Probability And Statistics Masterclass - Making Maths Fun.

Learn everything from Probability, Statistics, Permutation and Combination with walk along practice questions

What you'll learn

Visualizing data, including bar graphs, pie charts, histograms

Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores

Analyzing data, including mean, median, and mode, plus range and IQR and box plots

Different types of distribution - Bernoulli, Binomial, Uniform, Normal, Pareto, Chi square, Hypothesis Testing, Central limit theorem

Probability, including union vs. intersection and independent and dependent events and Bayes' theorem

Permutation with examples

Combination with examples

Different types of distributions - Uniform, Log Normal, Pareto, Normal, Binomial, Bernoulli Chi Square distribution and Goodness of Fit

Central Limit Theorem

Hypothesis Testing

Requirements

Foundation in Mathematics

Description

PROBABILITY & STATISTICS MASTERCLASS IS SET UP TO MAKE LEARNING FUN AND EASYThis 100+ lesson course includes 20+ hours of high-quality video and text explanations of everything from Probability, Statistics, Permutation and Combination. Topic is organized into the following sections:Data Type - Random variable, discrete, continuous, categorical, numerical, nominal, ordinal, qualitative and quantitative data typesVisualizing data, including bar graphs, pie charts, histograms, and box plotsAnalyzing data, including mean, median, and mode, IQR and box-and-whisker plotsData distributions, including standard deviation, variance, coefficient of variation, Covariance and Normal distributions and z-scoresDifferent types of distributions - Uniform, Log Normal, Pareto, Normal, Binomial, BernoulliChi Square distribution and Goodness of FitCentral Limit TheoremHypothesis TestingProbability, including union vs. intersection and independent and dependent events and Bayes' theorem, Total Law of ProbabilityHypothesis testing, including inferential statistics, significance levels, test statistics, and p-valuesPermutation with examplesCombination with examplesExpected Value.AND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:We will start with basics and understand the intuition behind each topicVideo lecture explaining the concept with many real life examples so that the concept is drilled inWalkthrough of worked out examples to see different ways of asking question and solving themLogically connected concepts which slowly builds up Enroll today ! Can't wait to see you guys on the other side and go through this carefully crafted course which will be fun and easy.YOU'LL ALSO GET:Lifetime access to the courseFriendly support in the Q&A sectionUdemy Certificate of Completion available for download30-day money back guarantee

