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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Section 1

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

Section 2


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
Premium course icon A Summary of Combinatorics
Premium course icon Combinatorics: Practical Example
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Section 3

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.

Premium course icon Sets and Events
Premium course icon The Different Ways Events Can Interact
Premium course icon The Intersection and Union of Two Sets
Premium course icon Mutually Exclusive Sets and Independence
Premium course icon Conditional Probability
Premium course icon Law of Total Probability
Premium course icon Additive Law
Premium course icon Multiplication Rule
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Premium course icon Bayes Rule
Premium course icon Bayesian: Practical Example
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Section 4

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.

Premium course icon An overview of distributions
Premium course icon Types of Distributions
Premium course icon Discrete Distributions
Premium course icon Discrete Uniform Distributions
Premium course icon Bernoulli Distributions
Premium course icon Binomial Distributions
Premium course icon Poisson Distributions

Section 5

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.

Premium course icon Continuous Distributions
Premium course icon Normal Distributions
Premium course icon Standardizing Normal Distributions
Premium course icon Students' T Distributions
Premium course icon Chi Squared Distributions
Premium course icon Exponential Distributions
Premium course icon Logistic Distributions
Premium course icon Probability Distributions: A Practical Example

Section 6

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.

Premium course icon Probability in Finance
Premium course icon Probability in Statistics
Premium course icon Probability in Data Science

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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