athematics for Data Scientist

To excel in the field of data science, especially as a data scientist, I would recommend you have good command over the topics mentioned below. These are the topics from mathematics and statistics. There are many YouTube channels that you can use for this purpose. Because this is 10+2 level mathematics, and it is just a matter of revision. So I am not offering any course unless there is a specific need for some group, organization.

Linear Algebra

  1. Introduction to Linear Algebra
  2. Eigenvalues And Eigenvectors
  3. Calculating Eigenvalues and Eigenvectors
  4. Eigen decomposition of a Matrix
  5. Eigenvectors: What Are They? Intuition behind.

Vectors, Matrices & Linear Transformations

** Vector & Vector Spaces **

  1. Vectors: The Basics
  2. Basis Vector
  3. Norm of a vector
  4. Identity matrix or operator
  5. Determinant of a matrix
  6. Column and Null Space
  7. Rank of a matrix
  8. Transpose of a matrix
  9. Inverse of a matrix
  10. Least Squares Approximation
  11. Linear Transformations
  12. Matrices: The Basics
  13. Matrix Operations
  14. Matrix operations and manipulations
  15. Dot product of two vectors
  16. Linear independence of vectors


Multivariable Calculus

  1. Critical Points, Maxima and Minima
  2. Differentiation
  3. Functions and Derivatives
  4. Functions: Primer
  5. Multivariable Functions
  6. Partial Derivatives
  7. Taylor Series and Linearization
  8. The Hessian
  9. The Jacobian
  10. Vector-Valued Functions


  1. Introduction to probability – probability, events, additive & multiplicative rule
  2. Basics of probability – random variables, probability distribution, expected value
  3. Joint and Conditional Probability
  4. Probability Rules
  5. Bayes’ Theorem


  1. Descriptive statistics
  2. Inferential Statistics
  3. Prescriptive statistics
  4. What is sampling, different sampling techniques?
  5. Random Variable, Predictor, Predicted variables
  6. Data Distribution (continuous, discrete, Normal/Bernoulli, standard, binomial, Poisson, etc.)
  7. CDF (Cumulative Distribution Function), PDF (Probability Distribution Function)
  8. Statistical Measures (mean, mode, median, max, min)
  9. Measure of dispersion (range, standard deviation, variance, covariance, correlation, error deviation)
  10. Central Limit Theorem (CLT)
  11. What is Regression? How it works? OLS (Ordinary Least Square), Multi-linear regression.
  12. Standard Error
  13. Dimensionality Reduction (PCA)
  14. Parameter Properties (Bias, Consistency, Efficiency)
  15. Statistical tests t-test, z-test, ANOVA test, Chi-Square test
  16. Conditional Probability (Bayesian Theorem)
  17. Type I/Type II errors
  18. Hypothesis testing
  19. Confidence Interval & Significance Level (alpha)
  20. p-value and its interpretation