HomeProbability & Statistics

# 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.

Recommended for Probability & Statistics beginners

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

course

### .css-1531qan{-webkit-text-decoration:none;text-decoration:none;color:inherit;}Introduction to Statistics in R

IntermediateSkill Level
4 hours
2.6K
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

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### Statistician with R

52 hours
32
A statistician collects and analyzes data and helps companies make sense of quantitative data, including spotting trends and making predictions.

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74 results

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### Introduction to Statistics in Python

IntermediateSkill Level
4 hours
5.9K
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.

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### Introduction to Statistics

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

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### Introduction to Statistics in R

IntermediateSkill Level
4 hours
2.6K
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

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### Hypothesis Testing in Python

IntermediateSkill Level
4 hours
2.9K
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

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### Introduction to Regression with statsmodels in Python

IntermediateSkill Level
4 hours
2.4K
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.

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### Sampling in Python

IntermediateSkill Level
4 hours
2.8K
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

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IntermediateSkill Level
4 hours
1.5K
Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!

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### Introduction to Regression in R

IntermediateSkill Level
4 hours
1.3K
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.

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### Time Series Analysis in Python

IntermediateSkill Level
4 hours
498
In this four-hour course, you’ll learn the basics of analyzing time series data in Python.

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### Hypothesis Testing in R

IntermediateSkill Level
4 hours
793
Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.

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### Statistical Thinking in Python (Part 1)

IntermediateSkill Level
3 hours
588
Build the foundation you need to think statistically and to speak the language of your data.

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### Intermediate Regression in R

IntermediateSkill Level
4 hours
729
Learn to perform linear and logistic regression with multiple explanatory variables.

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### A/B Testing in Python

IntermediateSkill Level
4 hours
237
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.

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### Sampling in R

IntermediateSkill Level
4 hours
759
Master sampling to get more accurate statistics with less data.

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### Introduction to Statistics in Google Sheets

BeginnerSkill Level
4 hours
478
Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.

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### RNA-Seq with Bioconductor in R

IntermediateSkill Level
4 hours
142
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.

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### Statistical Techniques in Tableau

IntermediateSkill Level
4 hours
405
Take your reporting skills to the next level with Tableau’s built-in statistical functions.

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### Statistical Thinking in Python (Part 2)

IntermediateSkill Level
4 hours
483
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

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### Linear Algebra for Data Science in R

IntermediateSkill Level
4 hours
122
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.

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### Intermediate Regression with statsmodels in Python

IntermediateSkill Level
4 hours
460
Learn to perform linear and logistic regression with multiple explanatory variables.

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### Bayesian Data Analysis in Python

IntermediateSkill Level
4 hours
191
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!

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### Introduction to Bioconductor in R

IntermediateSkill Level
4 hours
173
Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!

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### Foundations of Probability in R

BeginnerSkill Level
4 hours
400
In this course, youll learn about the concepts of random variables, distributions, and conditioning.

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### Anomaly Detection in Python

IntermediateSkill Level
4 hours
138
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.

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### Time Series Analysis in R

IntermediateSkill Level
4 hours
189
Learn the core techniques necessary to extract meaningful insights from time series data.

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### Foundations of Probability in Python

IntermediateSkill Level
5 hours
320
Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

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### Modeling with Data in the Tidyverse

IntermediateSkill Level
4 hours
324
Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.

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### Forecasting in R

IntermediateSkill Level
5 hours
146
Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.

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### Monte Carlo Simulations in Python

IntermediateSkill Level
4 hours
73
Learn to design and run your own Monte Carlo simulations using Python!

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### Customer Analytics and A/B Testing in Python

IntermediateSkill Level
4 hours
112
Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.

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### Practicing Statistics Interview Questions in Python

4 hours
43
Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.

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### Fundamentals of Bayesian Data Analysis in R

IntermediateSkill Level
4 hours
132
Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

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### Analyzing Survey Data in R

IntermediateSkill Level
4 hours
172
Learn survey design using common design structures followed by visualizing and analyzing survey results.

course

IntermediateSkill Level
4 hours
218
In this course youll learn about basic experimental design, a crucial part of any data analysis.

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### Introduction to Linear Modeling in Python

IntermediateSkill Level
4 hours
170
Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.

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### Generalized Linear Models in R

IntermediateSkill Level
4 hours
179
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

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### Hierarchical and Mixed Effects Models in R

4 hours
104
In this course you will learn to fit hierarchical models with random effects.

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### Foundations of Inference in R

IntermediateSkill Level
4 hours
161
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

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### Introduction to Network Analysis in Python

IntermediateSkill Level
4 hours
67
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.

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### Foundations of Inference in Python

4 hours
203
Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.

