Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Probability and Statistics: Complete Course 2026
Bestseller
Highest Rated
Rating: 4.8 out of 5(932 ratings)
6,341 students

Probability and Statistics: Complete Course 2026

Learn the Probability and Statistics You Need to Succeed in Data Science and Business Analytics
Last updated 5/2026
English

What you'll learn

  • Descriptive Statistics
  • Visualizing Data
  • Probability Theory
  • Bayesian Statistics
  • Discrete Distributions (Binomial, Poisson and More)
  • Continuous Distributions (Normal and Others)
  • Hypothesis Tests
  • Regression
  • Type I and Type II Errors
  • Chi-Squared Test

Course content

12 sections116 lectures16h 18m total length
  • Introduction1:46
  • Course Overview2:35

Requirements

  • No pre-requisites for most of the course. One small optional section requires knowledge and calculus, but other than that this is suitable for beginners.

Description

This is course designed to take you from beginner to expert in probability and statistics. It is designed to be practical, hands on and suitable for anyone who wants to use statistics in data science, business analytics or any other field to make better informed decisions.

Videos packed with worked examples and explanations so you never get lost, and every technique covered is implemented in Microsoft Excel so that you can put it to use immediately.

Key concepts taught in the course are:

  • Descriptive Statistics: Averages, measures of spread, correlation and much more.

  • Cleaning Data: Identifying and removing outliers

  • Visualization of Data: All standard techniques for visualizing data, embedded in Excel.

  • Probability: Independent Events, conditional probability and Bayesian statistics.

  • Discrete Distributions: Binomial, Poisson, expectation and variance and approximations.

  • Continuous Distributions: The Normal distribution, the central limit theorem and continuous random variables.

  • Hypothesis Tests: Using binomial, Poisson and normal distributions, T-tests and confidence intervals.

  • Regression: Linear regression analysis, correlation, testing for correlation, non-linear regression models.

  • Quality of Tests: Type I and Type II errors, power and size, p-hacking.

  • Chi-Squared Tests: The chi-squared distribution and how to use it to test for association and goodness of fit.

  • Much, much more!

It requires no prior knowledge, with the exception of 2 optional videos at the end of the continuous distribution chapter, in which knowledge of calculus is required).

Who this course is for:

  • Data Scientists
  • Business Analysts
  • Business Students
  • People studying Statistics
  • Anyone looking to power their decision making with a thorough understanding of statistics.