Intro To Probability Distributions

A gentle intro to probability distributions, extreme values, and outliers. Learn from an experienced data scientist.

What you'll learn

Understand the concept of a probability distribution

Understand how empirical simulations can converge to theoretical distribution

Unerstand why the Normal distribution is used in so many places, where it is appropriate, and where it may not be appropriate.

Understand the concept of "fat tailed" distributions and why they can generate extreme values

Critically evaluate whether "outliers" are actually outliers and whether or not they should be removed

Be familiar with specific terms used in describing data

Requirements

An understanding of the basics of probability theory.

Understand the notation of probability theory.

Description

This is a rare opportunity to learn about probability distributions from an experienced data scientist. You will get an intuitive understanding of probability distributions. Whether you aspire to be a data scientist or just a data literate leader, probability distributions are an essential concept to understand. Whatever your journey, this course will boost your data literacy. A lot of courses on probability distributions will give you 10 hours of maths. And they will teach you to pass written exams. But they won't give you the conceptual and intuitive understanding that you can use in your role. So you need a short course that will give you an intuitive and practical understanding of the concepts without burying you in equations and theory that you'll never apply in your role. You might have looked into probability distributions before. You might have taken other courses. You might have looked through books, and you might have been left wondering, how does this apply outside of the classroom? The truth is that other training material on probability distributions is not created by experienced professional data scientists. Other courses are created to teach theory that you can use to pass exams and to publish academic papers. The university professors that teach these courses are not concerned about applying their theories outside of the classroom. In your personal learning journey, you have two paths. The first path involves learning all of the theory and then working as a data scientist for 14 years to truly understand it. Or the quicker path, take a short course where an experienced data scientist explains the concepts that you need to know. If your ultimate goal is to truly understand the theory, this course is the perfect primer. It's better to have an understanding of the concepts before diving into the details of the formulas, rather than learning the formulas before understanding what they're actually measuring. So who am I? My name is Slava Razbash. I've worked in data science roles since 2011. I've worked as a data scientist at some of the largest companies in Australia. I hold a master of applied econometrics from one of Australia's top universities, Monash University. So, you can see that I might know a thing or two about the practical application of probability distributions and data literacy. This course is a gentle introduction to the concept of a probability distribution. We start with discrete probability distributions. I'll use the example of rolling a six sided die to introduce the concept. I also used the six-sided die example in my probability theory basics course. If you're completely new to probability theory, then start there. Next, we build on your knowledge to introduce continuous probability distributions. The examples become more interesting and you will learn a little bit about mathematical history. The most unique and practical section of this course is the section on extreme values and outliers, because it's a topic that's usually left out of beginner courses. Finally, there's a short lecture to define some additional terms that you will hear in the workplace. Getting started is easy, just enrol in this course. By comparison, taking a subject at a university will cost you thousands of dollars, and your professors may not have worked outside of academia. Although this course doesn't cover as much as a university subject, it's much more affordable and it's taught by a real industry expert. So you get access to over 14 years of industry experience for much less money than you would spend in a restaurant. Now, there is a caveat. If you are completely new to probability theory, then you should start with my free probability theory basics course. It's free. It was originally designed as part one of this course. Now, some people might be thinking, I'm an AI engineer. I vibe code B2B SaaS unicorns. Why do I need to know about probability distributions? Well, did you know that LLMs predict the probability distribution of the next token? Understanding how it works will make you a better unicorn vibe coder, especially in the B2B space. From my experience, I can see that you have three different paths in front of you. You can do nothing and get replaced by someone who is more data literate than you. The second option is to jump into a long theory heavy course that will teach you to pass exams. You will then need years of industry experience to understand how the theory applies outside of the classroom. Or you can enroll in this course and get an intuitive introduction to probability distributions from an experienced data scientist and quickly boost your data literacy. You might choose to continue learning and then take a long theory heavy course later. Having already done this course as a foundation, you will understand the maths better because you will already have an intuitive understanding of the concepts. So if you want to quickly leverage my 14 years of data science experience, enrol in this course.

Overview

Section 1: Introduction

Lecture 1 Prerequisites

Lecture 2 Where to find the Probability Theory Basics Course

Lecture 3 Course Overview

Section 2: Discrete Probability Distributions

Lecture 4 Discrete Probability Distributions Part 1

Lecture 5 Discrete Probability Distributions Part 2

Section 3: Continuous Probability Distributions

Lecture 6 Continuous Probability Distributions Part 1

Lecture 7 Continuous Probability Distributions Part 2

Lecture 8 Continuous Probability Distributions Part 3

Section 4: Extreme Values and Outliers

Lecture 9 Extreme Values and Outliers

Lecture 10 More on Extreme Values

Lecture 11 Even more on Extreme Values

Section 5: Additional Definitions

Lecture 12 Additional Definitions

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