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Statistics is a branch of science and it is all about data. It is the science of collecting, organizing, describing, and interpreting data.

So it’s not surprising that aspiring data scientists and machine learning engineers need to understand statistics.

If you’re a beginner and you might be curious about how Statistics can fast-track your data science career to the next level.

**As a busy professional**, you’re probably also looking for a way to get a solid understanding of Statistical concepts that’s not only rigorous, but practical and concise.

# Learn Probability and Statistics for Data Science

I’ve curated a list of best online courses to learn Statistics for Data Science so that you can learn to **optimally apply data science techniques** to make informed (and hence better) decisions.

### — Basic Statistics

Basic Statistics course is offered by University of Amsterdam on Coursera. This course is divided in 3 modules, designed for absolute beginners to learn the basics of statistics and understand the methods of descriptive statistics.

Hands-on training for probability and inferential statistics is central to the course and if you like hands-on training, you will love this course.

#### Is it right for you?

Upon the Completion of this course, you’ll not only learn about the basic statistics, but also gain a functional knowledge required to calculate and generate statistics yourself using open-source statistical tools.

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

This course is taught by Dr. James Abdey, assistant Professorial Lecturer at University of London. **This course is designed to edify you with the essential skills** for a lifetime of good decision-making.

First, You will have an introduction to quantifying uncertainty with probability, an overview of descriptive statistics, and then you will learn concepts of point and interval estimation of means and proportions.

Finally, you will learn the basics of hypothesis testing, and a selection of multivariate applications of key terms.

#### Is it right for you?

This course is suitable for beginners and you will be introduced to the useful tools to deal with uncertainty and to make informed decisions.

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### — Introduction to Probability and Data

Introduction to Probability and Data course is offered by Duke University on Coursera. This is a beginner level courses and will introduce you to sampling and exploring data, as well as basic probability theory and Bayes’ rule.

This is a very good course to learn exploratory data analysis techniques, including numeric summary statistics and basic data visualization.

This course is also part ofStatistics with R Specializationand the course modules will cover;

Introduction to Probability and Data Exploratory Data Analysis and Introduction to Inference Introduction to Probability Probability Distributions

#### Is it right for you?

This course will equip you with skills of Sampling methods, inference, data analysis techniques, probability and binomial distributions.

Also, Hands-on labs exercise using R programming and R Studio is essential to this course. So if you want an introduction to R, this course is perfect place to start.

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### — Statistics Foundations: Understanding Probability and Distributions

This course is created by Dmitri Nesteruk, a quantitative analyst, software developer and author of many high rated programming courses and books.

In this course, you will go through an introduction to set theory, a non-rigorous introduction to probability, and get an in-depth overview of statistical research.

You will also learn to discover different statistical distributions, discrete and continuous random variables, probability density functions, and moment generating functions.

#### Is it right for you?

Upon the successful completion, you will not only gain understanding of Probability and Distributions but also be able to use the key distribution measures such as mean and variance, and explore topics of covariance and correlation.

This course assumes no background in Statistics and will help you to look at data and reason about it in terms of its possible distributions and descriptive statistics.

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### — Statistics Essentials for Analytics

In this Statistics Essentials for Analytics course by Edureka, you will learn essential statistics required for Data analytics and Data Science.

This course explains the complete mechanism of Data Science in terms of Statistics and Probability. And you’ll gain hands on practice about the sampling procedures to understand Data and Data Types.

#### Is it right for you?

No prerequisites are required for this course. If you decide to take this courses, you’ll also be introduced to primary machine learning algorithms in this Course.

By the end of this course, you will have a better understanding of statistical inference, testing, clustering.

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### — Statistics with Python Specialization

Statistics with Python specialization is offered by University of Michigan on Coursera. All the courses are designed to teach learners basics and intermediate concepts of statistical analysis using Python.

There are3 Coursesin this Specialization:

- Understanding and Visualizing Data with Python
- Inferential Statistical Analysis with Python
- Fitting Statistical Models to Data with Python

This specialization will also cover important topics in Statistics for Data Science including but not limited statistical and data analysis methods using Python.

#### Is it right for you?

Upon the successful completion of this specialization, you’ll be able use Python to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures.

When you finish this Specialization and complete the hands-on project, you’ll earn a Specialization Certificate.

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### — Statistics with R Specialization

This specialization is designed to help you go from basics to mastering statistics with R.

You will learn key statistical concepts and techniques for performing exploratory data analysis in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of correlation, regression and statistical inference.

#### Is it right for you?

Mastering Statistics is no exception to solving real-world data science problems. This specialization is perfect for you if you don’t have a formal statistical background and will equip you with the essential tools of statistical thinking needed for data science.

All the courses in this specialization will prepare you for the handling of Big Data and Predicting in Machine Learning.

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### — Statistical Thinking in Python (Part 1 & 2 )

Statistical Thinking (Part 1 & 2 ) is offered by DataCamp to provide an immersive hands-on experience using Python for Data Science and equip learners to build a firm Statistical grounding.

In this course, you will learn the relevant conceptual and computational tools to build a solid Statistical grounding and you’ll also learn the basics of exploratory data analysis also known as EDA.

You will learn to plot your data in instructive ways using Python and how to interpret such plots. You will also learn how to use a variety of summary statistics to make sense of and communicate meaningful information about your data.

Finally, you will work with probability distributions, how they arise from stories that occur in the real world and you will come out being able to simulate stories in their distributions using hacker statistics.

#### Is it right for you?

Every Data Scientist must have a strong Statistical grounding to get the most out of their data and they must also have a computational framework to do the statistics.

This is the first course on statistical thinking in Python and you will come out being able to simulate stories in their distributions using hacker statistics.

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If you liked this article, I’ve got a practical reads for you. One about the Learning Python, Maths and Statistics for Data Science and one about Data Science Courses/ Specialization.

Also, i’ve already got you covered to learn Maths in this piece about Maths for Data Science.

I’ve also got this Data-Centric newsletter that you might be into. I send a tiny email once or twice every quarter with some useful resource I’ve found.

Don’t worry, I hate spam as much as you. ☟