About the course
Break into the rapidly growing field of data science with Stanford University's Foundations for Data Science professional program.
Comprised of three comprehensive and introductory online courses, this program will teach you the foundational programming and statistics skills you need to kick-start a career in data science—no prior experience necessary.
Learn from Stanford faculty with step-by-step instructions, listen to insights from industry experts, and develop your understanding through case studies and real-world examples. The program includes ungraded programming exercises to help build and practice your skills. No final exam or capstone project is required.
Earning the Certificate
To receive a Certificate of Completion for the Foundations for Data Science program, you must complete all three courses – R, Python, and Statistics.
Time to Complete Certificate
The program’s online courses are self-paced and available on-demand for 90 days after the date of enrollment. This enables you to complete the program at your own pace. The Introduction to Statistics course takes roughly 6-8 hours to complete, and R and Python each take roughly 12-15 hours. Completion times will vary depending on your familiarity with the topic and experience with online learning.
Guenther Walther studied mathematics, economics, and computer science at the University of Karlsruhe in Germany and received his Ph.D. in Statistics from UC Berkeley in 1994. His research has focused on statistical methodology for detection problems, shape-restricted inference, and mixture analy...
Trained in the French school of Data Analysis in Montpellier, Susan Holmes has been working in non parametric multivariate statistics applied to Biology since 1985. She has taught at MIT, Harvard and was an Associate Professor of Biometry at Cornell before moving to Stanford in 1998. She teaches ...
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.