Foundations of Data Science
Online Graduate Certificate
Data Literacy Defines Career Success.
The world has changed. As data becomes the language of work, those who don't speak it are left out of the conversation.
That's why understanding data fundamentals is no longer optional, no matter your industry or role. If you want to keep up, you must build your own data fluency.
The coursework was directly applicable to my day-to-day work in UX and AI design. From building regression models to analyzing large datasets, I could immediately translate what I learned into actionable insights. "
Jen Kelleman
Principal Content Designer & UX Content Strategist
Microsoft
Who is this Program For?
Professionals and recent graduates who want to build a data-driven mindset, collaborate effectively with data teams, and use data to make smarter, more efficient decisions - whether advancing in their current role or preparing for graduate study in data science.
What You Will Learn
In less than a year, you’ll be confident using data to make better decisions in your organization – not just collecting numbers, but understanding what they mean and how to act on them.
The CMU Difference
All online certificate programs are not the same. Our online learning experiences use evidence-based learning science to support learning, retention, and real-world application. All taught by CMU's world class faculty in live-online classes.
Curriculum Highlights
Transition from knowing data matters to using it confidently - no technical background required. Coursework is carefully designed to teach you how to:
- Interpret data in Statistics & Statistical Modeling
- Analyze information in Computing Workflows
- Communicate insights in Data Visualization
- Apply learnings to real-world datasets in the Capstone
Together, these courses provide a foundation in the core skills of data science - equipping you to thoughtfully engage with data and use it to make informed recommendations and decisions.Ìý
Course Descriptions
Learn how to interpret, understand and correctly apply fundamental terminology and techniques in future data analysis situations. Explore the theoretical aspects of probability and statistical inference, including basic probability, random variables, univariate and bivariate probability distributions, statistics, likelihood, point and interval estimation, hypothesis testing, and the frameworks underlying linear and logistic regression and Naive Bayes. Mathematical details are supplemented with computer-based examples and exercises (e.g. visualization and simulation).
This course is taught in a 7-week mini semester (half a semester).
Designed to teach you how to approach, analyze and interpret data, topics include data input/output, processing, exploratory analysis, clustering, common regression and classification models (including those of classical statistics and of machine learning), and experimental design. Practice using these methods on real-world data and subsequently apply them when analyzing data in the program's Data Science Capstone course.
Prerequisite: Probability & Statistics for Data Science
This course is taught in a 7-week mini semester (half a semester).
Explore the most common forms of graphical displays and their (mis)uses. Learn how to create well-designed graphs and understand them from a statistical perspective, while working with increasingly common, complex data structures (temporal, spatial, and text data). All assignments will be completed in R. Throughout the course, communication skills will play an important role.
This course is taught in a 7-week mini semester (half a semester).
Learn how to apply computational thinking to data processing and analysis problems through R programming language. Topics include defining and manipulating vectors, lists, and data frames; processing strings and applying regular expressions in string searches; input and output data; writing functions; applying numerical methods such as integration and optimization; working with date-and-time-based data; and applying unit testing.
This course is taught in a 7-week mini semester (half a semester).
In the capstone course, work with real-world data to apply the skills and knowledge acquired throughout the program. Supported by subject matter experts, you will have the opportunity to practice synthesizing and communicating results in a clear and concise manner.
This course is taught in a 7-week mini semester (half a semester).
Quality Online Learning for Working Professionals
Building data fluency is analytical, iterative and demanding — and it requires a learning environment designed to support both rigor and flexibility.
Rigor — Expect a rigorous learning experience with the same high academic standards as our on-campus offerings. It won’t be easy, but it will be worth it.
Flexibility — Complete the program in less than a year through a combination of live online classes and self-paced activities that fit your schedule.
Live, online classes meet weekly with CMU faculty after work hours for interactive discussion, problem solving and collaborative learning.
Self-paced activities - readings, short lectures and applied exercises allow you to master concepts on your own timeline with ongoing faculty support.Ìý
World Class Faculty
From the Department of Statistics and Decision Sciences
Dr. Rebecca Nugent
Associate Teaching Professor
Ph.D., University of Washington
Research Focus: Clustering, record linkage, educational data mining/psychometrics, public health, tech/innovation/entrepreneurship and semantic organization.
Dr. Peter Freeman
Associate Teaching Professor
Ph.D., University of Chicago
Research Focus: Astrostatistics
Dr. Alex Reinhart
Assistant Teaching Professor
Ph.D., ºÃÉ«ÏÈÉúTV
Research Focus: Natural Language Processing & LLM's
Dr. Zach Branson
Associate Teaching Professor
Ph.D., Harvard University
Research Focus: Causal Inference & Statistical Machine Learning
Julia Walchessen
Special Faculty
Post Doctoral Fellow, ºÃÉ«ÏÈÉúTV
Research Focus: Statistical Machine Learning
Application Requirements
Designed for any professional, from the seasoned programmer in financial services to a non-technical manager in human resources. Successful applicants have:
- A bachelor’s degree from an accredited college or university in any subject
- Basic knowledge of statistics and calculus
- Exposure to programming languages (Python, R, Java or MATLAB)
- A forward-thinking mindset - we find that success comes from a drive to learn and apply new skills immediately
We encourage you to apply even if you don't have a background in math or programming. However, we may require or recommend preparatory work to ensure your academic success.
