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