Data Science Math Skills
About this course
Data Science Math Skills is Duke's accessible introduction to the mathematical concepts data science builds on — set theory, real line and interval notation, counting and combinatorics, probability rules, Bayes' theorem, and random variables. It's designed for learners who want to start a data science learning path but feel uncertain about their mathematical foundation.
The course doesn't require calculus or linear algebra — it fills the gap that sits below MIT's graduate statistics course and above high school math: the specific probability and reasoning foundations that make data science tools interpretable. It's the right starting point before Statistics for Data Science courses, used by over 580,000 learners who found the jump from general math to data science too steep.
What you'll learn
This course includes
Compare alternatives for Data Science Math Skills
- Price
- PaidFree to audit, paid certificate
- Duration
- 20 hrs
- Level
- Beginner
- Certificate
- Course Certificate
- Price
- FreeCompletely free, openly licensed — no certificate
- Duration
- 34 hrs
- Level
- Intermediate
- Certificate
- Price
- FreeFree lecture materials; some versions paid
- Duration
- 50 hrs
- Level
- Advanced
- Certificate
- Price
- FreeFree lecture materials; some versions paid
- Duration
- 50 hrs
- Level
- Advanced
- Certificate
Instructor
Taught by Daniel Egger and Paul Bendich, Duke University mathematics faculty, designed specifically for learners approaching data science from non-mathematical backgrounds.
Requirements
- High school algebra; no calculus required
Who this course is for
- Beginners starting a data science learning path who want the math foundation
- Non-technical professionals entering data science who feel rusty on math
- Anyone who struggled with probability in statistics courses and wants a cleaner foundation