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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

Foundations of Inference in R

IntermediateSkill Level
4 hours
379
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

Course

ARIMA Models in R

BasicSkill Level
4 hours
363
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

Course

Generalized Linear Models in R

IntermediateSkill Level
4 hours
357
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

Course

Experimental Design in R

IntermediateSkill Level
4 hours
321
In this course youll learn about basic experimental design, a crucial part of any data analysis.

Course

Statistical Simulation in Python

IntermediateSkill Level
4 hours
305
Learn to solve increasingly complex problems using simulations to generate and analyze data.

Course

Analyzing Survey Data in R

IntermediateSkill Level
4 hours
283
Learn survey design using common design structures followed by visualizing and analyzing survey results.

Course

Foundations of Probability in Python

IntermediateSkill Level
5 hours
278
Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

Course

Error and Uncertainty in Google Sheets

IntermediateSkill Level
4 hours
255
Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.

Course

Survival Analysis in R

IntermediateSkill Level
4 hours
249
Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!

Course

Foundations of Inference in Python

AdvancedSkill Level
4 hours
246
Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.

Course

Advanced Probability: Uncertainty in Data

AdvancedSkill Level
2 hours
235
Develop a better intuition for advanced probability, risk assessment, and simulation techniques to make data-driven business decisions with confidence.

Course

A/B Testing in R

IntermediateSkill Level
4 hours
227
Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.

Course

Survival Analysis in Python

AdvancedSkill Level
4 hours
215
Use survival analysis to work with time-to-event data and predict survival time.

Course

Factor Analysis in R

AdvancedSkill Level
4 hours
215
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.

Course

Network Analysis in R

IntermediateSkill Level
4 hours
177
Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.

Course

Case Studies in Statistical Thinking

IntermediateSkill Level
4 hours
148
Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.

Course

Discrete Event Simulation in Python

AdvancedSkill Level
4 hours
142
Discover the power of discrete-event simulation in optimizing your business processes. Learn to develop digital twins using Pythons SimPy package.

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An Introduction to Statistical Machine Learning

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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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