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If you want to become a skilled practitioner-level data scientist, then you need to become highly equipped in probability and statistics for data science and machine learning.

It is exceedingly important to master statistics to solve the complex challenges with data.

As a data scientist, your role will require sound statistical skills to make sense of quantitative data to spot trends and make predictions.

And the typical statistical responsibilities of the data science roles include:

— Statistical Analysis — Hypothesis Testing

— Statistical Programming — Probability

— Tendency and Distribution of Data — Analysing trends

— Designing Data Acquisition trials — Assessing results

& much more …

So, with the help of quality resources, you can build the statistical intuition and the cognitive skills you need for almost any discipline, not just Data Science.

Statistics is critical to efficient data science and in this article, we’ve curated the highest-rated courses from world-class educators to help learners, acquire sound knowledge.

So, without further ado, let’s get started…

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## Best Statistics for Data Science Courses

Inside This Article

These **Statistics for Data Science** Courses from Notable Instructors are highly recommended for beginners, intermediate and advanced learners.

And you can audit these courses for free to learn efficiently.

### — Introduction to Computational Statistics for Data Scientists

The high-quality specialization aims to teach the basics of Computational Statistics for the purpose of performing inference to Data Science learners.

The life-long skills you will gain in this course will bootstrap your abilities to have a tight grip on the basics of Bayesian modelling and inference.

Along with the functional knowledge, you will gain a conceptual understanding of the techniques used to perform Bayesian inference in practice.

#### GO TO → Introduction to Computational Statistics for Data Scientists

### — Introduction to Statistics

This Introductory-level course is offered by Stanford University to help learners gain foundational skills in Statistics for learning advanced topics for Data Science and Machine Learning.

By the end, you’ll have acquired competence in Descriptive Statistics, Sampling and Randomized Controlled Experiments.

Moreover, you’ll gain a strong familiarity with Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, and Multiple Comparisons.

#### GO TO → Introduction to Statistics

### — Introduction to Statistics in R

This beginner-level course offered by DataCamp is suitable to learn probability and to conduct a well-designed study to draw your own conclusions from data using R Programming.

Through the guided lectures, you’ll work with the random numbers for experimental probability, grow your understanding of Probability distributions, and finally learn Correlation and Experimental design

#### GO TO → Introduction to Statistics in R

### — Basic Statistics

This beginner-friendly course offered by The University of Amsterdam is suitable for absolute beginners to gain familiarity with the basic statistical concepts for data analysis.

Although, basic familiarity with R programming is assumed for enrolling in this course.

Upon successful completion, you’ll have gained data science skills in Statistics, Confidence Interval, Statistical Hypothesis Testing and the basics of R Programming.

#### GO TO → Basic Statistics

### — Introduction to Statistics in Python

This Introductory-level statistics course in Python for beginners is offered by DataCamp and taught by Adel Nehme.

This course to grow Statistical skills is perfect to learn to calculate averages, use scatterplots to show the relationship between numeric values, and calculate the correlation using Python.

Through the guided lectures, you’ll gain life-long skills in Python for Data Science.

#### GO TO → Introduction to Statistics in Python

### — Statistics and R

This open course is offered by Harvard to help learners understand the basic Statistical Concepts with R Programming.

Through the guided lectures, you will gain the R programming skills necessary for analyzing real-world data.**By the end** of this course, you’ll have gained some foundational knowledge of Statistics with R Programming for Data Science.

#### GO TO → Statistics and R

### — Statistics with Python Specialization

This highest-rated specialization offered by The University of Michigan is suitable for beginners to master Statistical Inference, Data Visualization, and Modeling in Python.

By the end, you will have gained skills in Python Programming, Data Visualization, Statistical Model, Statistical inference methods.

Moreover, you’ll also learn Data Analysis, Confidence Interval, Statistical Hypothesis Testing, Bayesian Statistics and Statistical Regression

#### GO TO → Statistics with Python Specialization

### — Statistics with R Specialization

This beginner-friendly specialization offered by Duke University is suitable for beginners to master Statistics with R.

Upon successful completion, you will have gained skills in Bayesian Statistics, Linear Regression, Statistical Inference, R Programming, and Rstudio.

You’ll also learn Exploratory Data Analysis, Statistical Hypothesis Testing, Regression Analysis, Bayesian Linear Regression, Bayesian Inference, and Model Selection

#### GO TO → Statistics with R Specialization

### — Statistics with R

This beginner-level course offered on Udemy is suitable for learners who have basic knowledge of R Programming and Statistics.

By the end of this course, you should be able to manipulate data in R, Compute Statistical Indicators, Build frequency tables.

You’ll also gain skills to Create Histograms and Cumulative Frequency Charts, Perform Pnivariate Analyses, determine Skewness and Kurtosis, and more

#### GO TO → Statistics with R

### — Intermediate Regression in R

If you have sound R programming skills and good knowledge of Regression, then this course will bootstrap your abilities in using statistical models for Data Science.

