Mathematics for Data Science
Not Started0 / 10 complete
Open a video or resource link, watch it, then check its box to track your progress and unlock what comes next.
10 lessons3 levels1 capstone project
Master the mathematical foundation for data science: vectors, probability, statistics, calculus, optimization and numerical computing.
BASIC
- Vectors, What Even Are They? — 3Blue1Brown (10 min)
Builds the visual intuition for vectors - the basic representation of data in modern analytics and ML. - The Main Ideas Behind Probability Distributions — StatQuest (6 min)
A concise intuition for uncertainty, distributions and the language of statistics. - Gradient Descent, Step-by-Step — StatQuest (24 min)
Connects calculus and optimization to how models actually learn parameters.
INTERMEDIATE
- Bayes Theorem — StatQuest
A deeper treatment of the same skill; use it to move from concept to working patterns. - Eigenvectors and Eigenvalues — 3Blue1Brown
A deeper treatment of the same skill; use it to move from concept to working patterns. - But What Is a Neural Network? — 3Blue1Brown
A deeper treatment of the same skill; use it to move from concept to working patterns.
ADVANCED
- Cross Validation — StatQuest
Extend Mathematics for Data Science into senior/production practice through Statistics & Probability. - ROC and AUC — StatQuest
Extend Mathematics for Data Science into senior/production practice through Statistics & Probability. - Stochastic Gradient Descent — StatQuest
Extend Mathematics for Data Science into senior/production practice through ML Mathematics.