Can I really get the best courses for probability online?

Nowadays, when you go on the internet, you find so many *options* to choose from.

This only makes you wonder if you can even get the best probability courses.

Probability, which is also known as **probability theory in application to mathematics**, is the *measurement* of the possibility of a particular outcome.

And yes you can get these **best probability courses on Coursera**.

In this article, I have outlined some of these best Coursera courses for probability that will not only sharpen your *skills*, but also help you *learn* about probability in an engaging and effective online learning environment complete with video tutorials, quizzes, and more.

Are you interested in also learning project management?

If yes, then you should check out my previous article where I reviewed the best Coursera courses for project management.

Here are quick links to these courses on Coursera…

COURSES | STUDENTS |
---|---|

1. Game Theory | 291, 344 |

2. Basic Statistics | 199, 269 |

3. Introduction to Probability and Data with R | 189, 749 |

4. Data Science Math Skills | 198, 624 |

5. Introduction to Logic | 157, 162 |

6. Divide and Conquer, Sorting and Searching, and Randomized Algorithms | 142, 512 |

7. Fundamentals of Quantitative Modeling | 123, 156 |

8. Statistical Inference | 122, 165 |

9. Bayesian Statistics: From Concept to Data Analysis | 102, 983 |

10. Introduction to Spreadsheets and Models | 81, 405 |

11. Mathematical Biostatistics Boot Camp 1 | 47, 622 |

Here is a detailed summary of what you’ll learn in each of these probability courses on Coursera.

We’ll look at what each course is about, what you’ll be able to do after the course as well as the requirements and skill level you need to have before starting any of these courses

## 1. Game Theory

Popularized by movies such as “A Beautiful Mind,” game theory is the **mathematical modeling of strategic interaction** among rational (and irrational) agents.

Beyond that, you can also call ‘*games*‘ in common language, such as *chess*, *poker*, *soccer*, etc., it includes:

- The modeling of conflict among nations
- Political campaigns
- Competition among firms
- Trading behavior in markets such as the NYSE.

How could you begin to model keyword *auctions* and peer to peer file-sharing *networks*, without accounting for the incentives of the people using them?

This best selling **probability course will provide you with the basics**: representing games and strategies, the extensive form (which computer scientists call game trees), Bayesian games (modeling things like auctions), repeated and stochastic games, and more.

The instructors will also include a variety of examples including classic games and a few applications.

You can find a full syllabus and description of the course here.

There is also an advanced follow-up course to this one, for people already familiar with game theory.

**Rating**: 4.6 stars (3, 3273 ratings).

**Students**: 291, 344.

**Level**: Beginner.

**Duration**: 17 hours.

**Language**: English.

**Subtitle**: English, Chinese (Simplified)

## 2. Basic Statistics

While understanding statistics, **it is also essential to understand research in the social and behavioral sciences**.

In this statistics course on Coursera, you will learn the basics of statistics, and not just how to calculate them, but also **how to evaluate them**.

This course will also prepare you for the next course in the specialization – the course Inferential Statistics.

In the first part of the course, the tutors will discuss methods of descriptive statistics. Here, you will **learn what cases and variables are** and how you can compute measures of central tendency (*mean, median,* and *mode*) and dispersion (*standard deviation* and *variance*).

The second part of the course is concerned with the *basics* of probability: calculating probabilities, probability distributions, and sampling distributions.

The third part of the course consists of an *introduction* to methods of inferential statistics, methods that help us decide whether the patterns we see in our data are strong enough to draw conclusions about the underlying population we are *interested* in.

You will not only learn about all these statistical concepts, but you will also be **trained to calculate and generate these statistics yourself** using freely available statistical software.

Since this course gives us more details about the basics statics, it has earned its spot as one of the best probability courses on Coursera.

**Rating**: 4.6 stars (3, 284 ratings).

**Students**: 199, 269.

**Level**: Beginner.

**Duration**: 27 hours.

**Language**: English.

**Subtitle**: English, Vietnamese, German

## 3. Introduction to Probability and Data with R

This Coursera course for probability **introduces you to sampling and exploring data**, as well as basic probability theory and Bayes’ rule.

You will examine *various* types of sampling methods, and discuss how such methods can *impact* the scope of inference.

A variety of exploratory data analysis techniques will also be covered in this course by the Instructor, including **numeric summary statistics and basic data visualization**.

Additionally, you will be guided through *installing* and using R and RStudio (free statistical software). You will then use this software for lab exercises and a final project.

The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.