Overview

Section 1: Getting Started

Lecture 1 Introduction

Section 2: Visualizing Data

Lecture 2 What is a random variable

Lecture 3 Nominal and Ordinal Data

Lecture 4 Introduction to Central Tendency

Lecture 5 Central Tendency - Examples

Lecture 6 Data Visualization

Lecture 7 Types of Quartiles, Inter Quartile Range, Percentiles

Lecture 8 Types of Quartile, Inter Quartile Range - Example

Section 3: Basics of Statistics

Lecture 9 Standard Devation & Variance

Lecture 10 Sample Standard Deviation

Lecture 11 Co-variance

Lecture 12 Normal Distribution

Lecture 13 Chi Square Distribution

Lecture 14 Chi Square Goodness of Fit

Lecture 15 Association between Categorical variables

Lecture 16 Correlation

Section 4: Advanced Statistics

Lecture 17 Probability Mass Function

Lecture 18 Probability Distribution Function

Lecture 19 Bernoulli Distribution

Lecture 20 Binomial Distribution

Lecture 21 Expected Value

Lecture 22 Expected Value - Example

Lecture 23 Expected Value for Bernoulli Distribution

Lecture 24 Expected Value for Binomial Distribution

Lecture 25 Law of large numbers

Lecture 26 Normal Distribution and its properties

Lecture 27 Impact of standard deviation on the PDF

Lecture 28 Cumulative Distribution Function

Lecture 29 Formula of Normal Distribution

Lecture 30 Understanding Normal Distribution through excel

Lecture 31 Normal Standard deviation

Lecture 32 Extreme values in normal distribution

Lecture 33 Z score Introduction

Lecture 34 Z score detailed explanation

Lecture 35 How to read a z score table

Lecture 36 Using z score - Example 1

Lecture 37 Using z score - Example 2

Lecture 38 Using z score - Example 3

Lecture 39 Using z score - Example 4

Lecture 40 Symmetric Distribution and Skewness

Lecture 41 Central Limit Theorem - Introduction

Lecture 42 Central Limit Theorem - Revisiting

Lecture 43 Central Limit Theorem - Conclusion

Lecture 44 Central Limit Theorem - Solved Example 1

Lecture 45 Central Limit Theorem - Solved Example 2

Lecture 46 Uniform Distribution

Lecture 47 Log Normal Distribution

Lecture 48 Log Normal Distribution - Examples

Lecture 49 Power Law Distribution

Lecture 50 Pareto Distribution

Lecture 51 Pareto Distribution Formula

Lecture 52 Q-Q plot

Lecture 53 Box Cox Transformation

Lecture 54 How distributions are used

Lecture 55 Introduction to Null Hypothesis

Lecture 56 Confidence Interval - Example 1

Lecture 57 Confidence Interval - Example 2

Lecture 58 z table vs t table

Lecture 59 Hypothesis Testing - Example 1

Lecture 60 Hypothesis Testing - Example 2

Lecture 61 Hypothesis Testing - Example 3

Lecture 62 Concluding Hypothesis Testing

Section 5: Introduction to Probability

Lecture 63 Introduction to Probability

Lecture 64 Laws of Probability - Conditional Probability & Bayes Theorem

Lecture 65 Laws of Probability - Mutually Exclusive and Independent Events

Lecture 66 Probability - Examples to Practice - Part1

Lecture 67 Probability - Examples to Practice - Part2

Lecture 68 Chain Rule of Probability

Lecture 69 Chain Rule of Probability - Example

Lecture 70 Chain Rule of Probability - Cards Example

Lecture 71 Birthday Paradox

Lecture 72 Probability - More Examples - Part 1

Lecture 73 Probability - More Examples - Part 2

Lecture 74 Probability - More Examples - Part3

Lecture 75 Probability Trees

Lecture 76 Total Law of Probability

Lecture 77 Total Law of Probability - Example

Lecture 78 Probability - More Examples

Lecture 79 Total Law of Probability - More Examples

Section 6: Introduction to Permutation and Combination through Examples

Lecture 80 Permutation & Combinations - Introduction

Lecture 81 Permutation & Combinations - Examples - Part 1

Lecture 82 Permutation & Combinations - Examples - Part 2

Lecture 83 Permutation & Combinations - Examples - Part 3

Lecture 84 Permutation & Combinations - Examples - Part 4

Lecture 85 Permutation & Combinations - Examples - Part 5

Lecture 86 Permutation & Combinations - Examples - Part 7

Section 7: Permutation through Formula

Lecture 87 Factorial Notation

Lecture 88 Introduction to Permutation - Part 1

Lecture 89 Introduction to Permutation - Part 2

Lecture 90 Introduction to Permutation - Part 3

Lecture 91 Permutation - Examples 1

Lecture 92 Permutation - Part 3 - Examples 1

Lecture 93 Permutation - Part 3 - Examples 2

Lecture 94 Permutation - Examples 2

Lecture 95 Permutation - Examples 3

Lecture 96 Introduction to Permutation - Part 4

Section 8: Introduction to Combination

Lecture 97 Introduction to Combinations

Lecture 98 Combination - More Examples 1

Lecture 99 Combination - More Examples 2

Lecture 100 Combination - More Examples 3

Section 9: Expected Value

Lecture 101 What is Expected Value

Lecture 102 Expected Value - Example1

Lecture 103 Properties of Expected Value - Part1

Lecture 104 Properties of Expected Value - Part2

Lecture 105 Properties of Expected Value - Part3

Lecture 106 Properties of Expected Value - Part4

Lecture 107 Expected Value - Example2

Lecture 108 Expected Value - Example3

Lecture 109 Expected Value - Example4

Section 10: Miscellaneous

Lecture 110 Probability - Examples 1

Lecture 111 Probability - Examples 2

Lecture 112 Probability - Examples 3

Students currently studying probability and statistics or students about to start probability and statistics,Anyone who wants to study math for fun,Anyone wanting to learn foundational Maths for Data Science