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.

with Viktor Mehandzhiyski

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

Skills you will gain

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 Lesson 25 Min

Course Introduction Free What is the Probability Formula Free Expected Values Free Probability Frequency Distribution Free Complements Free
Combinatorics Free

12 Lesson 43 Min

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 Lesson 54 Min

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 Lesson 33 Min

An Overview of Distributions Types of Distributions Discrete Distributions Uniform Distribution Bernoulli Distribution Binomial Distribution Poisson Distribution
Continuous Distributions

8 Lesson 41 Min

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 Lesson 18 Min

Probability in Finance Probability in Statistics Probability in Data Science

“Having a probabilistic mindset is much more important than knowing “absolute truths”, if you want to succeed in data science. I have carefully crafted this course to reflect the most in-demand skills that will enable you to understand and compute complex probabilistic concepts. This is the place where you’ll take your skillset to the next level – that of probability, conditional probability, Bayesian probability, and probability distributions.”

Viktor Mehandzhiyski

Content Creator at 365 Data Science

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