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.
Hours

4

Lessons

47

Quizzes

37

Assignments

0

Course description

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.
FREE
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.

4

Descrete 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.

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.