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.

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create free accountIn 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.

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What is the probability formula?

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Computing Expected Values

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The Probability Frequency Distribution

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Complements

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.

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Fundamentals of Combinatorics

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Computing Permutations

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Solving Factorials

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Computing Variations with Repetition

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Computing Variations without Repetition

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Computing Combinations

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Symmetry of Combinations

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Combinations with Separate Sample Spaces

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Winning the Lottery

A Summary of Combinatorics

Combinatorics: Practical Example

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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

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Bayes Rule

Bayesian: Practical Example

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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

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

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

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.

See All ModulesReal-life project and data. Solve them on your own computer as you would in the office.

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