Ever wanted to learn probability and statistics through easy online courses? Find the best ones here!

Statistics and probability are two of the main tools of any Data Scientist or Machine Learning practitioner. Without understanding them well, it is almost impossible to make sense of how our algorithms and models work, and what they tell us.

It is also very important to master them in order to be able to translate business problems into Machine Learning solutions properly.

As we know it is not always easy to find resources for these topics, we have put together a list of on-line courses for you. Have fun, and learn a lot!

If you are already familiar with statistics and probability, go take a look at our list of Machine Learning Courses.

**Statistics and Probability by Khanacademy**

**Free course:** This course is absolutely free. No tricks or certificates.

**Description**: As most of Khan Academie’s courses, Statistics and Probability is offered through an extensive series of fun and short, videos with quizzes in between where you can get points and check the level of your statistical knowledge.

They give the courses a game-like structure which makes them a lot of fun to take and also very educational. However, despite this game-like structure it is a course that covers a lot of material: from basic probability and distributions to more advanced concepts like inference or ANOVA models. This course might be a great step after going through an Introductory Statistics book like Bayesian Statistics: The Fun way that is very theoretical, and has very little code.

**Duration**: It is quite an extensive course. Probably it will take a little less than a month with good dedication.

**Prerequisites**: It requires no formal prerequisites, other than an appropriate level of maths.

**Introduction to probability and data on Coursera**

**Paid Course:** As most courses from this platform, this course is only available with a Coursera subscription. We do believe however that because of the wide variety of courses for every category available (if you subscribe you can also take Machine Learning and Artificial Intelligence Courses for example), it is worth paying for this subscription in your learning process as you will probably end up saving money, and also obtain shiny cool certificates.

**Description: **This course is offered by Duke University. It is more oriented towards Data Science than other courses that can be found out there. It will introduce you to sampling and exploring data, basic probability theory and Bayes Theorem.

You will also learn how to explore and visualise data using the R software, which you will use too for the exercises and final project. It is an extremely complete course, and for many it has been the first step in their Data Science careers. It is a great alternative to books like Statistical Rethinking or Practical Statistics for Data Scientists.

**Duration: **The course contains about 25 hours of video plus the exercises, so it would take about a month with dedication of 1 hour per day.

**Prerequisites**: A decent level of maths is needed to feel comfortable while completing this course, taking into account it is a beginner level statistics course.

**Data Science: Probability on edx**

**Free course**: This course is free if you don’t want the shiny certificate at the end.

**Description:** It is offered by Harvard University, so you can expect it to be a very good probability course. It covers probability theory concepts like random variables, and independence, expected values, mean, variance and all the elements of statistics you need to understand in order to become a Data Scientist.

It also covers some practical methodologies like Monte Carlo simulations along with theoretical insights like the Central Limit Theorem. Exercises and lessons are covered in R, so you will also get a nice introduction into this programming Language. Taking this course, and then reading a book like the Python for Data Science Handbook will make you perfectly understand the concepts of probability and statistics, and also able to implement them in both R and Python.

**Duration:** The course is almost 20 hours long, so it can be easily completed in a couple of weeks with a fair dedication.

**Prerequisites:** knowledge of maths and programming are advised, as progressing through the course will be easier, however they are not mandatory if you are willing to make an effort.

**Mathematics for Machine Learning Specialisation by Imperial Collage London on Coursera**

**Free course:** Like many others in Coursera this specialisation is free if you donβt want a certificate or the exercises. For that, you have to audit the Course. Follow the instructions in this article to enrol for free. If you enrol for free however, you will miss out on assignments, which we consider fundamental, so our advice is to paid for the specialisation in order to get the most out of it.

**Description:** The title of this specialisation defines it very neatly. In order to be able to understand Machine Learning, some basic mathematical and algebraic knowledge is needed. In this course you will be provided with the necessary mathematical background and skills in order to understand, design, and implement modern statistical Machine Learning methodologies and inference mechanisms.

You will also be provided with examples regarding the use of the mathematical tools that are used to design and that serve as the foundation of Machine learning Techniques like Principal Component Analysis (PCA), Bayesian Linear Regression and Support Vector Machines (SVMs)

The specialisation is divided into three courses:

In the first course on Linear Algebra you will learn what linear algebra is and how it relates to data. Then you will get comfortable with vectors and matrices, and learn how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimise fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. Lastly, the third course covers Dimensionality Reduction with Principal Component Analysis and uses the mathematics from the first two courses to compress high-dimensional data.

At the end of this specialisation you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

We think that if you don’t have a strong mathematical background, and want to get into Machine Learning this specialisation is a very good point to take you from beginner to medium/high level. The courses can be take individually instead of in the specialisation format, so we advise at least to take the first one in order to be able to grasp the maths of Machine Learning. The individual courses can be found on the following links:

- Mathematics for Machine Learning: Linear Algebra
- Mathematics for Machine Learning: Multivariate Calculus
- Mathematics for Machine Learning: PCA

**Duration**: Completing the 3 courses and achieving the whole specialisation takes up to 4 months of easy work, however with some effort you can finish it in half the time.

**Prerequisites:** This course is of intermediate difficulty and will require Python and numpy knowledge.

**Intro to Statistics on Udacity**

**Free Course:** This is yet another one free statistics course, however if you don’t pay you will not be able to get mentorship or a certification.

**Description**: The intro to statistics course on Udacity (also known as Statistics 101) is, as its name says, a beginner statistics course that covers data visualisation, probability and many elementary statistics concepts like regression, hypothesis testing and more.

It is a self paced course with a final exam to check your knowledge on the probability and statistics learned throughout the different chapters. Specifically it covers: Visualisation and relationships in data, Probability with Bayes Rule and Correlation vs Causation, estimation with Maximum Likelihood, and an introduction to concepts that are central to statistics like the mean, median and mode, a whole chapters on outliers and distributions, and two final blocks covering statistical inference and regression analysis.

Sebastian Thrun, the professor is engaging, bright and fun, making the lectures pass by with ease, while increasing the retention of the learned concepts. Each chapter contains assignments that are a must do, and will guide you to making the most out of the course.

**Duration:** The course is about 2 months or 8 weeks long, with a small daily dedication. It has 43 lessons, and doing 1 lesson each day shouldn’t take too much effort, so it is what we recommend for optimal retention.

**Prerequisites**: despite being an intro to statistics course, we find it will be best if you have already heard of some easy statistical concepts: you will best grasp what you are being taught with this small previous knowledge.

We hope that you found our section on Statistics and Probability Courses to prepare you for tackling Machine Learning Material, useful. Once you’re done here, check out our list of resources to guide on that task: