Probability for Statistics and Data Science
4.4 (427 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
8,503 students enrolled

Probability for Statistics and Data Science

Probability for improved business decisions: Introduction, Combinatorics, Bayesian Inference, Distributions
Bestseller
4.4 (427 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
8,503 students enrolled
Created by 365 Careers
Last updated 1/2020
English
English [Auto-generated], Italian [Auto-generated]
Current price: $135.99 Original price: $194.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 3.5 hours on-demand video
  • 22 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Understand probability theory
  • Discover Combinatorics
  • Learn how to use and interpret Bayesian Notation
  • Different types of distributions variables can follow
Course content
Expand all 46 lectures 03:40:27
+ Introduction to Probability
5 lectures 26:47
What is the probability formula?
3 questions
How to compute expected values?
05:29
How to compute expected values?
3 questions
What is a probability frequency distribution?
05:00
What is a probability frequency distribution?
3 questions
What is a complement?
3 questions
+ Combinatorics
11 lectures 42:56
Why are combinatorics useful?
1 question
When do we use Permutations?
03:21
When do we use Permutations?
2 questions
Solving Factorials
03:35
Solving Factorials
3 questions
Why can we use certain values more than once?
02:59
Why can we use certain values more than once?
3 questions
What if we couldn't use certain values more than once?
03:48
Computing Variations without Repetition
3 questions
What are combinations and how are they similar to variations?
04:51
What are combinations and how are they similar to variations?
4 questions
What is "symmetry" in Combinations?
03:26
What is "symmetry" in Combinations?
1 question
How do we combine combinations of events with separate sample spaces?
02:52
How do we combine combinations of events with separate sample spaces?
1 question
What is the chance of a single ticket winning the lottery?
03:12
What is the chance of winning the lottery?
1 question
A Summary of Combinatorics
02:55
Practical Example: Combinatorics
10:53
+ Bayesian Inference
12 lectures 54:41
What is a set?
04:28
What is a set?
3 questions
What are the different ways two events can interact with one another?
03:45
What are the different ways two events can interact with one another?
2 questions
What is the intersection of sets A and B?
02:06
What is the intersection of sets A and B?
3 questions
What is the union of sets A and B?
04:51
What is the union of sets A and B?
3 questions
Are all complements mutually exclusive?
02:09
Are all complements mutually exclusive?
4 questions
What does it mean to for two events to be dependent?
03:01
What does it mean to for two events to be dependent?
3 questions
What is the difference between P(A|B) and P(B|A)?
04:16
What is the difference between P(A|B) and P(B|A)?
3 questions
Conditional Probability in Real-Life
03:03
How do we apply the additive rule?
02:21
How do we apply the additive rule?
2 questions
How do we derive the Multiplication Rule formula?
04:05
How do we interpret the Multiplication Rule Formula?
2 questions
When do we use Bayes' Theorem in Real Life?
05:44
Bayes' Theorem
2 questions
Practical Example: Bayesian Inference
14:52
+ Distributions
15 lectures 01:17:12
What is a probability distribution?
06:29
What is a probability distribution?
3 questions
What are the two main types of distributions based on the type of data we have?
07:32
What are the two main types of distributions based on the type of data we have?
2 questions
Discrete Distributions and their characteristics.
02:00
Discrete Distributions and Their Characteristics.
2 questions
What is the Discrete Uniform Distribution?
02:13
What is the Discrete Uniform Distribution?
2 questions
What is the Bernoulli Distribution?
03:26
What is the Bernoulli Distribution?
1 question
What is the Binomial Distribution?
07:04
What is the Binomial Distribution?
1 question
What is the Poisson Distribution?
05:27
What is the Poisson Distribution?
1 question
What is a Continuous Distribution?
07:12
What is a Continuous Distribution?
1 question
What is a Normal Distribution?
04:08
What is a Normal Distribution?
1 question
Standardizing a Normal Distribution
04:25
How do we Standardize a Normal Distribution?
1 question
What is a Student's T Distribution?
02:29
What is a Student's T Distribution?
1 question
What is a Chi Squared Distribution?
02:22
What is a Chi-Squared Distribution?
1 question
What is an Exponential Distribution?
03:15
What is an Exponential Distribution?
1 question
What is the Logistic Distribution?
04:07
What is a Logistic Distribution?
1 question
Practical Example: Distributions
15:03
+ Tie-ins to Other Fields
3 lectures 18:51
Tie-ins to Finance
07:46
Tie-ins to Statistics
06:18
Tie-ins to Data Science
04:47
Requirements
  • Absolutely no experience is required. We will start from the basics and gradually build up your knowledge.
  • A willingness to learn and practice
Description

Probability is probably the most fundamental skill you need to acquire if you want to be successful in the world of business. What most people don’t realize is that having a probabilistic mindset is much more important than knowing “absolute truths”.

You are already here, so actually you know that.

And it doesn’t matter if it is pure probability, statistics, business intelligence, finance or data science where you want to apply your probability knowledge…

Probability for Statistics and Data Science has your back!

