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Probability for Statistics and Data Science


Probability for Statistics and Data Science

Probability and Statistics form the basis of Data Science. The probability theory is very much helpful for making the prediction. Estimates and predictions form an important part of Data science. ... Thus, statistical methods are largely dependent on the theory of probability. Bestseller

What you'll learn

  • Understand probability theory
  • Discover Combinatorics
  • Learn how to use and interpret Bayesian Notation
  • Different types of distributions variables can follow


Probability is probably the most fundamental skill you need to acquire if you want to be successful in the world of business. What most people don’t realize is that having a probabilistic mindset is much more important than knowing “absolute truths”.

You are already here, so actually you know that.

And it doesn’t matter if it is pure probability, statistics, business intelligence, finance or data science where you want to apply your probability knowledge…

Probability for Statistics and Data Science has your back!

This is the place where you’ll take your career to the next level – that of probability, conditional probability, Bayesian probability, and probability distributions.

You may be wondering: “Hey, but what makes this course better than all the rest?”

Probability for Statistics and Data Science has been carefully crafted to reflect the most in-demand skills that will enable you to understand and compute complicated probabilistic concepts. This course is:

  • Easy to understand
  • Comprehensive
  • Practical
  • To the point
  • Beautifully animated (with amazing video quality)

Packed with plenty of exercises and resources

That’s all great, but what will you actually learn? Probability. And nothing less.

To be more specific, we focus on the business implementation of probability concepts. This translates into a comprehensive course consisting of:

  • An introductory part that will acquaint you with the most basic concepts in the field of probability: event, sample space, complement, expected value, variance, probability distribution function
  • We gradually build on your knowledge with the first widely applicable formulas:
  • Combinatorics or the realm of permutations, variations, and combinations. That’s the place where you’ll learn the laws that govern “everyday probability”
  • Once you’ve got a solid background, you’ll be ready for some deeper probability theory – Bayesian probability.
  • Have you seen this expression: P(A|B) = P(B|A)P(A)/P(B) ? That’s the Bayes’ theorem – the most fundamental building block of Bayesian inference. It seems complicated but it will take you less than 1 hour to understand not only how to read it, but also how to use it and prove it
  • To get there you’ll learn about unions, intersections, mutually exclusive sets, overlapping sets, conditional probability, the addition rule, and the multiplication rule

Most of these topics can be found online in one form or another. But we are not bothered by that because we are certain of the outstanding quality of teaching that we provide.

What we are really proud of, though, is what comes next in the course. Distributions.

Distributions are something like the “heart” of probability applied in data science. You may have heard of many of them, but this is the only place where you’ll find detailed information about many of the most common distributions.

  • Discrete: Uniform distribution, Bernoulli distribution, Binomial distribution (that’s where you’ll see a lot of the combinatorics from the previous parts), Poisson
  • Continuous: Normal distribution, Standard normal distribution, Student’s T, Chi-Squared, Exponential, Logistic

Not only do we have a dedicated video for each one of them, how to determine them, where they are applied, but also how to apply their formulas.

Finally, we’ll have a short discussion on 3 of the most common places where you can stumble upon probability:

  • Finance
  • Statistics
  • Data Science

    If that’s not enough, keep in mind that we’ve got real-life cases after each of our sections. We know that nobody wants to learn dry theory without seeing it applied to real business situations so that’s in store, too!

We think that this will be enough to convince you curriculum-wise. But we also know that you really care about WHO is teaching you, too. 

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