credits)
- ECEA 5316 Theory and Analysis (0.8 credits)
- ECEA 5317 Mission-Critical - SW Applications (0.8 credits)
- ECEA 5318 Real-Time Embedded Systems Project (0.8 credits)
Embedded Interface Design
- ECEA 5346 User Experience Interface Design for Embedded Systems (1 credit)
- ECEA 5347 Rapid Prototyping of Embedded Interface Designs (1 credit)
- ECEA 5348 M2M IoT I/F Interface Design & Protocols (1 credit)
Advanced Embedded Linux Development
- ECEA 5305 Linux System Programming and Introduction to Buildroot (1 credit)
- ECEA 5306 Linux Kernel Programming and Introduction to Yocto (1 credit)
Power Electronics
Power Electronics (Pathway Specialization)
- ECEA 5700 Introduction to Power Electronics (0.8 credits)
- ECEA 5701 Converter Circuits (1 credit)
- ECEA 5702 Converter Control (1.2 credits)
- ECEA 5703 Magnetics Design (1 credit)
Power Electronics Capstone
- ECEA 5715 Power Electronics Capstone Project (1.2 credits) - No non-credit version available
Modeling and Control of Power Electronics
- ECEA 5705 Averaged Switch Modeling and Simulation (0.8 credits)
- ECEA 5706 Technical Design-Oriented Analysis (0.6 credits)
- ECEA 5707 Input Filter Design (0.6 credits)
- ECEA 5708 Current Mode Control (1.2 credits)
- ECEA 5709 Modeling and Control of Single-Phase Rectifiers and Inverters (0.6 credits)
Algorithms for Battery Management Systems
- ECEA 5730 Introduction to Battery-Management Systems (0.8 credits)
- ECEA 5731 Equivalent Circuit Cell Model Simulation (0.8 credits)
- ECEA 5732 Battery State-of-Charge (SOC) Estimation (1 credit)
- ECEA 5733 Battery State-of-Health (SOH) Estimation (0.8 credits)
- ECEA 5734 Battery Pack Balancing and Power Estimation (0.8 credits)
Photovoltaic Power Electronics
- ECEA 5716 Open-Loop Photovoltaic Power Electronics Laboratory (1 credit) - Same as ECEA 5005 - No non-credit version available
- ECEA 5717 Closed-Loop Photovoltaic Power Electronics Laboratory (1 credit) - Same as ECEA 5006 - No non-credit version available
- ECEA 5718 Photovoltaic Power Electronics Battery Management Laboratory (1 credit) - No non-credit version available
Power Semiconductor Devices
- ECEA 5721 Introduction to Power Semiconductor Switches (0.6 credits)
Photonics and Optics
Optical Engineering (Pathway Specialization)
- ECEA 5600 First Order Optical System Design (1 credit)
- ECEA 5601 Optical Efficiency and Resolution (1 credit)
- ECEA 5602 Design of High-Performance Optical Systems (1 credit)
Semiconductor Devices (Pathway Specialization)
- ECEA 5630 Semiconductor Physics (1 credit)
- ECEA 5631 Diode - pn Junction and Metal Semiconductor Contact (1 credit)
- ECEA 5632 Transistor - Field Effect Transistor and Bipolar Junction Transistor (1 credit)
Active Optical Devices
- ECEA 5605 Light Emitting Diodes and Semiconductors Lasers (1.2 credits)
- ECEA 5606 Nanophotonics and Detectors (1.2 credits)
- ECEA 5607 Displays (0.6 credits)
Quantum Mechanics for Engineers
- ECEA 5610 Foundations of Quantum Mechanics (1.4 credits)
- ECEA 5611 Theory of Angular Momentum (0.8 credits)
- ECEA 5612 Approximation Methods (0.8 credits)
Elective Courses
Up to 9 credits of courses from other CU degrees on Coursera programs may be applied as elective credits toward the MS-EE on Coursera. Courses must be graduate-level and meet all applicable academic standards. Courses may not be double counted toward two credentials of the same level. See the MS-EE on Coursera Student Handbook for details.
Indicates a cross-listed course offered under two or more programs (e.g., Dynamic Programming, Greedy Algorithms is offered as both CSCA 5414 and DTSA 5503). You may not earn credit for more than one version of a cross-listed course.
How It Works
- The work you complete in the non-credit version of a course transfers to the for-credit version when you upgrade and pay tuition. Due to their interactive nature, discussion board posts and peer-graded assignments may not transfer from session to session if you drop/withdraw and later re-enroll in a particular class. Be sure to save your work outside of the Coursera platform.
- After you upgrade, you will complete additional graded assignments to earn credit for the course. Select courses may require the purchase of additional materials to complete graded assignments. See course pages linked above for details.
- You can upgrade from non-credit to for-credit at any time during your learning journey.
Students currently enrolled in for-credit CU degree on Coursera courses are eligible for free access to non-credit versions of 200+ CU courses through the CU on Coursera learning program. This program grants you sponsored access to a selected bundle of non-credit (open) courses from across the University of Colorado Coursera portfolio. Click below to learn more and get started.
