Computational Data Science
Online Graduate Certificate
Cutting-Edge Curriculum
The Power of Data Grounded in Computer Science
Our Philosophy
Artificial intelligence is transforming how all industries and organizations operate. Now, more than ever, there is increasing demand for data scientists and engineers who are experts in machine learning. To drive efficiency, create technological advancements, gain insights for the future, and win in the marketplace, the world needs data professionals who can develop algorithms and create intelligent machines.
Carnegie Mellon is one of the top universities in the nation for learning computational data science. Our certificate program is taught by leading data science researchers and focuses on applying AI to solve real-world industry problems.
Industry Impact
Everything we teach offers significant impact. Our approach is built for the large scale data problems that organizations are currently facing and addresses the cloud-based technologies needed to solve real-world data problems.
You will learn how to apply relevant mathematical and computational concepts and skills to create solutions and innovations. With a solid background in computational data science, you will be able to solve problems across a variety of industries.
Hands-On
Our program is practical and interactive, featuring collaborative coursework and hands-on training. After completing the certificate program, you will have the ability 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 for the future.
Collaborative
At Carnegie Mellon, multi-disciplinary work is part of who we are, and this program is no exception. Our program taps into expertise from the Language Technologies Institute, Computer Science department, Human-Computer Interaction Institute, and Machine Learning department. Bringing together diverse perspectives helps us foster more powerful and challenging discussions. When you enroll in our certificate program, you can trust that you will learn computational data science from all angles.
Meet Our World-Class Faculty
Bryan Wilder, Assistant Professor, Machine Learning
Dr. Bryan Wilder's research focuses on AI for equitable, data-driven decision making in high-stakes social settings, integrating methods from machine learning, optimization, and social networks. Much of his work is motivated by public health applications, including HIV prevention, maternal and child health, and COVID-19. Previously, he was a Schmidt Science Fellow at Harvard School of Public Health and Carnegie Mellon University. He completed his Ph.D. in computer science at Harvard University. Learn more...
Carolyn Rosé, Professor of Language Technologies and Human Computer Interaction
Dr. Carolyn Rosé serves as the Interim Director of the Language Technologies Institute at Carnegie Mellon and is a leader in the Generative AI Innovation initiative at CMU.
Her 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 both between people, and between people and computers. Her research is grounded in the fields of language technologies and human-computer interaction and invokes approaches from computational discourse analysis and text mining, conversational agents, and computer-supported collaborative learning. Learn more...
Questions? Call 412-268-4686 with your inquiries, send an email to apply@online.cmu.edu, or schedule an appointment with an admissions counselor.
Curriculum Overview
As a student in the Computational Data Science Foundations certificate, you will complete the following graduate-level, credit-bearing courses over three semesters. All completed courses will show on your Carnegie Mellon transcript with the grade earned.
Mathematical Foundations of Machine Learning (6 units; 10-680)
This course offers the necessary mathematical background to understand machine learning. Topics will include probability, linear algebra, and multivariate differential calculus. Students will also learn how to translate these foundational math skills into concrete coding programs.
Computational Foundations for Machine Learning (6 units; 10-681)
This course offers the necessary computational background for studying machine learning. Topics include computational complexity, analysis of algorithms, proof techniques, optimization, dynamic programming, recursion, and data structures.
Python for Data Science (delivered in two parts, 6 units each; 11-604 & 11-605)
This course teaches the concepts, techniques, skills, and tools needed for developing programs in Python. Topics include types, variables, functions, iteration, conditionals, data structures, classes, objects, modules, and I/O operations. This course can be waived for computer science professionals who are already fluent in Python.
Foundations of Computational Data Science (delivered in two parts, 6 units each; 11-671 & 11-672)
This course offers a hands-on introduction to foundational computational data science concepts, including computing systems, analytics, and human-centered data science. Upon completing the coursework, students will have the necessary skills to obtain further graduate education in data science and/or artificial intelligence.
Course Waivers
Students who already have proficiency in either math or programming may waive the following courses upon successful completion of an exemption exam:
- Math Fundamentals of Machine Learning AND Computational Fundamentals of Machine Learning
- Python for Data Science
The exemption exam will be administered to admitted students only. Students who are interested in sitting for the exam should apply to the program and indicate within the application their interest in taking the exam. Once admitted, additional information about sitting for the exam will be provided.
Upon successful completion of one, or both, exemption exams, students will only complete the remaining courses to qualify for the certificate. No credit will be earned, nor will tuition be assessed, for waived courses.
For more information about course waivers, contact an admissions counselor today.