By the end of this probability course on Coursera, you will have skills in R programming and R studio.

**Rating**: 4.7 stars (4, 340).

**Students**: 198, 624.

**Level**: Beginner.

**Duration**: 15 hours.

**Language**: English.

**Subtitle**: English, Korean

## 4. Data Science Math Skills

Did you know that data science courses contain math?

Unfortunately, there’s no avoiding that!

This course for probability on Coursera is **designed to teach you the basic math** you need in order to be successful in your data science career.

It was created for learners who have *basic* math skills but may not have taken algebra or pre-calculus.

Data Science Math Skills introduces **the core math that data science is built upon**, with no extra *complexity*, introducing unfamiliar ideas and math symbols *one-at-a-time*.

After completing this course, you will *master* the vocabularies, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material.

While this course is intended as a general introduction to the math skills needed for data science, it can be considered a prerequisite for learners interested in the course “*Mastering Data Analysis in Excel*,” which is part of the Excel to MySQL Data Science Specialization.

If you master Data Science Math Skills, you will be fully prepared for success with the more advanced math concepts introduced in “Mastering Data Analysis in Excel.”

**Rating**: 4.5 stars (7, 608 ratings).

**Students**:198, 624.

**Level**: Beginner.

**Duration**: 13 hours.

**Language**: English.

**Subtitle**: French, Portuguese (Brazilian), Russian, English, Spanish.

## 5. Introduction to Logic

This Coursera course for probability is **an introduction to Logic from a computational perspective**.

This course will show you:

- How to encode information in the form of logical sentences
- How to reason with information in this form
- An overview of logic technology and its applications in mathematics, science, engineering, business, law, and so forth.

The course will also **help you understand the fundamentals of mathematical logic**.

By the end of this course, you will have gained *skills* in relational algebra, problem-solving, and propositional calculus.

Even though this is a great course, it has some cons that need to be fixed. For example, some parts are just too technical and are rather for technicians and not for persons looking more into the theoretical aspects.

Apart from that, this course earned it’s a spot here as one of the best courses for probability on Coursera.

**Rating**: 4.5 stars (563 ratings).

**Students**: 157, 162.

**Level**: Intermediate.

**Duration**: 54 hours.

**Language**: English.

**Subtitle**: Arabic, Chinese (Simplified), Greek, Portuguese (Brazilian), Afrikaans, German, Turkish, English, Spanish, Polish.

## 6. Divide and Conquer, Sorting and Searching, and Randomized Algorithms

The primary topics in this part of the specialization are:

- Asymptotic (“
*Big-oh*“) notation, - Sorting and searching,
- Divide and conquer (master method, integer, and matrix multiplication, closest pair), and
- Randomized algorithms (QuickSort, contraction algorithm for min cuts).

By the end of this probability specialization on Coursera, you will **gain skills to help you in your daily real-life situations** like sorting algorithms.

The instructor will also take you through Linear-time selection; graphs, cuts, and the contraction algorithm.

In this specialization, the instructor explains everything in *much* detail and *precision* making it another best probability course on Coursera.

Finally, you’ll go through the QuickSort algorithm and its analysis.

This is a well-researched course that has the topics covered well, with a walkthrough for example cases for each newly introduced algorithm.

You will **walk away with some great experience** and learn a lot of important algorithms and algorithmic thinking practices.

**Rating**: 4.8 stars (4, 140 ratings).

**Students**: 142, 512.

**Level**: Intermediate.

**Duration**: 17 hours.

**Language**: English.

**Subtitle**: French, Portuguese (Brazilian), Russian, English, Spanish

## 7. Fundamentals of Quantitative Modeling

How can you **put data to work for you**?

Specifically, how can numbers in a spreadsheet tell us about the *present* and *past* business activities?

How can we use them to *forecast* the future?

The answer is in building quantitative models, and this probability course is designed to help you **understand the fundamentals** of this critical, foundational, business skill.

Through a series of short lectures, demonstrations, and assignments, you’ll **learn the key ideas** and processes of quantitative modeling so that you can begin to *create* your own models for your own business or enterprise.

By the end of this course, you will have seen a *variety* of practical *commonly* used quantitative models as well as the building blocks that will allow you to start structuring your own models.

These building blocks will be put to use in the other courses in this Specialization.

**Rating**: 4.6 stars (6, 449 ratings).

**Students**: 123, 156.

**Level**: Beginner.

**Duration**: 8 hours.

**Language**: English.

**Subtitle**: French, Portuguese (Brazilian), Russian, English, Spanish

## 8. Statistical Inference

Statistical inference is **the process of drawing conclusions about populations or scientific truths** from data.

