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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

After you enroll in the Machine Learning & Data Science Foundations program, you will take six graduate-level, credit-bearing courses. 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 and Foundations of Computational Data Science courses are delivered in two consecutive parts at 6 units each.

The certificate includes the following courses taught by CMU faculty:

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.

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.

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: 11-671 & 11-672

Units: 6 units each

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.

Meet Our World-Class Faculty

Dr. Carolyn RoséDr. Carolyn Rosé

Professor of Language Technologies and Human-Computer Interaction

Education: Ph.D., Carnegie Mellon University

Research Focus: to better understand the social and pragmatic nature of conversation and to build computational systems that improve the efficacy of conversation between people, and between people and computers by using approaches from computational discourse analysis and text mining, conversational agents, and computer-supported collaborative learning. 

Dr. Henry ChaiDr. Henry Chai

Assistant Teaching Professor of Machine Learning

Education: Ph.D., Washington University in St. Louis

Research Focus: topics at the intersection of Bayesian machine learning, probabilistic numerics and active learning that help address the following question: how can we efficiently and accurately reason about inherently intractable quantities? Dr. Chai is also passionate about pedagogical research and K-12 computer science education.

CMU School of Computer Science logo

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:

autonomous driving

Researchers from CMU’s Robotics Institute completed a long-distance autonomous driving test in 1995 called the No Hands Across America mission.

football field

In 2001, SCS Founders University Professor Takeo Kanade and his team created a video replay system called EyeVision for Super Bowl XXXV.

Graphic of autonomous vehicle data

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.



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.



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.