Probability And Statistics Masterclass – Making Maths Fun

Free Download Probability And Statistics Masterclass – Making Maths Fun
Last updated 2/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 8.02 GB | Duration: 20h 15m
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
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