Probability
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.
4 hours 
36 exercises 
3 assignments 
37 quizzes 
22 downloadables
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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.
Section 2
Combinatorics
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.
A Summary of Combinatorics
Combinatorics: Practical Example
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.
Sets and Events
The Different Ways Events Can Interact
The Intersection and Union of Two Sets
Mutually Exclusive Sets and Independence
Conditional Probability
Law of Total Probability
Additive Law
Multiplication Rule
Bayes Rule
Bayesian: Practical Example
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.
An overview of distributions
Types of Distributions
Discrete Distributions
Discrete Uniform Distributions
Bernoulli Distributions
Binomial Distributions
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.
Continuous Distributions
Normal Distributions
Standardizing Normal Distributions
Students' T Distributions
Chi Squared Distributions
Exponential Distributions
Logistic Distributions
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.
Probability in Finance
Probability in Statistics
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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Why Choose the 365 Data Science Program?
Practice
Real-life project and data. Solve them on your own computer as you would in the office.
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Trust the other 500,000 students
The course is in-depth and is delivered at a steady pace with eye catching visuals. The instructors go through all the basics really well. They try not to over-simplify the material, you get a good sense аof how deep Data Science is in the course. Great job!!!
This course is amazing! After watching the video carefully and doing all the exercises, I am even capable of having discussions with Machine learning major Master’s students! High standard course with reasonable pricing.
Very clear and in-depth explanation of data science and how all the inter-related concepts apply in real life business environment. Absolutely great for beginners! Best data science course I have come across so far!
I would highly recommend the course to any beginner who wants to venture into the world of Data Science. The concepts are very well explained and there is an emphasis on practical application which really helps create a better understanding of the concepts.
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