All Topics of Statistics for Data Science

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.

1. Basic Probability Theory
   1. Probability Spaces
   2. Conditional Probability 
   3. Independent and Dependent Variables
2. Random Variables
   1. What are random variables?
   2. Multivariate random variables 
   3. Discrete random variables
   4. Continuous random variables 
   5. Functions of random variables 
   6. Creating random variables 
3. Expectation
   1. Expectation operator 
   2. Mean and Variance 
   3. Covariance
   4. Conditional Expectation 
4. Random Processes
   1. What are random processes?
   2. Mean and autocovariance functions 
   3. Independent identically-distributed sequences
   4. Gaussian process
   5. Random walk
5. Convergence of Random Processes 
   1. Types of convergence
   2. Law of large numbers 
   3. Central limit theorem 
   4. Monte Carlo Simulation
6. Descriptive Statistics 
   1. Histogram
   2. Sample mean and variance 
   3. Order statistics
   4. Sample covariance
7. Frequent Statistics
   1. Independent identically distributed sampling 
   2. Mean square error
   3. Consistency
   4. Confidence Intervals 
   5. Nonparametric model estimation
   6. Parametric model estimation
8. Bayesian Statistics 
   1. Bayesian parametric models
   2. Conjugate prior
   3. Bayesian estimators 
9. Hypothesis Testing 
   1. What is hypothesis testing?
   2. Parametric testing 
   3. Nonparametric testing 
   4. Multiple Testing
10. Linear Regression
    1. Linear Models
    2. Least-square estimation 
    3. 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.