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### ARIMA Models in R

IntermediateSkill Level
4 hours
151
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

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### Practicing Statistics Interview Questions in R

4 hours
18
In this course, youll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.

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### Statistical Simulation in Python

IntermediateSkill Level
4 hours
171
Learn to solve increasingly complex problems using simulations to generate and analyze data.

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### Survival Analysis in R

IntermediateSkill Level
4 hours
113
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!

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### Generalized Linear Models in Python

5 hours
55
Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.

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### Factor Analysis in R

4 hours
78
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.

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### Survival Analysis in Python

4 hours
39
Use survival analysis to work with time-to-event data and predict survival time.

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### Structural Equation Modeling with lavaan in R

4 hours
38
Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.

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### Performing Experiments in Python

IntermediateSkill Level
4 hours
78
Learn about experimental design, and how to explore your data to ask and answer meaningful questions.

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### Network Analysis in R

IntermediateSkill Level
4 hours
41
Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.

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### Discrete Event Simulation in Python

4 hours
30
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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### Analyzing Survey Data in Python

IntermediateSkill Level
4 hours
69
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.

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### Inference for Linear Regression in R

4 hours
117
In this course youll learn how to perform inference using linear models.

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### Nonlinear Modeling with Generalized Additive Models (GAMs) in R

4 hours
41
GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.

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### ChIP-seq with Bioconductor in R

IntermediateSkill Level
4 hours
54
Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.

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### Differential Expression Analysis with limma in R

4 hours
73
Learn to use the Bioconductor package limma for differential gene expression analysis.

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### Inference for Categorical Data in R

4 hours
80
In this course youll learn how to leverage statistical techniques for working with categorical data.

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### A/B Testing in R

IntermediateSkill Level
4 hours
23
Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.

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### Case Study: Analyzing City Time Series Data in R

IntermediateSkill Level
4 hours
90
Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.

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### Inference for Numerical Data in R

4 hours
95
In this course youll learn techniques for performing statistical inference on numerical data.

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### Bayesian Regression Modeling with rstanarm

4 hours
51
Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.

course

### Error and Uncertainty in Google Sheets

IntermediateSkill Level
4 hours
75
Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.

course

### Case Studies in Statistical Thinking

IntermediateSkill Level
4 hours
33
Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.

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### Multivariate Probability Distributions in R

IntermediateSkill Level
4 hours
22
Learn to analyze, plot, and model multivariate data.

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### Bayesian Modeling with RJAGS

4 hours
27
In this course, youll learn how to implement more advanced Bayesian models using RJAGS.

course

### Intermediate Network Analysis in Python

4 hours
30
Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.

course

### Forecasting Product Demand in R

IntermediateSkill Level
4 hours
13
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

### Probability Puzzles in R

IntermediateSkill Level
4 hours
25
Learn strategies for answering probability questions in R by solving a variety of probability puzzles.

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### Choice Modeling for Marketing in R

4 hours
23
Learn to analyze and model customer choice data in R.

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### Introduction to Anomaly Detection in R

IntermediateSkill Level
4 hours
24
Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.

course

### Mixture Models in R

IntermediateSkill Level
4 hours
8
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.

course

### Predictive Analytics using Networked Data in R

IntermediateSkill Level
4 hours
15
Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network

course

### Building Response Models in R

4 hours
16
Learn to build simple models of market response to increase the effectiveness of your marketing plans.

course

### Case Studies: Network Analysis in R

BeginnerSkill Level
4 hours
13
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
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## Related resources on Probability & Statistics

blog

### How to Become a Statistician in 2024

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

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.

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.

Abid Ali Awan

10 min

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

project

### Hypothesis Testing with Men's and Women's Soccer Matches

1 hour
1.9K
Perform a hypothesis test to determine if more goals are scored in women's soccer matches than men's!

project

### Hypothesis Testing in Healthcare

1 hour
2.6K
Perform hypothesis tests to determine if the adverse effects of a pharmaceutical drug are significant!
See More

## Frequently asked questions

### How does probability and statistics related to data science?.css-1kmjmu4{color:#7933FF;-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0;margin-left:16px;margin-top:6px;-webkit-transform:rotate(45deg);-moz-transform:rotate(45deg);-ms-transform:rotate(45deg);transform:rotate(45deg);-webkit-transition:-webkit-transform 0.3s cubic-bezier(0.85, 0, 0.15, 1);transition:transform 0.3s cubic-bezier(0.85, 0, 0.15, 1);}

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?.css-10011sa{color:#7933FF;-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0;margin-left:16px;margin-top:6px;-webkit-transform:none;-moz-transform:none;-ms-transform:none;transform:none;-webkit-transition:-webkit-transform 0.3s cubic-bezier(0.85, 0, 0.15, 1);transition:transform 0.3s cubic-bezier(0.85, 0, 0.15, 1);}

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.