Are you looking for the **Best Courses on Statistics for Data Science?** If yes, then don’t worry! I have chosen the** 12 **best courses on **Statistics for Data Science** after filtering out hundreds of courses. So, give your few minutes to this article and find out the **Best Courses on Statistics for Data Science**.

**Courses List-**

- 1. Statistics with R Specialization– Duke University
- 2. Practical Statistics- Udacity
- 3. Statistics with Python Specialization- University of Michigan
- 4. Statistician with R- Datacamp
- 5. Intro to Statistics– Udacity
- 6. Data Science: Statistics and Machine Learning Specialization- Johns Hopkins University
- 7. Business Statistics and Analysis Specialization- Rice University
- 8. Statistics Fundamentals with R- Datacamp
- 9. Statistical Analysis with R for Public Health Specialization- Imperial College London
- 10. Basic Statistics- University of Amsterdam
- 11. Statistics Fundamentals with Python- Datacamp
- 12. Learn Statistics with Python- Codecademy
- Conclusion
- FAQ

**Best Courses on Statistics for Data Science**

**Best Courses on Statistics for Data Science**To become a successful data scientist or data analyst, you should have a good understanding of Statistics. Knowledge of statistics will give you the ability to decide which algorithm is good for a certain problem.

Statistics knowledge includes** statistical tests, distributions, and maximum likelihood estimators**. All are essential in data science.

As a Data Scientist or Analyst, you have to find **useful insights from the data**, and for that statistics, knowledge is crucial for you. Now, you might be thinking, OK fine, Statistical knowledge is required but from where to learn?

So, if you are thinking the same, then don’t worry. I have chosen the **12 best courses on statistics for data science**. These courses will give you in-depth knowledge of statistics.

I have filtered these courses on the following criteria-

**Criteria-**

- Rating of these Courses.
- Coverage of Topics.
- Engaging trainer and Interesting lectures.
- Number of Students Benefitted.
- Good Reviews from various aggregators and forums.

So, without wasting your time, let’s start finding the **Best Courses on Statistics for Data Science**.

**1. ****Statistics with R Specialization**– **Duke University**

**Statistics with R Specialization****Duke University**

**Rating-** 4.6/5

**Provider-** Coursera

This specialization program will give you **in-depth Statistics knowledge** with the help of **R**. In this program, you will learn how to **analyze and visualize data in R** and create reproducible data analysis reports, and much more.

R is much better than Python for performing statistical operations. So, if you want to master Statistics, then I would recommend this specialization program. This specialization program contains **5 Courses**. Let’s see the course details-

**Courses Include-**

**Introduction to Probability and Data with R****Inferential Statistics****Linear Regression and Modeling****Bayesian Statistics****Statistics with R Capstone**

Now, let’s see what statistic skills you will gain after completing this specialization program-

**Skills Gain-**

- Bayesian Statistics
- Linear Regression
- Statistical Inference
- R Programming
- Statistics
- Rstudio
- Exploratory Data Analysis
- Statistical Hypothesis Testing
- Regression Analysis
- Bayesian Linear Regression
- Bayesian Inference
- Model Selection

**Extra Benefits-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - Along with this, you will get
**Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.**

Now, let’s see whether you should enroll in this specialization program or not?

**You Should Enroll if-**

- You have
**basic math knowledge**. No previous programming knowledge is required for this course.

**Time to Complete-**

- This program will take approximately
**7 months**to complete

**What’s the Price?**

- 7 Day Full Access Free Trial and after that 49$/month.

**Interested to Enroll?**

If yes, then check out all details here- **Statistics with R Specialization**

**2. Practical Statistics– Udacity**

**Rating- **NA

**Provider- **Mode

As the name sounds, “**Practical Statistics**“, this course is focused on the** practical implementation** of statistical concepts. In this course, you will understand how to tackle common real-world challenges, such as **analyzing AB tests **and building **regression models.**

In this course, there is a **project**, where you have to use **statistical techniques **to answer questions about the data and report your conclusions and recommendations in a report. Dataset will be provided.