This is the place where you’ll take your career to the next level – that of probability, conditional probability, Bayesian probability, and probability distributions.

You may be wondering: “Hey, but what makes this course better than all the rest?”

Probability for Statistics and Data Science has been carefully crafted to reflect the most in-demand skills that will enable you to understand and compute complicated probabilistic concepts. This course is:

  • Easy to understand

  • Comprehensive

  • Practical

  • To the point

  • Beautifully animated (with amazing video quality)

Packed with plenty of exercises and resources


That’s all great, but what will you actually learn? Probability. And nothing less.


To be more specific, we focus on the business implementation of probability concepts. This translates into a comprehensive course consisting of:

  • An introductory part that will acquaint you with the most basic concepts in the field of probability: event, sample space, complement, expected value, variance, probability distribution function

  • We gradually build on your knowledge with the first widely applicable formulas:

  • Combinatorics or the realm of permutations, variations, and combinations. That’s the place where you’ll learn the laws that govern “everyday probability”

  • Once you’ve got a solid background, you’ll be ready for some deeper probability theory – Bayesian probability.

  • Have you seen this expression: P(A|B) = P(B|A)P(A)/P(B) ? That’s the Bayes’ theorem – the most fundamental building block of Bayesian inference. It seems complicated but it will take you less than 1 hour to understand not only how to read it, but also how to use it and prove it

  • To get there you’ll learn about unions, intersections, mutually exclusive sets, overlapping sets, conditional probability, the addition rule, and the multiplication rule


Most of these topics can be found online in one form or another. But we are not bothered by that because we are certain of the outstanding quality of teaching that we provide.

What we are really proud of, though, is what comes next in the course. Distributions.

Distributions are something like the “heart” of probability applied in data science. You may have heard of many of them, but this is the only place where you’ll find detailed information about many of the most common distributions.

  • Discrete: Uniform distribution, Bernoulli distribution, Binomial distribution (that’s where you’ll see a lot of the combinatorics from the previous parts), Poisson

  • Continuous: Normal distribution, Standard normal distribution, Student’s T, Chi-Squared, Exponential, Logistic

Not only do we have a dedicated video for each one of them, how to determine them, where they are applied, but also how to apply their formulas.

Finally, we’ll have a short discussion on 3 of the most common places where you can stumble upon probability:

  • Finance

  • Statistics

  • Data Science


    If that’s not enough, keep in mind that we’ve got real-life cases after each of our sections. We know that nobody wants to learn dry theory without seeing it applied to real business situations so that’s in store, too!

We think that this will be enough to convince you curriculum-wise. But we also know that you really care about WHO is teaching you, too. 

Teaching is our passion  

We worked hard for over four months to create the best possible Probability course that would deliver the most value to you. We want you to succeed, which is why the course aims to be as engaging as possible. High-quality animations, superb course materials, quiz questions, handouts and course notes, are just some of the perks you will get. What else?

Exceptional Q&A support. Yes. That’s our favorite part – interacting with you on the various topics you learn about (and you are going to love it, too!)

What makes this course different from the rest of the Probability courses out there?  

  • High-quality production – HD video and animations (This isn’t a collection of boring lectures!)

  • Knowledgeable instructor (an adept mathematician who has competed at an international level) who will bring you not only his probability knowledge but the complicated interconnections between his areas of expertise – finance and data science

  • Comprehensive – we will cover all major probability topics and skills you need to level up your career

  • Extensive Case Studies - helping you reinforce everything you’ve learned  

  • Exceptional support – we said that, but let’s say it again - if you don’t understand a concept or you simply want to drop us a line, you’ll receive an answer within 1 business day

  • Succinct – the biggest investment you’ll make is your own time. And we will not waste it. All our teaching is straight to the point

    Still not convinced?

Here’s why you need these skills?  

  1. Salary/Income – most businesses are starting to realize      the advantages of implementing data-driven decisions. And those are all stepping on probability. A probabilistic mindset is definitely one of the non-automatable skills that managers of the next decade will be  expected to have

  2. Promotions and secure future – If you understand probability well, you will be able to back up your business and positions in much more convincing way, draining from quantitative evidence; needless to say, that’s the path to career growth       

  3. New horizons – probability is a pathway to many positions in any industry. While it is rarely a full-time position, it is crucial for most business jobs nowadays. And it’s not a boring aspect!

Please bear in mind that the course comes with Udemy’s 30-day money-back guarantee. And why not give such a guarantee? We are certain this course will provide a ton of value for you.  

Let's start learning together now!

 

Who this course is for:
  • People who want a career in Data Science
  • People interested in a Business Intelligence career
  • Business analysts
  • Business executives
  • Individuals who are passionate about numbers and quant analysis
  • Anyone who wants to learn the subtleties of Probability and how it is used in the business world
  • People who want to start learning probability
  • People who want to learn the fundamentals of probability
  • People who wish to extract insights from summarized statistics to understand academic papers