# Probability

4.8/5
(1,161)

Learn probability fundamentals to think like a data scientist and unlock analytical insights

5 hours of content 26829 students

\$99.00

14-Day Money-Back Guarantee

What you get:

• 5 hours of content
• Interactive exercises
• World-class instructor
• Closed captions
• Q&A support
• Course exam
• Certificate of achievement

# Probability

\$99.00

14-Day Money-Back Guarantee

What you get:

• 5 hours of content
• Interactive exercises
• World-class instructor
• Closed captions
• Q&A support
• Course exam
• Certificate of achievement

\$99.00

14-Day Money-Back Guarantee

What you get:

• 5 hours of content
• Interactive exercises
• World-class instructor
• Closed captions
• Q&A support
• Course exam
• Certificate of achievement

## What You Learn

• Acquire foundational knowledge in probability theory
• Become familiar with key probability terms and ideas
• Explore the practical applications of probability theory
• Be able to determine what kind of distribution a dataset follows
• Learn how to describe events and analyze their interactions
• Use and interpret Bayesian notation

## Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

## Course Description

1.1 Course Introduction

3 min

1.2 What is the Probability Formula

7 min

1.4 Expected Values

5 min

1.6 Probability Frequency Distribution

5 min

1.8 Complements

5 min

2.1 Fundamentals of Combinatorics

1 min

## Interactive Exercises

Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

## Curriculum

• 1. The Basics of Probability
5 Lessons 25 Min

In this part of the Probability for data science course, 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
3 min
What is the Probability Formula
7 min
Expected Values
5 min
Probability Frequency Distribution
5 min
Complements
5 min
• 2. Combinatorics
12 Lessons 44 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
1 min
Computing Permutations
3 min
Solving Factorials
4 min
Variations with Repetition
3 min
Variations without Repetition
4 min
Combinations without Repetition
5 min
1 min
Symmetry of Combinations
3 min
Combinations with Separate Sample Spaces
3 min
Winning The Lottery
3 min
Summary of Combinatorics
3 min
Practical Example - Combinatrics
11 min
• 3. 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
4 min
The Different Ways Events Can Interact
4 min
The Intersection of Two Sets
2 min
The Union of Two Sets
5 min
Mutually Exclusive Sets
2 min
Dependent and Independent Events
3 min
Conditional Probability
4 min
Law of Total Probability
3 min
2 min
Multiplication Rule
4 min
Bayes Rule
6 min
Practical Example - Bayesian Inference
15 min
• 4. Discrete Distributions
7 Lessons 33 Min

In this section of the probability for data science course, 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
6 min
Types of Distributions
8 min
Discrete Distributions
2 min
Uniform Distribution
2 min
Bernoulli Distribution
3 min
Binomial Distribution
7 min
Poisson Distribution
5 min
• 5. Continuous Distributions
8 Lessons 41 Min

Here, you will build upon the probability distributions knowledge you developed in the previous section of the probability class. 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
7 min
Normal Distribution
4 min
Standardizing a Normal Distribution
4 min
Students T Distribution
2 min
Chi-Squared Distribution
2 min
Exponential Distribution
3 min
Logistic Distribution
4 min
Practical Example - Distributions
15 min
• 6. 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
8 min
Probability in Statistics
6 min
Probability in Data Science
4 min

## Topics

probabilityCombinatoricsBayesian InferenceBayes TheoremProbability Distributions

## Course Requirements

• No prior experience or knowledge is required. We will start from the basics and gradually build your understanding. Everything you need is included in the course.

## Who Should Take This Course?

Level of difficulty: Beginner

• People who want to improve their decision-making skills
• Aspiring data analysts, data scientists, business analysts
• Graduate students who need probability for their studies

## Exams and Certification

Data Scientist at NielsenIQ

3 Courses

2443 Reviews

57182 Students

A Hamilton College graduate, Viktor has a strong analytics background, focusing on the fields of Statistics, Econometrics, Financial Time-Series Econometrics, and Behavioral Economics. Viktor’s coding experience is rather diverse – from working with C, C++, and Python through to the more math/econ-oriented MATLAB and STATA. He has been fascinated by coding algorithms since the age of 11 and describes himself as a “Bachelor of Science and overall cool guy”. We couldn’t agree more. Some of Viktor’s personal achievements include developing a model for forecasting transfer prices of soccer players across Europe’s top divisions and Stock Market Indexes analysis on the effects of contagion on the effectiveness of international portfolio diversification.

## What Our Learners Say

18.09.2024
Excellent, so happy I took this course!
17.09.2024
15.09.2024

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