Data science isn’t just for people who know how to code, it’s primarily for those who can analyze a business’s performance and find the information needed to solve business problems. To understand a company’s performance, **statistics** is one of the most important concepts every **data scientist** should know. So in this article, I will take you through all the topics of statistics that you should learn for data science.

## All Topics of Statistics for Data Science

Statistics mean the collection and analysis of numerical data to find the information and patterns necessary to understand the behaviour of a specific population. There are some very important concepts in statistics that you must learn if you want to become a data scientist. So here are all the topics of statistics for data science that you should learn.

- Basic Probability Theory
- Probability Spaces
- Conditional Probability
- Independent and Dependent Variables

- Random Variables
- What are random variables?
- Multivariate random variables
- Discrete random variables
- Continuous random variables
- Functions of random variables
- Creating random variables

- Expectation
- Expectation operator
- Mean and Variance
- Covariance
- Conditional Expectation

- Random Processes
- What are random processes?
- Mean and autocovariance functions
- Independent identically-distributed sequences
- Gaussian process
- Random walk

- Convergence of Random Processes
- Types of convergence
- Law of large numbers
- Central limit theorem
- Monte Carlo Simulation

- Descriptive Statistics
- Histogram
- Sample mean and variance
- Order statistics
- Sample covariance

- Frequent Statistics
- Independent identically distributed sampling
- Mean square error
- Consistency
- Confidence Intervals
- Nonparametric model estimation
- Parametric model estimation

- Bayesian Statistics
- Bayesian parametric models
- Conjugate prior
- Bayesian estimators

- Hypothesis Testing
- What is hypothesis testing?
- Parametric testing
- Nonparametric testing
- Multiple Testing

- Linear Regression
- Linear Models
- Least-square estimation
- Underfitting and Overfitting

### Summary

So these were all the important concepts of statistics that you should learn for data science. To understand a company’s performance, statistics is one of the most important concepts every data scientist should know. I hope you liked this article on all the topics of statistics you should learn for data science. Feel free to ask your valuable questions in the comments section below.