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 as well as probability distributions.
4 hours
36 exercises
3 assignments
37 quizzes
22 downloadables Sign up to
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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 3 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 a 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 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 build up on 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 up on your knowledge from the previous one. Whereas the other three modules are designed to improve upon your technical skillset, 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.
See All Modules 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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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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