This course is suitable for learners who have taken Introduction to Regression in R by DataCamp.

By the end, you’ll have a deeper understanding of how linear and logistic regressions work by working on real-world datasets.

#### GO TO → Intermediate Regression in R

### — Statistics and Data Science MicroMasters ® Program

This highly recommended **MicroMasters **program offered by MIT via edX provides expert instructions to help learners grasp the foundations of data science, statistics, and machine learning.

This certification program is very suitable for learners interested in Big Data.

You’ll learn through the guided lectures, exercises and projects to analyze and make data-driven predictions through probabilistic modelling and statistical inference.

You’ll gain employable skills to build machine learning algorithms on your own to extract meaningful information even from seemingly unstructured data.

Moreover, you’ll learn popular unsupervised learning methods, including clustering methodologies and supervised methods such as deep neural networks.

Upon successful completion, you’ll be prepared for job titles such as Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst, and Data Engineer.

#### GO TO → Statistics and Data Science MicroMasters® Program

### — Bayesian Statistics: From Concept to Data Analysis

This course is suitable to help learners advance in Statistical understanding and data analysis skills through the guided lectures and hands-on exercises in R Programming and Excel.

Through the series of lectures, demonstrations, readings, exercises, and discussions, you’ll know how to perform data analysis on your own.

By the end, you’ll have a sound understanding of the Bayesian approach to Statistics, Probability, Statistical Inference, and building models for data analysis.

#### GO TO → Bayesian Statistics: From Concept to Data Analysis

### — Improving your Statistical Inferences

This is one of the **best Intermediate-Level courses** offered on Coursera that is rich with simple explanations, strong examples, and very useful exercises to learn Inferential Statistics.

You’ll gain lifelong statistical skills in Likelihood Function, Bayesian Statistics, P-Value and Statistical Inference.

By the end, you will have the skills to evaluate hypotheses using equivalence testing and Bayesian statistics.

#### GO TO → Improving your Statistical Inferences

### — Statistics Fundamentals with Python ( Skill-Track )

This skill track provides a comprehensive path to gain strong skills in Data Analysis, Exploratory Data Analysis, Inference, Formal Statistical Modeling, Interpretation, and Communication.

The courses included in this skill track will help you tremendously in applying statistical knowledge and techniques to business contexts and working on complex data science projects.

#### GO TO → Statistics Fundamentals with Python

### — Probability and Statistics: To p or not to p?

This high-quality course offered by The University of London introduces learners to many useful tools for dealing with uncertainty to make informed decisions.

This course will equip you with a sound understanding of quantifying uncertainty with probability, descriptive statistics, & points.

And you’ll know how to perform interval estimation of means and proportions, the basics of hypothesis testing, and a selection of multivariate applications.

#### GO TO → Probability and Statistics: To p or not to p?

### — Statistical Thinking in Python (Part 1)

This high-quality “Statistical Thinking in Python” course aims to help you build the foundation you need in Python to think statistically.

The lessons in this course include:

- Graphical Exploratory Data Analysis
- Quantitative Exploratory Data Analysis
- Thinking Probabilistically– Discrete Variables
- Thinking Probabilistically– Continuous Variables

#### GO TO → Statistical Thinking in Python (Part 1)

### — Statistical Thinking in Python (Part 2)

In Part 2, you will dig deeper to become highly prepared to execute key tasks in statistical inference; parameter estimation, and hypothesis testing using real-world data in Python.

The modules in this course thoroughly cover the following topics:

- Parameter estimation by optimization.
- Bootstrap confidence intervals.
- Introduction to hypothesis testing.
- Hypothesis test examples.

#### GO TO → Statistical Thinking in Python (Part 2)

### — Probability Theory, Statistics and Exploratory Data Analysis

This interactive course teaches the important concepts in probability theory and statistics, from basics to the level required for learning advanced topics.

You’ll pay closer attention to learn probabilities, sampling, data analysis, and data visualization in Python.

This course requires basic knowledge in Discrete mathematics (combinatorics) and calculus (derivatives, integrals) and is part of the Master of Data Science degree program offered by HSE University.

#### GO TO → Probability Theory, Statistics and Exploratory Data Analysis

### — Intermediate Statistical Modeling in R

This course is suitable for learners with a basic knowledge of Statistics and experience in R Programming.

Through the interactive exercises, you’ll dive deeper to a look at effect size and interaction, and then understand the concepts of a total and partial change.

Moreover, you will learn about sampling variability and mathematical transforms, and the implications of something called collinearity.

#### GO TO → Intermediate Statistical Modeling in R

### — Statistical Modeling for Data Science Applications Specialization

This intermediate-level specialization teaches advanced statistical modelling techniques and equips learners with the functional knowledge for Data Science.

This specialization is suitable for learners who have a basic knowledge of R language, Calculus, linear algebra, and probability theory.

