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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

课程

R 统计学入门

中级技能水平
4.7+
2,022 条评价
4小时
提升统计技能，学习如何收集、分析数据并得出准确结论。

学习路径

统计学家 在 R 中

4.5+
7 条评价
52小时
统计学家收集和分析数据，帮助公司理解定量数据，包括发现趋势和进行预测。

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课程

统计学入门

基础技能水平
4.8+
8,527 条评价
4小时
学习统计学基础，包括集中趋势和离散程度、概率分布和假设检验，无需编程！

课程

Python 统计学入门

中级技能水平
4.7+
7,933 条评价
4小时
提升统计技能，学习如何使用Python收集、分析数据并得出准确结论。

课程

R 统计学入门

中级技能水平
4.7+
2,022 条评价
4小时
提升统计技能，学习如何收集、分析数据并得出准确结论。

课程

R 中的回归入门

中级技能水平
4.8+
1,430 条评价
4小时
用 R 实现、分析并解读回归分析，预测房价和广告点击率。

课程

Python 假设检验

中级技能水平
4.7+
3,399 条评价
4小时
了解如何以及何时在 Python 中使用常见假设检验，如 t 检验、比例检验和卡方检验。

课程

Python 抽样

中级技能水平
4.7+
3,591 条评价
4小时
学会用 Python 和统计学从有限数据中得出结论。 本课程涵盖从随机抽样到分层抽样和整群抽样的所有内容。

课程

R 中的假设检验

中级技能水平
4.7+
942 条评价
4小时
了解如何以及何时在 R 中使用假设检验，包括 t 检验、比例检验和卡方检验。

课程

Intermediate Regression in R

中级技能水平
4.7+
764 条评价
4小时
Learn to perform linear and logistic regression with multiple explanatory variables.

课程

Time Series Analysis in Python

中级技能水平
4.8+
134 条评价
4小时
In this four-hour course, you’ll learn the basics of analyzing time series data in Python.

课程

Experimental Design in Python

中级技能水平
4.7+
1,757 条评价
4小时
Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!

课程

Sampling in R

中级技能水平
4.7+
824 条评价
4小时
Master sampling to get more accurate statistics with less data.

课程

ARIMA Models in R

基础技能水平
4.8+
306 条评价
4小时
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

课程

Time Series Analysis in R

中级技能水平
4.8+
88 条评价
4小时
Learn the core techniques necessary to extract meaningful insights from time series data.

课程

Introduction to Statistics in Google Sheets

基础技能水平
4.8+
577 条评价
4小时
Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.

课程

Modeling with Data in the Tidyverse

中级技能水平
4.8+
227 条评价
4小时
Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.

课程

A/B Testing in Python

中级技能水平
4.7+
353 条评价
4小时
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.

课程

Introduction to Bioconductor in R

中级技能水平
4.7+
112 条评价
4小时
Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!

课程

Forecasting in R

基础技能水平
4.9+
51 条评价
5小时
Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.

课程

Linear Algebra for Data Science in R

中级技能水平
4.7+
133 条评价
4小时
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.

课程

Foundations of Inference in R

中级技能水平
4.7+
50 条评价
4小时
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

课程

Generalized Linear Models in R

中级技能水平
4.8+
190 条评价
4小时
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

课程

Experimental Design in R

中级技能水平
4.7+
320 条评价
4小时
In this course youll learn about basic experimental design, a crucial part of any data analysis.

课程

Statistical Techniques in Tableau

中级技能水平
4.8+
632 条评价
4小时
Take your reporting skills to the next level with Tableau’s built-in statistical functions.

课程

Foundations of Probability in Python

中级技能水平
4.8+
201 条评价
5小时
Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

课程

Foundations of Probability in R

基础技能水平
4.7+
443 条评价
4小时
In this course, youll learn about the concepts of random variables, distributions, and conditioning.

课程

RNA-Seq with Bioconductor in R

中级技能水平
4.7+
138 条评价
4小时
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.

Probability & Statistics 相关资源

博客

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分钟

教程

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分钟

教程

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分钟

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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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