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Mathematics for Data Scientist

Mathematical 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. 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

  • Introduction to Linear Algebra
  • Eigenvalues And Eigenvectors
  • Calculating Eigenvalues and Eigenvectors
  • Eigen decomposition of a Matrix
  • Eigenvectors: What Are They?

Vectors, Matrices & Linear Transformations

  • Vector & Vector Spaces
  • Vectors: The Basics
  • Basis Vector
  • Norm of a vector
  • Identity matrix or operator
  • Determinant of a matrix
  • Column and Null Space
  • Rank of a matrix
  • Transpose of a matrix
  • Inverse of a matrix
  • Least Squares Approximation
  • Linear Transformations
  • Matrices: The Basics
  • Matrix Operations
  • Matrix operations and manipulations
  • Dot product of two vectors
  • Linear independence of vectors

## Multivariable Calculus

  • Critical Points, Maxima and Minima
  • Differentiation
  • Functions and Derivatives
  • Functions: Primer
  • Multivariable Functions
  • Partial Derivatives
  • Taylor Series and Linearization
  • The Hessian
  • The Jacobian
  • Vector-Valued Functions


  • Introduction to probability – probability, events, additive & multiplicative rule
  • Basics of probability – random variables, probability distribution, expected value
  • Joint and Conditional Probability
  • Probability Rules
  • Bayes’ Theorem