By the end, you will have gained skills in Linear Model, R Programming, Statistical Model, Regression, Calculus, Probability, and Linear Algebra

#### GO TO → Statistical Modeling for Data Science Applications Specialization

### — Correlation and Regression in R

This course is suitable for learners who have understanding and some experience in doing Exploratory Data Analysis in R Programming.

By the end, you’ll not only have mastered the techniques for bivariate relationships but also gained advanced skills in Correlation, Simple Linear Regression, Interpreting regression models, Model Fit.

#### GO TO → Correlation and Regression in R

### — Linear Regression for Business Statistics

This course helps you to learn and understand the applications of Linear Regression for Variable Regression, Transforming Variables, and Interaction effect.

By the end, you’ll have a stronghold in Log-Log Plot, Interaction (Statistics), Linear Regression, and Regression Analysis.

#### GO TO → Linear Regression for Business Statistics

### — Statistics for Data Science and Business Analysis

This Intermediate-level course aims to equip learners for data science skills in Descriptive & Inferential statistics, Hypothesis testing, Regression analysis, and more.

By the end, you’ll know the fundamentals of statistics, work with different types of data, Estimate confidence intervals, Carry out regression analysis, Understand the concepts needed for data science with Python and R programming, and more.

#### GO TO → Statistics for Data Science and Business Analysis

### — Practicing Statistics Interview Questions in Python

This course prepares learners for the Statistical interview required for Data Science in Python by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.

By the end, you’ll have a clear understanding of how to work with a diverse collection of datasets including web-based experiment results and Australian weather data.

#### GO TO → Practicing Statistics Interview Questions in Python

### — Practicing Statistics Interview Questions in R

In this course, you’ll prepare for the most frequently covered Statistics topics for Data Science to confidently tackle any questions in R Programming.

By the end, you’ll have covered the key concepts and learned distributions to hypothesis testing, regression models, and more.

#### GO TO → Practicing Statistics Interview Questions in R

### — Probabilistic Graphical Models

This course aims to help learners master a new way of reasoning and learning in complex domains and getting a deeper understanding of Representation, Inference, and Learning.

If you take this course 2 to 3 times, you’ll gain strong knowledge and skills in Inference, Bayesian Network, Belief Propagation, Graphical Model, Markov Random Field, Gibbs Sampling, Markov Chain Monte Carlo (MCMC), Algorithms, and Expectation-Maximization (EM) Algorithm.

#### GO TO → Probabilistic Graphical Models

### — Statistics with R – Advanced Level

This course is suitable for learners who have intermediate R Programming skills and good knowledge of Statistics.

Upon completion, you’ll be able to perform the analysis of covariance, un the mixed analysis of variance, run the cluster analysis (k-means and hierarchical), run the simple and multiple discriminant analysis, execute the binomial logistic regression, and more

#### GO TO → Statistics with R – Advanced Level

### — Advanced Linear Models for Data Science 1: Least Squares

In this course, learners will gain a firm foundation in a linear algebraic treatment of regression modelling.

Through the series of guided lectures, exercises, readings and discussions, you become equipped with the Sound Data Science skills in Linear Regression, R Programming, and Linear Algebra.

#### GO TO → Advanced Linear Models for Data Science 1: Least Squares

### — Advanced Linear Models for Data Science 2: Statistical Linear Models

In this course, you’ll dig much deeper to understand expected values, multivariate normal distribution, Distributional results, and Residuals.

This course is suitable for learners with a background in R and college-level statistics and Math.

You’ll go a little further in this course to gain a solid understanding of models for Data Science.

#### GO TO → Advanced Linear Models for Data Science 2: Statistical Linear Models

### — Spatial Statistics in R

This advanced-level class is suitable for learners with good experience in R Programming and intermediate-level skills in Statistics.

By the end, you’ll have sound skills and strong knowledge of the key concepts to perform spatial data analysis.

Moreover, you’ll acquire knowledge in Point Pattern Analysis, Areal Statistics, and Geostatistics.

#### GO TO → Spatial Statistics in R

### — Advanced Statistics for Data Science Specialization

In this Advanced-level Specialization by John Hopkins University, you’ll become familiar with the fundamental concepts in probability and statistics, data analysis, matrix algebra, and linear models for Data Science.

Courses in this Specialization include;

- Mathematical Biostatistics Boot Camp,
- Mathematical Biostatistics Boot Camp 2,
- Advanced Linear Models for Data Science 1: Least Squares,
- Advanced Linear Models for Data Science 2: Statistical Linear Models

#### GO TO → Advanced Statistics for Data Science Specialization

##### Closing Notes

So, that was us giving away carefully curated Statistics for Data Science courses for the common good!

We hope this curation helps you expand your data science knowledge and fight your fear of discovering what’s happening behind the scenes.

###### Thanks for making it to the end *:* )

If you liked this article, we’ve got a few practical resources for you. One about Math for Data Science and one about Spatial Analysis.

Cheers