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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

पाठ्यक्रम

Introduction to Statistics in R

मध्यमकौशल स्तर
4.7+
4.5K
4 घंटे
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

ट्रैक

Statistician in R

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

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मूल्यांकन लें

Probability & Statistics पाठ्यक्रम और ट्रैक ब्राउज़ करें

पाठ्यक्रम

Introduction to Statistics

बुनियादीकौशल स्तर
4.8+
9.7K
4 घंटे
Learn the fundamentals of statistics, including measures of center and spread, probability distributions, and hypothesis testing with no coding involved!

पाठ्यक्रम

Introduction to Statistics in Python

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

पाठ्यक्रम

Introduction to Statistics in R

मध्यमकौशल स्तर
4.7+
4.5K
4 घंटे
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

पाठ्यक्रम

Introduction to Regression in R

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

पाठ्यक्रम

Introduction to Regression with statsmodels in Python

मध्यमकौशल स्तर
4.8+
2.4K
4 घंटे
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.

पाठ्यक्रम

Hypothesis Testing in Python

मध्यमकौशल स्तर
4.7+
2.4K
4 घंटे
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

पाठ्यक्रम

Sampling in Python

मध्यमकौशल स्तर
4.7+
2.2K
4 घंटे
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

पाठ्यक्रम

Hypothesis Testing in R

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

पाठ्यक्रम

Intermediate Regression in R

मध्यमकौशल स्तर
4.7+
1.5K
4 घंटे
Learn to perform linear and logistic regression with multiple explanatory variables.

पाठ्यक्रम

Time Series Analysis in Python

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

पाठ्यक्रम

Experimental Design in Python

मध्यमकौशल स्तर
4.7+
1.1K
4 घंटे
Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!

पाठ्यक्रम

Sampling in R

मध्यमकौशल स्तर
4.7+
987
4 घंटे
Master sampling to get more accurate statistics with less data.

पाठ्यक्रम

Introduction to Statistics in Google Sheets

बुनियादीकौशल स्तर
4.8+
960
4 घंटे
Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.

पाठ्यक्रम

Time Series Analysis in R

मध्यमकौशल स्तर
4.8+
940
4 घंटे
Learn the core techniques necessary to extract meaningful insights from time series data.

पाठ्यक्रम

ARIMA Models in R

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

पाठ्यक्रम

Forecasting in R

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

पाठ्यक्रम

A/B Testing in Python

मध्यमकौशल स्तर
4.7+
786
4 घंटे
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.

पाठ्यक्रम

Modeling with Data in the Tidyverse

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

पाठ्यक्रम

Statistical Thinking in Python (Part 1)

मध्यमकौशल स्तर
4.8+
686
3 घंटे
Build the foundation you need to think statistically and to speak the language of your data.

पाठ्यक्रम

Foundations of Probability in R

बुनियादीकौशल स्तर
4.8+
665
4 घंटे
In this course, youll learn about the concepts of random variables, distributions, and conditioning.

पाठ्यक्रम

Linear Algebra for Data Science in R

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

पाठ्यक्रम

Introduction to Bioconductor in R

मध्यमकौशल स्तर
4.8+
552
4 घंटे
Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!

पाठ्यक्रम

Statistical Techniques in Tableau

मध्यमकौशल स्तर
4.8+
515
4 घंटे
Take your reporting skills to the next level with Tableau’s built-in statistical functions.

पाठ्यक्रम

Generalized Linear Models in R

मध्यमकौशल स्तर
4.8+
515
4 घंटे
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

पाठ्यक्रम

Foundations of Inference in R

मध्यमकौशल स्तर
4.7+
505
4 घंटे
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

पाठ्यक्रम

Bayesian Data Analysis in Python

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

पाठ्यक्रम

Introduction to Linear Modeling in Python

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

पाठ्यक्रम

RNA-Seq with Bioconductor in R

मध्यमकौशल स्तर
4.7+
480
4 घंटे
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.

Probability & Statistics पर संबंधित संसाधन

ब्लॉग

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 मि॰

ट्यूटोरियल

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's photo

Joanne Xiong

11 मि॰

ट्यूटोरियल

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's photo

Abid Ali Awan

10 मि॰


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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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