# 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

## Probability

- 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