Get Ready for Data Science at Stanford
Hoping to study data science at Stanford? DataCamp can help you build your data skills across a range of disciplines.
- Learn at your own pace
- Get hands-on experience
- Practice and apply your skills
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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.Popular Courses for Stanford Students
Introduction to Data Science in Python
Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.
Hillary Green-Lerman
Lead Data Scientist, Looker
Supervised Learning with scikit-learn
Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!
George Boorman
Core Curriculum Manager, DataCamp
Introduction to Statistics
Learn how to explore, visualize, and extract insights from data.
George Boorman
Core Curriculum Manager, DataCamp
Introduction to Data Visualization with Matplotlib
Learn how to create, customize, and share data visualizations using Matplotlib.
Ariel Rokem
Senior Data Scientist, University of Washington
Introduction to Linear Modeling in Python
Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.
Jason Vestuto
Data Scientist, University of Texas at Austin
Machine Learning for Everyone
An introduction to machine learning with no coding involved.
Hadrien Lacroix
Curriculum Manager at DataCamp
Prepare to Study Data Science at Stanford
Though there are no explicit requirements for undergraduate (first-year) applicants interested in majoring in data science, a “rigorous” mathematical understanding of fundamental skills (algebra, trigonometry, and geometry) along with additional mathematical preparation in calculus and statistics are recommended.
For graduate applicants, the Stanford Department of Statistics recommends the following preparatory courses (independent learning/online or certificate program/community college, etc.) for their Data Science program:
- Linear algebra
- Probability
- Stochastic processes
- Numeric method
- Proficiency in programming
DataCamp is an excellent resource for those looking to develop the skills recommended by Stanford University Data Science programs. With over 300 courses, we provide interactive online courses in different programming languages, including Python, R, Tableau, SQL, and Power BI, plus data analysis, data visualization, machine learning, and data engineering courses.
Practice your Data Skills
DataCamp is useful for everyone, no matter their programming proficiency. For those just starting their data literacy journey, we have non-coding courses to explain the fundamentals of data science. And for those looking forward to developing their programming skills, we have intermediate courses for each programming language we offer.
Moreover, our other resources, such as tutorials, blogs, cheat sheets, and free datasets, are available to our learners to help assist with their studies at Stanford.
Work with Real Datasets
DataCamp understands the importance of learning by doing. With that in mind, our courses provide real-life datasets to practice on. Similarly, with DataCamp Projects, you can apply your coding skills to solve real-world problems in your browser. Users are also able to integrate their own dataset into DataCamp Projects, making it the perfect place to practice course work or prepare for your studies at Stanford.
Gain Sharable Statements of Accomplishments
Every course you complete on DataCamp is rewarded with a shareable Statement of Accomplishment. These can be shared with your network or as part of your Stanford data science application to show your progress in various subject areas.
Compile your own Portfolio
When you’re working on your own Stanford data science projects, you can save your projects into the DataCamp Workspace. Workspace can serve as a portfolio for showcasing your data science skills both during the application process and as you progress through your course.
Not only are you able to save your existing projects, but Workspace allows for collaborative environments. You are able to share and work together with your friends or fellow students on various projects, in a safe, trustworthy learning environment.
