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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 hours
5K
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

Track

Statistician in R

52 hours
966
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

Practicing Statistics Interview Questions in R

AdvancedSkill Level
4 hours
120
In this course, youll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.

Course

Analyzing Survey Data in Python

IntermediateSkill Level
4 hours
108
Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.

Course

ChIP-seq with Bioconductor in R

IntermediateSkill Level
4 hours
102
Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.

Course

Bayesian Modeling with RJAGS

AdvancedSkill Level
4 hours
85
In this course, youll learn how to implement more advanced Bayesian models using RJAGS.

Course

Building Response Models in R

IntermediateSkill Level
4 hours
66
Learn to build simple models of market response to increase the effectiveness of your marketing plans.

Course

Forecasting Product Demand in R

IntermediateSkill Level
4 hours
56
Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.

Course

Mixture Models in R

IntermediateSkill Level
4 hours
53
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.

Course

Probability Puzzles in R

BasicSkill Level
4 hours
49
Learn strategies for answering probability questions in R by solving a variety of probability puzzles.

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.
Joleen Bothma's photo

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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