# Probability

Teaching you the probability theory necessary to think like a data scientist. You will learn about expected values, combinatorics, Bayesian notation, and probability distributions.

with Viktor Mehandzhiyski

Start course#### Course Overview

Data science is based on statistics which, in turn, steps on the foundations that probability laid out. This course will help you master the probability theory necessary to think like a data scientist. In addition, you will learn about expected values, combinatorics, Bayesian notion, and probability distributions.

#### Skills you will gain

#### What You'll Learn

Probability and statistics are essential when working with predictions. With this course, you will master probability theory and learn how to apply it as a data scientist.

#### Curriculum

- The Basics of Probability Free5 Lessons 25 Min
In this part, we explore why probability is fundamental to becoming a data scientist. We introduce you to the key terms and ideas concerning probabilities and events, including theoretical and experimental probabilities, preferred outcomes, sample space, expected value, and complements.

Course Introduction Free What is the Probability Formula Free Expected Values Free Probability Frequency Distribution Free Complements Free - Combinatorics Free12 Lessons 43 Min
This section is designed to teach you what combinatorics is and where we encounter it in life. We will consider the three central concepts in combinatorics – permutations, variations, and combinations – and you’ll learn how to calculate each of these with the correct formulas.

Fundamentals of Combinatorics Free Computing Permutations Free Solving Factorials Free Variations with Repetition Free Variations without Repetition Free Combinations without Repetition Free Combinations with Repetition Free Symmetry of Combinations Free Combinations with Separate Sample Spaces Free Winning The Lottery Free Summary of Combinatorics Free Practical Example - Combinatrics Free - Bayesian Inference12 Lessons 54 Min
Here you will learn how to describe events and the ways they interact with one another. We introduce important concepts like intersections, unions, and conditional probability. Then we focus on Bayes’ Law and how to use it to interpret the relationships between the possible outcomes of various events.

Sets and Events The Different Ways Events Can Interact The Intersection of Two Sets The Union of Two Sets Mutually Exclusive Sets Dependent and Independent Events Conditional Probability Law of Total Probability Additive Law Multiplication Rule Bayes Rule Practical Example - Bayesian Inference - Discrete Distributions7 Lessons 33 Min
In this section, you will learn to determine what kind of distribution a dataset follows. This is crucial in making accurate predictions about the future. We talk about the possible values a random variable can take and how frequently they occur. We introduce well-known distributions and events that follow them, and then proceed to discuss each common distribution in greater detail.

An Overview of Distributions Types of Distributions Discrete Distributions Uniform Distribution Bernoulli Distribution Binomial Distribution Poisson Distribution - Continuous Distributions8 Lessons 41 Min
Here, you will build upon the probability distributions knowledge you developed in the previous section. We review several of the most widely encountered continuous distributions and discuss how to determine them, where they are applied, and how to apply their formulas.

Continuous Distributions Normal Distribution Standardizing a Normal Distribution Students T Distribution Chi-Squared Distribution Exponential Distribution Logistic Distribution Practical Example - Distributions - Probability in Other Fields3 Lessons 18 Min
In this section, we spend a minute exploring the tie-ins between this field and several others, such as finance, statistics and data science.

Probability in Finance Probability in Statistics Probability in Data Science

#### “Having a probabilistic mindset is much more important than knowing “absolute truths”, if you want to succeed in data science. I have carefully crafted this course to reflect the most in-demand skills that will enable you to understand and compute complex probabilistic concepts. This is the place where you’ll take your skillset to the next level – that of probability, conditional probability, Bayesian probability, and probability distributions.”

##### Viktor Mehandzhiyski

##### Content Creator at 365 Data Science

##### Probability

with Viktor Mehandzhiyski