Ready to Build Your Future?
Tuition
We know that a graduate-level certificate represents a significant investment of both time and money. But we also know the impact of investing in your own personal growth.Ìý
See below of a full breakdown of tuition and more details on payment options.Ìý
A limited number of partial scholarships are still available. Apply by the final deadline to be considered for these awards.
You will be notified of your award amount in your admission letter. Ìý
| Term | Course | Investment |
|---|---|---|
| Spring 2026 | Introduction to Data Science Computing Workflows | $4,242 |
| Spring 2026 | Telling Impactful Stories with Data Visualization | $4,242 |
| Summer 2026 | Probability & Statistics for Data Science | $4,242 |
| Summer 2026 | Gaining Insights Through Statistical Modeling | $4,242 |
| Fall 2026 | Application of Real-World Data Science: A Capstone Experience | $4,242 |
| Total Investment | $21,210 |
Financing Your Future
To help make the financial commitment more manageable, we offer a limited number of scholarships and flexible monthly payment plans. Students also use employer tuition reimbursement benefits, and the G.I. Bill to cover tuition costs. See below for more details on ways to make an investment in your future a reality.
Additional Fees & NotesÌý
- A $240 technology fee will be assessed each semester (subject to change).Ìý
- Tuition rates are for the current academic year only. If the certificate is not completed within that time frame, tuition may increase slightly for the following academic year.
Funding Information and Resources
All applications received by the priority deadline are eligible for a partial scholarship award; those received later may be eligible if funds are still available. You will be notified at the time of admission of any awards. Scholarships are applied by course and are non-transferrable between courses or semesters.
The majority of our students use tuition reimbursement benefits from their company. While some policies won't cover certificate programs, since this certificate is credit-bearing and a verifiable credential, many organizations will allow tuition benefits.
And remember, fall enrollment will maximize benefits since most benefit plans are based on calendar year. Enroll in Fall 2026 and you will use both your 2026 and 2027 benefits to cover the program cost.Ìý
If your employer is uncertain about providing financial support, or if you need specific documents to proceed with enrollment, contact a Program Specialist who will help highlight the value and benefits of completing an online certificate at Carnegie Mellon. Visit this webpage to see examples of how employer tuition reimbursement can be structured throughout the semester.Ìý
A monthly payment option is available to break tuition into manageable installments. Managed by Nelnet, students can enroll online.
Visit this webpage to explore available payment options and see examples of how tuition can be structured throughout the semester.
ºÃÉ«ÏÈÉúTV provides services to veterans and their dependents who are eligible for Veterans Education Benefits under the Montgomery G.I. Bill®, Post-9/11 G.I. Bill, and the Vocational Rehabilitation and Employment Program. Please note that our online graduate certificates are not currently eligible for the Yellow Ribbon program.
The process begins with an application directly to Veterans Affairs. Once approved, you will provide your Certificate of Eligibility to the Carnegie Mellon Veterans Affairs Coordinator. Contact information and additional details about the process can be found here.Ìý
Students eligible for GI Bill funding may receive scholarship awards prior to full GI Bill funding confirmation. Scholarship awards will be adjusted to reflect GI Bill funding and cannot exceed the cost of tuition and fees.
All CMU Online graduate certificates are eligible for CMU tuition remission. Review theÌýCMU tuition remission policyÌýto check your eligibility.
Students pursuing a graduate certificate are not eligible to receive federal financial aid. However, private loans are a viable alternative to consider, offering competitive interest rates and borrower benefits. See , a free loan comparison service to easily research options.
Start Your Application
Ready to Apply? Here's what you'll need to complete the application process for the Foundations of Data Science Online Graduate Certificate.
Complete the Online Application
Submit your application via the .
Submit Your Resume/CVÌý
Tell us more about your employment history, academic background, technical skills and professional achievements.
Submit Your TranscriptsÌý
Upload unofficial copies from schools where a degree was earned or significant coursework was taken. Transcripts must include:
- Your name
- College or university name
- The degree awarded (along with the conferral date)
- All courses taken and grades earned
Upload a Statement of PurposeÌý
In 500 words or less, tell us why you are interested in this certificate program and how you anticipate using it in your professional capacity.
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CMU Dept. of Statistics & Data Science
By the Numbers
# 5
Nationally for Graduate Statistics Programs
U.S. News & World Report
# 2
Nationally for Data Science & AI Programs
QS World University by Subject, 2025
21
Research Areas
and lab groups dedicated to real-world applications of statistics research