Topics covered in this course are- **Simpson’s Paradox, Probability, Binomial Distribution, Conditional Probability, Bayes Rule, Standardizing, Sampling Distributions, and Central Limit Theorem, confidence intervals, Hypothesis Testing, T-Tests, and A/B Tests, Regression, multiple linear regression, and logistic regression.**

**Extra Benefits-**

- You will chance to work on
**real-world projects with industry experts.** - You will get
**Project feedback from experienced reviewers**. - You will also get
**Technical mentor support.**

**Who Should Enroll?**

- Those who have experience working with
**SQL**and with data in**Python.**

**Interested to Enroll?**

If yes, then check out all details here- **Practical Statistics**

**3. Statistics with Python Specialization– ****University of Michigan**

**University of Michigan**

**Rating- **4.5/5

**Provider- **Coursera

This specialization program is especially dedicated to statistics. In this program, you will learn **basic and intermediate** concepts of statistical analysis using the Python programming language.

In this program, you will learn all important concepts like- **where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization.**

Along with that, you will work on a variety of **assignments** that will help you to check your knowledge and ability. This specialization program is a** 3-course series**.

Now, let’s see what statistic skills you will gain after completing this specialization program-

**Skills Gain-**

- Python Programming
- Data Visualization (DataViz)
- Statistical Model
- Statistical inference methods
- Statistics
- Data Analysis
- Confidence Interval
- Statistical Inference
- Statistical Hypothesis Testing
- Bayesian Statistics
- Statistical regression

**Extra Benefits-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - Along with this, you will get
**Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.**

Now, let’s see whether you should enroll in this specialization program or not?

**You Should Enroll if-**

- You have Knowledge of
**basic Python and High school-level algebra.**

**Time to Complete-**

- Approximately, it would take
**3****months**to complete the entire specialization program( all 3 courses).

**What’s the Price?**

- 7 Day Full Access Free Trial and after that 49$/month.

**Interested to Enroll?**

If yes, then check out all details here- **Statistics with Python Specialization**

**4. ****Statistician with R**– *Datacamp*

**Statistician with R**–

*Datacamp*

**Time to Complete- **108 hours

This is a **Career Track** offered by Datacamp. In this career track, there are **27 courses. **This career track will help you to gain essential skills to land a job as a statistician. In this career track, you will learn basic to advanced level concepts of statistics.

At the beginning of the career track, you will learn how to** collect, analyze, and draw accurate conclusions** from data, concepts of **random variables, distributions, and conditioning**, using the example of coin flips, and how to fit **simple linear and logistic regressions**, how to fit model** binomial data with logistic regression** and count data with **Poisson regression**, etc.

Then you will learn **Sampling, Hypothesis testing, basic experimental design, A/B testing, how to deal with missing data, survey design, survival analysis, Bayesian data analysis, Factor Analysis, and much more**.

Now let’s see whether you should enroll in this Career track or not-

**You Should Enroll if-**

**You Should Enroll if-**

- If you have previous knowledge in R programming.

**Interested to Enroll?**

If yes, then check out all details here-**Statistician with R**

**5. ****Intro to Statistics**– **Udacity**

This is a **completely FREE course **for **beginners** and covers **data visualization, probability, and many elementary statistics concepts like regression, hypothesis testing, and more.**

In this course, you will also learn **visualization and relationships in data, Probability with Bayes Rule and Correlation vs Causation, estimation with Maximum Likelihood,** **mean, median, and mode,** **statistical inference, and regression analysis.**

**You Should Enroll if-**

- You are a beginner, but it’s good if you have already heard of some
**easy statistical concepts.**

**Interested to Enroll?**

If yes, then start learning- **Intro to Statistics**

**6. Data Science: Statistics and Machine Learning Specialization– ****Johns Hopkins University**

**Johns Hopkins University**

**Rating- **4.4/5

**Provider- **Coursera

This is another Specialization program dedicated to statistics concepts. In this program, you will learn **statistical inference, regression models, machine learning, and the development of data products.**

At the end of this program, you will work on **Capstone Project**, where you will apply the skills learned by building a data product using real-world data. This specialization program uses the **R programming language.**

There are 5 courses in this specialization program.

