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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

IntermediateSkill Level
4.7+
4.5K
4 hours
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

Track

Statistician in R

931
52 hours
A statistician collects and analyzes data and helps companies make sense of quantitative data, including spotting trends and making predictions.

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Browse Probability & Statistics courses and tracks

Course

Introduction to Statistics

BasicSkill Level
4.8+
9.7K
4 hours
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 Python

IntermediateSkill Level
4.7+
8.5K
4 hours
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.

Course

Introduction to Statistics in R

IntermediateSkill Level
4.7+
4.5K
4 hours
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

Course

Introduction to Regression in R

IntermediateSkill Level
4.8+
3.9K
4 hours
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.

Course

Hypothesis Testing in Python

IntermediateSkill Level
4.7+
2.4K
4 hours
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
4.7+
2.2K
4 hours
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

Course

Hypothesis Testing in R

IntermediateSkill Level
4.7+
1.8K
4 hours
Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.

Course

Intermediate Regression in R

IntermediateSkill Level
4.7+
1.5K
4 hours
Learn to perform linear and logistic regression with multiple explanatory variables.

Course

Time Series Analysis in Python

IntermediateSkill Level
4.8+
1.2K
4 hours
In this four-hour course, you’ll learn the basics of analyzing time series data in Python.

Course

Experimental Design in Python

IntermediateSkill Level
4.7+
1.1K
4 hours
Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!

Course

Sampling in R

IntermediateSkill Level
4.7+
987
4 hours
Master sampling to get more accurate statistics with less data.

Course

Time Series Analysis in R

IntermediateSkill Level
4.8+
940
4 hours
Learn the core techniques necessary to extract meaningful insights from time series data.

Course

ARIMA Models in R

BasicSkill Level
4.8+
857
4 hours
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

Course

Forecasting in R

BasicSkill Level
4.8+
832
5 hours
Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.

Course

A/B Testing in Python

IntermediateSkill Level
4.7+
786
4 hours
Learn the practical uses of A/B testing in Python to run and analyze experiments. Master p-values, sanity checks, and analysis to guide business decisions.

Course

Modeling with Data in the Tidyverse

IntermediateSkill Level
4.8+
710
4 hours
Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.

Course

Foundations of Probability in R

BasicSkill Level
4.8+
665
4 hours
In this course, youll learn about the concepts of random variables, distributions, and conditioning.

Course

Linear Algebra for Data Science in R

IntermediateSkill Level
4.7+
644
4 hours
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.

Course

Introduction to Bioconductor in R

IntermediateSkill Level
4.8+
552
4 hours
Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!

Course

Statistical Techniques in Tableau

IntermediateSkill Level
4.8+
515
4 hours
Take your reporting skills to the next level with Tableau’s built-in statistical functions.

Course

Generalized Linear Models in R

IntermediateSkill Level
4.8+
515
4 hours
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

Course

Foundations of Inference in R

IntermediateSkill Level
4.7+
505
4 hours
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

Course

Bayesian Data Analysis in Python

IntermediateSkill Level
4.7+
499
4 hours
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!

Course

Introduction to Linear Modeling in Python

IntermediateSkill Level
4.7+
490
4 hours
Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.

Course

RNA-Seq with Bioconductor in R

IntermediateSkill Level
4.7+
480
4 hours
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.

Related resources on Probability & Statistics

blog

How to Become a Statistician in 2026

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

Tutorial

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

Tutorial

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

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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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