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Probability & Statistics Courses

Probability and statistics courses explore mathematical concepts for analyzing random events and interpreting data through models and inference. Use tools such as Python, R, Excel and Google Sheets to apply your theoretical knowledge in statistics.
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Recommended for Probability & Statistics beginners

Build your Probability & Statistics skills with interactive courses, curated by real-world experts

Course

Introduction to Statistics in R

4 hr
2.9K
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

Track

Statistician with R

52 hr
29
A statistician collects and analyzes data and helps companies make sense of quantitative data, including spotting trends and making predictions.

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68 results

Course

Introduction to Statistics in Python

IntermediateSkill Level
4 hr
5.8K
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.

Course

Introduction to Statistics

BeginnerSkill Level
4 hr
4.4K
Learn the fundamentals of statistics, including measures of center and spread, probability distributions, and hypothesis testing with no coding involved!

Course

Introduction to Statistics in R

IntermediateSkill Level
4 hr
2.9K
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

Course

Introduction to Regression in R

IntermediateSkill Level
4 hr
2.4K
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.

Course

Hypothesis Testing in Python

IntermediateSkill Level
4 hr
2.4K
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

Course

Sampling in Python

IntermediateSkill Level
4 hr
2.4K
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

Course

Intermediate Regression in R

IntermediateSkill Level
4 hr
1.2K
Learn to perform linear and logistic regression with multiple explanatory variables.

Course

Time Series Analysis in Python

IntermediateSkill Level
4 hr
719
In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
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Related resources on Probability & Statistics

How to Become a Statistician in 2023

Curious about how to become a statistician? Find out what a statistician does, what you need to get started, and what you can expect from this career.
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Joleen Bothma

10 min

An Introduction to Statistical Machine Learning

Discover the powerful fusion of statistics and machine learning. Explore how statistical techniques underpin machine learning models, enabling data-driven decision-making.
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Joanne Xiong

11 min

T-tests in R Tutorial: Learn How to Conduct T-Tests

Determine if there is a significant difference between the means of the two groups using t.test() in R.
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Abid Ali Awan

10 min

Ready to apply your skills?

Projects allow you to apply your knowledge to a wide range of datasets to solve real-world problems in your browser

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Frequently asked questions

How does probability and statistics related to data science?

Probability and statistics are foundational to data science, offering the tools and frameworks necessary for analyzing data, making predictions, and deriving meaningful insights. They enable data scientists to understand patterns, assess uncertainties, and make informed decisions based on data analysis.

Why is it important to develop knowledge in probability and statistics?

Developing knowledge in probability and statistics is crucial for effectively interpreting data and making reliable predictions. This understanding forms the basis for designing experiments, analyzing results, and validating conclusions in various fields, ensuring decisions are data-driven and evidence-based.

What careers can I pursue with knowledge in probability and statistics?

With knowledge in probability and statistics, you can pursue a wide array of careers such as data scientist, market researcher, machine learning engineer, statistical analyst, and risk manager. These roles span various industries including finance, healthcare, technology, and government, where interpreting data and making evidence-based decisions are key.

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