Now, let’s see what statistic skills you will gain after completing this specialization program-

**Skills Gain-**

- Machine Learning
- Github
- R Programming
- Regression Analysis
- Data Visualization (DataViz)
- Statistics
- Statistical Inference
- Statistical Hypothesis Testing
- Model Selection
- Generalized Linear Model
- Linear Regression
- Random Forest

**Extra Benefits-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - Along with that, you will get
**Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.**

Now, let’s see whether you should enroll in this specialization program or not?

**You Should Enroll if-**

- You have completed the
specialization in order to gain the right foundation. Or you have a good understanding of R programming.**Data Science: Foundations using R Specialization**

**Time to Complete-**

- This program will take approximately
**5 months**to complete

**What’s the Price?**

- 7 Day Full Access Free Trial and after that 49$/month.

**Interested to Enroll?**

If yes, then check out all details here- **Data Science: Statistics and Machine Learning Specialization**

**7. Business Statistics and Analysis Specialization**– **Rice University**

**Rice University**

**Rating-** 4.8/5

**Provider-** Coursera

This specialization program will teach you** Business Statistics and Analysis.** In this program, you will learn basic **probability concepts**, including measuring and modeling uncertainty, and you’ll use various **data distributions,** along with the** Linear Regression Model,** to analyze and inform business decisions.

This specialization program has 5 courses.

Now, let’s see what statistic skills you will gain after completing this specialization program-

**Skills Gain-**

- Microsoft Excel
- Linear Regression
- Statistical Hypothesis Testing
- Lookup Table
- Data Analysis
- Pivot Table
- Statistics
- Statistical Analysis
- Normal Distribution
- Poisson Distribution
- Log–Log Plot
- Interaction (Statistics)

**Extra Benefits-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - Along with that, you will get

Now, let’s see whether you should enroll in this specialization program or not?

**Who Should Enroll?**

- There is
**no condition for enrolling**in this program. Anyone can enroll in this specialization program. No prior experience is required to enroll in this program.

**Time to Complete-**

- This program will take approximately
**5 months**to complete.

**What’s the Price?**

- 7 Day Full Access Free Trial and after that 49$/month.

**Interested to Enroll?**

If yes, then check out all details here- **Business Statistics and Analysis Specialization**

**8. Statistics Fundamentals with R– ***Datacamp*

*Datacamp*

**Time to Complete- **20 hours

This is a **Skill Track **offered by Datacamp. In this skill track, there are 5 courses. In this skill track, you will learn how to answer questions like, “**what is the likelihood of someone purchasing your product?”, “how many calls will your support team receive?**“, etc by using **sales data**.

You will also learn the two most widely used statistical models, **Linear regression and logistic regression**. And how to perform linear and logistic regression with **multiple explanatory variables.** Then you will learn about **Sampling and Hypothesis testing.**

Now, let’s see whether you should enroll in this specialization program or not?

**You Should Enroll if-**

- You are comfortable in R programming.

**Interested to Enroll?**

If yes, then check out all details here- **Statistics Fundamentals with R**

**9. Statistical Analysis with R for Public Health Specialization**– **Imperial College London**

**Imperial College London**

**Rating- **4.7/5

**Provider- **Coursera

This specialization program is especially dedicated to the **Statistical Analysis for Public Health.** In this program, you will learn key statistical concepts like **sampling, uncertainty, variation, missing values, and distributions.**

Along with that, you will get your hands dirty with analyzing data sets covering some big public health challenges – **fruit and vegetable consumption and cancer, risk factors for diabetes, and predictors of death following heart failure hospitalization – using R**.

This specialization consists of 4 courses.

Now, let’s see what statistic skills you will gain after completing this specialization program-

**Skills Gain-**

- Statistical Thinking
- Survival Analysis
- Logistic Regression
- Data analysis with R
- Linear Regression
- Run basic analyses in R
- R Programming
- Understand common data distributions and types of variables
- Formulate a scientific hypothesis
- Correlation And Dependence
- Understand common ways to choose what predictors go into a regression model
- Run and interpret Kaplan-Meier curves in R

**Extra Benefits-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - Along with that, you will get

Now, let’s see whether you should enroll in this specialization program or not?

**Who Should Enroll?**

- Anyone can enroll who has
**an interest in medicine and statistics**. This is aprogram.**Beginner Level****No medical, statistical, or R knowledge is assumed.**

**Time to Complete-**

- This program will take approximately
**4 months**to complete.

