Cutting-Edge Curriculum
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.
Curriculum Overview
The online Graduate Certificate in Machine Learning & Data Science Foundations includes five graduate-level, credit-bearing courses taught by expert CMU faculty and features the following course progression:
For Summer 2025 Start:
|
Semester |
Summer 2025 |
Fall 2025 |
Spring 2026 |
|---|---|---|---|
| Course |
Python for Data Science 1 Python for Data Science 2 |
Computational Foundations of Machine Learning |
Foundations of Computational Data Science |
For Fall 2025 Start:
|
Semester |
Fall 2025 |
Spring 2026 |
Summer 2026 |
|---|---|---|---|
| Course |
Computational Foundations of Machine Learning |
Python for Data Science 1 Python for Data Science 2 |
Foundations of Computational Data Science |
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.
Please note: the Python for Data Science course is delivered in two consecutive parts at 6 units each.
Course Descriptions:
Mathematical Foundations of Machine Learning
Course Number: 10-680
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.
Computational Foundations for Machine Learning
Course Number: 10-681
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.
Python for Data Science
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.
Foundations of Computational Data Science
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.
Please note: The Foundations of Computational Data Science course requires a computing infrastructure fee of approximately $300 to run models and analysis (subject to change).
Course Waivers
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.
Meet Our World-Class Faculty
Dr. Carolyn Rosé
Professor of Language Technologies and Human-Computer Interaction
Education: Ph.D., Carnegie Mellon University
Research Focus: Dr. Rosé's research focuses on better understanding the social and pragmatic nature of conversation, and using that understanding to build computational systems that improve the efficacy of conversation between people, and between people and computers. To pursue these goals, Dr. Rosé uses approaches from computational discourse analysis and text mining, conversational agents, and computer-supported collaborative learning.
The Graduate Certificate in Machine Learning & Data Science Foundations 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 below:
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.
The Building Blocks of Our Curriculum
Industry Impact
In this program, everything you learn serves a greater purpose - to approach and solve large-scale, real-world data challenges in today’s world. After learning fundamental skills in math and computational data science, you’ll have a firm understanding of cloud-based technologies and the ability to solve problems across industries with innovative solutions.
Hands-On
By completing practical, interactive, and collaborative coursework along with hands-on training exercises, you’ll be prepared to: define the analytical requirements of a data science problem, design a data gathering plan, build and deploy models using the right analytic algorithms, and improve models to achieve organizational objectives.
Collaborative
Interdisciplinary work is a core value of Carnegie Mellon. As you complete the coursework for this program, you will explore computational data science from different perspectives, participate in powerful discussions, and gain insights from different departments within the School of Computer Science, including: the Language Technologies Institute, Computer Science Department, Human-Computer Interaction Institute, and Machine Learning Department.