Machine Learning & Data Science
Harness the power of big data with skills in machine learning and data science
Next Start Date
January 2026
Application Deadlines
Priority*: November 12, 2025
Final: December 3, 2025
Program Length
12 months
Taught By
School of Computer Science
Your Pathway to the AI Workforce
Organizations know how important data is, but they don’t always know what to do with the volume of data they have collected. That’s why Carnegie Mellon University designed the online Graduate Certificate in Machine Learning & Data Science Foundations; to teach technically-savvy professionals how to leverage AI and machine learning technology for harnessing the power of large scale data systems.
Computer-Science Based Data Analytics
When you enroll in this program, you will learn foundational skills in computer programming, machine learning, and data science that will allow you to leverage data science in various industries including business, education, environment, defense, policy and health care. This unique combination of expertise will give you the ability to turn raw data into usable information that you can apply within your organization.
Throughout the coursework, you will:
- Practice mathematical and computational concepts used in machine learning, including probability, linear algebra, multivariate differential calculus, algorithm analysis, and dynamic programming.
- Learn how to approach and solve large-scale data science problems.
- Acquire foundational skills in solution design, analytic algorithms, interactive analysis, and visualization techniques for data analysis.
An online Graduate Certificate in Machine Learning & Data Science from Carnegie Mellon will expand your possibilities and prepare you for the staggering amount of data generated by today’s rapidly changing world.
A Powerful Certificate. Conveniently Offered.
The online Graduate Certificate in Machine Learning & Data Science Foundations is offered 100% online to help computer science professionals conveniently fit the program into their busy day-to-day lives. In addition to a flexible, convenient format, you will experience the same rigorous coursework for which Carnegie Mellon University’s graduate programs are known.
For Today’s Problem Solvers
This leading certificate program is best suited for:
- Industry Professionals looking to deliver value to companies by acquiring in-demand data science, AI, and machine learning skills. After completing the program, participants will acquire the technical know-how to build machine learning models as well as the ability to analyze trends.
- Recent computer science degree graduates seeking to expand their skill set and become even more marketable in a growing field. Over the past few years, data sets have grown tremendously. Today’s top companies need data science professionals who can leverage machine learning technology.
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Curriculum and Course Schedule
The power of data grounded in computer science
Artificial intelligence is transforming how all industries and organizations operate. Now more than ever, there is an increasing demand for data scientists and engineers who can understand and implement machine learning technology. To gain insights from massive data sets, drive efficiency, create technological advancements, and win in the marketplace, organizations need data professionals who can develop powerful algorithms and intelligent machines.
Offered by CMU’s School of Computer Science, one of the nation’s top universities for learning computational data science, this online certificate equips students with the requisite AI skills to solve real, large-scale data problems across various industries.
Each course will appear on your Carnegie Mellon transcript with the grade earned. To earn the certificate, you must successfully complete all courses in the program. If you are only interested in one course, however, you may complete that course only and it will show on your transcript with the grade earned.
| Semester | Spring 2026 | Summer 2026 | Fall 2026 |
|---|---|---|---|
| Course | Python for Data Science 1 Python for Data Science 2 | Mathematical Foundations of Machine Learning Computational Foundations of Machine Learning | Foundations of Computational Data Science |
Please note: the Python for Data Science course is delivered in two consecutive parts at 6 units each.
Course Descriptions
Course Number: 11-960
Units: 6 units
Practice the necessary mathematical background for further understanding in machine learning. You will study topics like probability (random variables, modeling with continuous and discrete distributions), linear algebra (inner product spaces, linear operators), and multivariate differential calculus (partial derivatives, matrix differentials). Some coding will be required; ultimately, you will learn how to translate these foundational math skills into concrete coding programs.
Course Number: 11-961
Units: 6 units
Practice the necessary computational background for further understanding in machine learning. You will study topics like computational complexity, analysis of algorithms, proof techniques, optimization, dynamic programming, recursion, and data structures. Some coding will be required; ultimately, you will learn how to translate these computational concepts into concrete coding programs.
Course Numbers: 11-604 & 11-605
Units: 6 units each
Master the concepts, techniques, skills, and tools needed for developing programs in Python. You will study topics like types, variables, functions, iteration, conditionals, data structures, classes, objects, modules, and I/O operations while also receiving hands-on experience with development environments like Jupyter Notebook and software development practices like test-driven development, debugging, and style. Course projects include real-life applications on enterprise data and document manipulation, web scraping, and data analysis. These courses can be waived for computer science professionals already fluent in Python.
Course Numbers: 11-673
Units: 12 units
Learn foundational concepts related to the three core areas of data science: computing systems, analytics, and human-centered data science. In this course, you will acquire skills in solution design (e.g. architecture, framework APIs, cloud computing), analytic algorithms (e.g., classification, clustering, ranking, prediction), interactive analysis (Jupyter Notebook), applications to data science domains (e.g. natural language processing, computer vision), and visualization techniques for data analysis, solution optimization, and performance measurement on real-world tasks.
