Probability For Statistics And Data Science

Probability for improved business decisions: Introduction, Combinatorics, Bayesian Inference, Distributions

What you'll learn

Understand probability theory

Discover Combinatorics

Learn how to use and interpret Bayesian Notation

Different types of distributions variables can follow

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 understandComprehensivePracticalTo the pointBeautifully animated (with amazing video quality)Packed with plenty of exercises and resourcesThat’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 functionWe 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 itTo get there you’ll learn about unions, intersections, mutually exclusive sets, overlapping sets, conditional probability, the addition rule, and the multiplication ruleMost 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), PoissonContinuous: 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:FinanceStatisticsData ScienceIf 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 scienceComprehensive – we will cover all major probability topics and skills you need to level up your careerExtensive 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 daySuccinct – the biggest investment you’ll make is your own time. And we will not waste it. All our teaching is straight to the pointStill not convinced? Here’s why you need these skills?  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 havePromotions 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       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! 

Overview

Section 1: Introduction to Probability

Lecture 1 What does the course cover?

Lecture 2 What is the probability formula?

Lecture 3 How to compute expected values?

Lecture 4 What is a probability frequency distribution?

Lecture 5 What is a complement?

Section 2: Combinatorics

Lecture 6 Why are combinatorics useful?

Lecture 7 When do we use Permutations?

Lecture 8 Solving Factorials

Lecture 9 Why can we use certain values more than once?

Lecture 10 What if we couldn't use certain values more than once?

Lecture 11 What are combinations and how are they similar to variations?

Lecture 12 What is "symmetry" in Combinations?

Lecture 13 How do we combine combinations of events with separate sample spaces?

Lecture 14 What is the chance of a single ticket winning the lottery?

Lecture 15 A Summary of Combinatorics

Lecture 16 Practical Example: Combinatorics

Section 3: Bayesian Inference

Lecture 17 What is a set?

Lecture 18 What are the different ways two events can interact with one another?

Lecture 19 What is the intersection of sets A and B?

Lecture 20 What is the union of sets A and B?

Lecture 21 Are all complements mutually exclusive?

Lecture 22 What does it mean to for two events to be dependent?

Lecture 23 What is the difference between P(A|B) and P(B|A)?

Lecture 24 Conditional Probability in Real-Life

Lecture 25 How do we apply the additive rule?

Lecture 26 How do we derive the Multiplication Rule formula?

Lecture 27 When do we use Bayes' Theorem in Real Life?

Lecture 28 Practical Example: Bayesian Inference

Section 4: Distributions

Lecture 29 What is a probability distribution?

Lecture 30 What are the two main types of distributions based on the type of data we have?

Lecture 31 Discrete Distributions and their characteristics.

Lecture 32 What is the Discrete Uniform Distribution?

Lecture 33 What is the Bernoulli Distribution?

Lecture 34 What is the Binomial Distribution?

Lecture 35 What is the Poisson Distribution?

Lecture 36 What is a Continuous Distribution?

Lecture 37 What is a Normal Distribution?

Lecture 38 Standardizing a Normal Distribution

Lecture 39 What is a Student's T Distribution?

Lecture 40 What is a Chi Squared Distribution?

Lecture 41 What is an Exponential Distribution?

Lecture 42 What is the Logistic Distribution?

Lecture 43 Practical Example: Distributions

Section 5: Tie-ins to Other Fields

Lecture 44 Tie-ins to Finance

Lecture 45 Tie-ins to Statistics

Lecture 46 Tie-ins to Data Science

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