Probability

with Viktor Mehandzhiyski

4.8/5

(602)

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

4 hours 47 lessons

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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.

47 High Quality Lessons

40 Practical Tasks

4 Hours of Video

Certificate of Achievement

Topics covered

Career developmentdata analysisExcelMathematics

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.

Understand key terms and ideas concerning probabilities and events

Use combinatorics

Describe events and the ways they interact with one another

Determine what kind of distribution a dataset follows

Learn about the application of the probability theory

Curriculum

The Basics of Probability Free

5 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 Free

12 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 Inference

12 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 Distributions

7 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 Distributions

8 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 Fields

3 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

Student feedback

4.8/5

602 ratings

5 stars

516 (86%)

4 stars

70 (12%)

3 stars

10 (2%)

2 stars

5 (1%)

1 star

1 (0%)

Carlton Njong

22.06.2022

This as well as the other courses I'm taking are just marvellous. I love the idea of having to answer questions after the explication of a major concept. This is an effort on the part of the 365 D.S team to ensure that I'm following along with the proper, requisite understanding for the next topics. Also, I like how everything is not given to me during the lesson and I'm constantly being reminded to do my own research, check the python documentation etc, and create and solve my own problems. I think it's a great way to make me a proactive learner. I'm a final-year computer engineering student who's been taking the data analysis course for a month now and I can definitely envision my ignorance in data science on its way to Calvary to be crucified, thanks to the D.S 365 team. Thanks guys.Absolutely worth the go.

Eric Mbogu

20.07.2022

By far the best course I have seen on this platform! My goodness. I am thoroughly impressed by the tutor. He knows how to communicate complex ideas in simple, digestible forms that gives a complete newbie a soft landing, yet firm grasp. Love it. Happy to recommend.

Tom Arundel

07.02.2023

really good. I knock off 1 star (should be 1/2 star) because the variations vs permutations definitions do not appear to be standard at least not in the UK. Variations in your terminology is what we call "permutations" so that is confusing.

David Santiago Ortiz Almanza

08.11.2022

In many videos used examples assuming the public has previous context knowledge as the cards examples. Both, the practical exercies and practical exams were significally more difficult than the examples explained. Many topics just touched.

Brad Doty

20.11.2022

Very dense and thorough coverage of probability. Moved a little fast in places. If I was new to these subjects, I think I'd get lost, but it's perfect for me to review and fill in missing pieces in my knowledge.