There are *many* modes of performing inference, including:

- Statistical modeling
- Data-oriented strategies
- Explicit use of designs and randomization in analyses

Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design-based), and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference.

A practitioner can often be left in an *exhausting* maze of techniques, philosophies, and nuance.

This course **presents the fundamentals of inference in a practical approach** to getting things done.

After taking this course, you will understand the *broad* directions of statistical inference and use this information for making *informed* choices in analyzing data.

**Rating**: 4.2 stars (4.2 ratings).

**Students**: 122, 165.

**Level**: Beginner.

**Duration**: 55 hours.

**Language**: English.

**Subtitle**: French, Portuguese (Brazilian), Vietnamese, Korean, Russian, English, Spanish

## 9. Bayesian Statistics: From Concept to Data Analysis

This Coursera course for probability **introduces the Bayesian approach to statistics**, starting with the *concept* of probability and moving to the analysis of data.

You will **learn about the philosophy of the Bayesian approach** as well as how to implement it for common types of data.

Additionally, you will also *compare* the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the *benefits* of the Bayesian approach.

In particular, the Bayesian approach allows for *better* accounting of uncertainty, results that have more *intuitive* and *interpretable* meaning, and more explicit statements of assumptions.

This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience making it one of the best Coursera courses for probability.

For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options.

**Rating**: 4.6 stars (2, 430 ratings).

**Students**: 81, 405.

**Level**: Intermediate.

**Duration**: 12 hours.

**Language**: English.

**Subtitle**: French, Portuguese (Brazilian), Russian, English, Spanish

## 10. Introduction to Spreadsheets and Models

The simple spreadsheet is **one of the most powerful data analysis tools that exist**, and it’s available to almost *anyone*.

Major corporations and small businesses alike use spreadsheet models to *determine* where key measures of their success are *now*, and where they are likely *to be* in the future.

But in order to get the most out of a spreadsheet, **you have to know how to use it**.

This probability and statistics course is **designed to give you an introduction to basic spreadsheet tools and formulas.**

So that you can begin harnessing the *power* of spreadsheets to map the data you have now and to predict the data you may have in the future.

Through short, *easy-to-follow* demonstrations, you’ll **learn how to use Excel or Sheets** so that you can begin to build models and decision trees in future courses in this Specialization.

Basic familiarity with, and access to, Excel or Sheets is *required*.

**Rating**: 4.2 stars (3, 166 ratings).

**Students**: 81, 405.

**Level**: Beginner.

**Duration**: 6 hours.

**Language**: English.

**Subtitle**: English, Portuguese (Brazilian)

## 11. Mathematical Biostatistics Boot Camp 1

This specialization on Coursera for probability **presents the fundamental probability and statistical concepts** used in elementary data analysis.

It starts with Mathematical Statistics Bootcamp, specifically **concepts, and methods used in biostatistics applications**. These range from *probability*, *distribution*, and *likelihood* concepts to hypothesis testing and case-control sampling.

This specialization will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus.

A small amount of linear algebra and programming are *useful* for the course, but not required.

The instructor will also cover the:

- Conditional Probability
- Bayes’ Rule
- Likelihood
- Distributions
- Asymptotics

… which are the most fundamental *core* concepts in mathematical biostatistics and statistics.

Finally, you’ll also learn about binomial proportions and logs.

**Rating**: 4.5 stars (305 ratings).

**Students**: 47, 622.

**Level**: Intermediate.

**Duration**: 13 hours.

**Language**: English.

**Subtitle**: French, Portuguese (Brazilian), Russian, English, Spanish

## Conclusion

Get **an introduction to probability** with these Coursera courses for probability from major universities and institutions.

In this article, I’ve offered both *individual* courses and *advanced* programs designed to help you learn about probability and statistics in an engaging and effective online learning environment complete with video tutorials, quizzes, and more.

Additionally, you can **earn verified certificates in probability** and other mathematics disciplines from these courses on Coursera, proof for teachers, employers, and others of successful completion of the coursework.

These courses will help you:

- Learn about binomial proportions and logs
- Learn how to use Excel or Sheets so that you can begin to build models and decision trees
- Understand the fundamentals of this critical, foundational, business skill
- Learn about the philosophy of the Bayesian approach

I hope these probability courses on Coursera help you learn the ins and outs of probability and launch a successful career in this lucrative field.

Have you ever taken any of these best Coursera courses for probability before?

If yes, please share your experience in the comments below.