# Probability

Data science is based on statistics and statistics steps on the foundations laid by probability. This course will help you master the probability theory necessary to think like a data scientist. You will learn about expected values, combinatorics, Bayesian notation, and probability distributions.

##### Our graduates work at exciting places:     ## The Basics of Probability

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.

FREE What is the probability formula?
FREE Computing Expected Values
FREE The Probability Frequency Distribution
FREE Complements

## Combinatorics

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.

FREE Fundamentals of Combinatorics
FREE Computing Permutations
FREE Solving Factorials
FREE Computing Variations with Repetition
FREE Computing Variations without Repetition
FREE Computing Combinations
FREE Symmetry of Combinations
FREE Combinations with Separate Sample Spaces
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FREE Winning the Lottery A Summary of Combinatorics Combinatorics: Practical Example
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## Bayesian Inference

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 and Union of Two Sets Mutually Exclusive Sets and Independence Conditional Probability Law of Total Probability Additive Law Multiplication Rule
Show all lessons Bayes Rule Bayesian: Practical Example
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## Discrete Distributions

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 Discrete Uniform Distributions Bernoulli Distributions Binomial Distributions Poisson Distributions

## Continuous Distributions

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 Distributions Standardizing Normal Distributions Students' T Distributions Chi Squared Distributions Exponential Distributions Logistic Distributions Probability Distributions: A Practical Example

## Probability in Other Fields

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
MODULE 1

## Data Science Fundamentals This course is part of Module 1 of the 365 Data Science Program. The complete training consists of four modules, each building upon your knowledge from the previous one. Whereas the other three modules are designed to improve upon your technical skill set, Module 1 is designed to help you create a strong foundation for your data science career. You will understand the core principles of probability, statistics, and mathematics; you will also learn how to visualize your data.

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