**What’s the Price?**

- 7 Day Full Access Free Trial and after that 49$/month.

**Interested to Enroll?**

If yes, then check out all details here- **Statistical Analysis with R for Public Health Specialization**

**10. Basic Statistics**– **University of Amsterdam**

**University of Amsterdam**

**Rating- **4.7/5

**Provider- **Coursera

In this course, you will learn the basics of statistics like what cases and variables are and how you can compute measures of central tendency (**mean, median, and mode**) and dispersion (**standard deviation and variance**).

Along with that, you will learn the basics of probability- **calculating probabilities, probability distributions, and sampling distributions.** You will also learn **inferential statistics.**

Now, let’s see the syllabus of the course-

**Syllabus of the Course-**

- Introduction
- Exploring Data
- Correlation and Regression
- Probability
- Probability Distributions
- Sampling Distributions
- Confidence Intervals
- Significance Tests

Now, let’s see what statistic skills you will gain after completing this course-

**Skills Gain-**

- Statistics
- Confidence Interval
- Statistical Hypothesis Testing
- R Programming

**Extra Benefits-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - Along with that, you will get

Now, let’s see whether you should enroll in this course or not?

**You Should Enroll if-**

- You want to learn the basics of statistics at a beginner level.

**Time to Complete-**

- This course will take approximately
**26 hours**to complete

**What’s the Price?**

- 7-Day Full Access Free Trial and after that 39$/month.

**Interested to Enroll?**

If yes, then check out all details here- **Basic Statistics**

**11. Statistics Fundamentals with Python– ***Datacamp*

*Datacamp*

**Time to complete- **19 hours

This is another **skill track** offered by Datacamp. This skill track is for those who want to learn **statistics using Python**. In this skill track, you will learn the foundation you need to **think statistically using Python**. Then you will learn how to perform the two key tasks in statistical inference, **parameter estimation, and hypothesis testing**.

In this career track, you will also learn about **exploring, quantifying, and modeling linear relationships in data** using Python. Then you will learn how to solve increasingly complex problems using simulations to generate and analyze data.

Now, let’s see whether you should enroll in this specialization program or not?

**You Should Enroll if-**

- You have previous knowledge of Python.

**Interested to Enroll?**

If yes, then check out all details here- **Statistics Fundamentals with Python**

**12. Learn Statistics with Python**– *Codecademy*

*Codecademy*

This course will teach you some different descriptive statistics including the **mean, median, mode, standard deviation, and variance of different datasets.** Along with that, you will learn how to calculate these statistics and how to interpret them. This course uses Python Programming language for coding.

**Topics Covered in this Course-**

- Mean, Median, and Mode
- Variance and Standard Deviation
- Histograms
- Describe a Histogram.
- Quartiles, Quantiles, and Interquartile Range
- Boxplots

**Projects You will build in** **this Course**–

- Variance in Weather
- Find the Best time to visit Acadia.
- Central Tendency for Housing Data

**Who Should Enroll?**

- There is no prerequisite. Anyone can enroll who wants to learn the basics of statistics.

**Time to Complete-**

- Approximately, it would take
**15 hours**to complete.

**Interested to Enroll?**

If yes, then check out all details here- **Learn Statistics with Python**

So, that’s all. These are the **12** **Best Courses on Statistics for Data Science**. Now, it’s time to wrap up.

**Conclusion**

I hope these courses will help you to learn statistics for data science. I aim to provide you with the best resources for Learning. If you have any doubts or questions, feel free to ask me in the comment section.

Tell me in the comment section, which is the **Best Course on Statistics for Data Science**?

All the Best!

Happy Learning!

**FAQ**

**1. How is Statistics used in Data Science?**

Statistics knowledge will give you the ability to decide which algorithm is good for a certain problem. As a Data Analyst, you have to find useful insights from the data, so, that’s why statistics knowledge is crucial for you.

**2. Which is the best course for data science?**

**IBM Data Science Professional Certificate** is the best Certification course for data science. For more courses on data science, you can check it out here- Best Online Courses for Data Science to become A Skilled Data Scientist

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### Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.