Students who already have proficient skills in either math or programming may waive the following courses upon successful completion of an exemption exam(s):
- Math Fundamentals of Machine Learning (10-680) and Computational Fundamentals of Machine Learning (10-681)
- Python for Data Science (11-604 & 11-605)
The exemption exam(s) will be administered to admitted students only. Students who are interested in taking the exam(s) should indicate their interest in the application when applying to the program. Once admitted, additional information about sitting for the exam(s) will be provided.
Upon successful completion of one, or both, of the exemption exams, students will only complete the remaining courses to qualify for the certificate. No credit will be earned, nor tuition will be assessed, for the waived courses.
Please note: Foundations of Computational Data Science is not eligible for a waiver.
For more information about course waivers, contact an admissions counselor today.
Are we the right fit?
Let’s face it, pursuing any kind of advanced training is an investment of your time, energy and resources. Before you consider our program, make sure your background aligns with our program expectations.
Successful applicants will have:
A bachelor’s degree in STEM or related field
Successful applicants will hold a degree in a science, technology, engineering or math-related field. Other degrees will be considered if the applicant can show the necessary proficiency in math and programming.
Relevant work experience
Ideally, applicants will have some relevant work experience in either computer programming or a related field. Internships or other related work are acceptable.
Proficiency in advanced math
Students should provide evidence of successful completion of advanced math coursework such as calculus, linear algebra and statistics.
Proficiency in programming
Students should be proficient in Python, R, or an analogous programming language, with experience writing at least 1000 lines of code.
A disciplined and motivated mindset
Harder to measure, but equally important, successful applicants will have a resilient spirit, a hunger to learn, and a knack for solving problems through technical innovation. With courses taught by CMU faculty from the #4 computer science school in the country, a consistent and conscious effort will be required to master each topic.
Application Requirements
Ready to apply? Here’s what you’ll need to complete the admissions process:
Submit your resume/CV
We’d like to learn more about your employment history, academic background, technical skills, and professional achievements. Submit a 1 to 2 page resume or CV showcasing your experience.
Submit your transcripts
Submit an unofficial copy of your transcript for each school you attended. Transcripts must include your name, the name of the college or university, the degree awarded (along with the conferral date), as well as the grade earned for each course. Email your transcripts directly to apply@online.cmu.edu. Please note: former Carnegie Mellon students and/or alumni can request a copy of their CMU transcript from The Hub.
Upload a statement of purpose
Tell us your professional story. Where have you been, and where do you hope to go? In 500 words or less, please share how our program would advance your capabilities in your current role or prepare you for a new role in the industry.
Submit your TOEFL, IELTS, or DuoLingo test scores
An official TOEFL, IELTS, or DuoLingo test is required for non-native English speakers. This requirement will be waived, however, for applicants who either completed an in-residence bachelor’s, master’s, or doctoral degree program in the United Kingdom, United States, or Canada (excluding Quebec) or have at least three years of professional work experience using English as their primary language. If you fall into one of these categories, please include this information on your resume.
Tuition
By enrolling in our graduate-level programs, you'll be investing in your professional growth to expand your skill set or advance your career. We know this is a significant investment. Not just for you, but your family as well.
| Semester | Course | Units | Investment |
|---|---|---|---|
| Fall 2025 | Mathematical Foundations of Machine Learning | 6 units | $4,242 |
| Computational Foundations of Machine Learning | 6 units | $4,242 | |
| Spring 2026 | Python for Data Science 1 | 6 units | $4,242 |
| Python for Data Science 2 | 6 units | $4,242 | |
| Summer 2026 | Foundations of Computational Data Science | 12 units | $8,484 |
| Total Investment | $25,452 | ||
Additional Fees & Notes
- $245 technology fee will be assessed each semester (subject to change).
- The rates above are for the 2025/2026 academic year only. If the certificate is not completed within that time frame, tuition may increase slightly for the following academic year.
Scholarship Opportunities
All students who submit their application by the priority deadline will receive a partial scholarship to help offset the cost of tuition. If admitted to the program, the amount of your award will be communicated with your admissions letter.
Students who submit after the priority deadline may be eligible for any remaining scholarships that are still available. But we highly encourage you to apply by the priority deadline if you are interested in a scholarship award.
In addition, Carnegie Mellon alumni are eligible for a scholarship to the Graduate Certificate in Machine Learning and Data Science Foundations worth up to 20% of tuition. Indicate your alumni status within the application to be eligible.
About the School of Computer Science
The Graduate Certificate in Generative AI & Large Language Models is offered by the Language Technologies Institute (LTI) at CMU, which is housed within the highly-ranked School of Computer Science (SCS). SCS faculty are esteemed in their field, and many of them have collaborated on critical projects that have paved the way for future discoveries in artificial intelligence. Check out some of their work:
- Researchers from CMU’s Robotics Institute completed a long-distance autonomous driving test in 1995 called the No Hands Across America mission.
- In 2001, SCS Founders University Professor Takeo Kanade and his team created a video replay system called EyeVision for Super Bowl XXXV.
- In 2007, Faculty Emeritus William “Red” Whittaker led CMU’s Tartan Racing team to victory in the DARPA’s Grand Challenge.
- Assistant Research Professor László Jeni used computer vision technology to create a facial recognition tool that can help people with visual impairment.
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