Probability And Statistics: Complete Course 2023

Learn the Probability and Statistics You Need to Succeed in Data Science and Business Analytics

What you'll learn

Descriptive Statistics

Visualizing Data

Probability Theory

Bayesian Statistics

Discrete Distributions (Binomial, Poisson and More)

Continuous Distributions (Normal and Others)

Hypothesis Tests

Regression

Type I and Type II Errors

Chi-Squared Test

Requirements

No pre-requisites for most of the course. One small optional section requires knowledge and calculus, but other than that this is suitable for beginners.

Description

This is course designed to take you from beginner to expert in probability and statistics. It is designed to be practical, hands on and suitable for anyone who wants to use statistics in data science, business analytics or any other field to make better informed decisions.Videos packed with worked examples and explanations so you never get lost, and every technique covered is implemented in Microsoft Excel so that you can put it to use immediately.Key concepts taught in the course are:Descriptive Statistics: Averages, measures of spread, correlation and much more.Cleaning Data: Identifying and removing outliersVisualization of Data: All standard techniques for visualizing data, embedded in Excel.Probability: Independent Events, conditional probability and Bayesian statistics.Discrete Distributions: Binomial, Poisson, expectation and variance and approximations.Continuous Distributions: The Normal distribution, the central limit theorem and continuous random variables.Hypothesis Tests: Using binomial, Poisson and normal distributions, T-tests and confidence intervals.Regression: Linear regression analysis, correlation, testing for correlation, non-linear regression models.Quality of Tests: Type I and Type II errors, power and size, p-hacking.Chi-Squared Tests: The chi-squared distribution and how to use it to test for association and goodness of fit.Much, much more!It requires no prior knowledge, with the exception of 2 optional videos at the end of the continuous distribution chapter, in which knowledge of calculus is required).

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Overview

Section 2: Descriptive Statistics

Lecture 3 Data for this chapter

Lecture 4 The Mean Average

Lecture 5 The Median Average

Lecture 6 The Modal Average

Lecture 7 Comparing Averages

Lecture 8 Quantiles, Range and Inter-Quartile Range

Lecture 9 Quantiles, Range and Inter-Quartile Range - Data

Lecture 10 Standard Deviation and Variance

Lecture 11 Standard Deviation and Variance - Data

Lecture 12 The Coefficient of Variation

Lecture 13 The Coefficient of Variation - Data

Lecture 14 Skew

Lecture 15 Skew - data

Lecture 16 Kurtosis

Lecture 17 Correlation Coefficients

Lecture 18 Correlation Coefficients - Data

Section 3: Cleaning Data

Lecture 19 Anomalies and Outliers

Lecture 20 Anomalies and Outliers - Data

Lecture 21 Coding Your Data

Section 4: Data Visualization

Lecture 22 Line Graphs

Lecture 23 Bar Charts

Lecture 24 Dual Axis Charts

Lecture 25 Pie Charts

Lecture 26 Histograms

Lecture 27 Histograms - Data

Lecture 28 Box Plots

Lecture 29 Cumulative Frequency

Lecture 30 Comparing Visualizations

Section 5: Sampling

Lecture 31 Populations and Samples

Lecture 32 Random Sampling

Lecture 33 Non-Random Sampling

Section 6: Probability

Lecture 34 What is Probability?

Lecture 35 Set Notation

Lecture 36 Independent Events

Lecture 37 Mutually Exclusive Events

Lecture 38 Tree Diagrams

Lecture 39 Venn Diagrams

Lecture 40 Conditional Probability

Lecture 41 Bayes' Theorem

Section 7: Discrete Distributions

Lecture 42 What is a Discrete Random Variable?

Lecture 43 Probability Mass Functions

Lecture 44 The Expectation of a Discrete Random Variable

Lecture 45 The Variance of a Discrete Random Variable

Lecture 46 The Binomial Distribution - Intro

Lecture 47 The Binomial Distribution Formula - Part 1

Lecture 48 The Binomial Distribution Formula - Part 2

Lecture 49 Using Excel to Solve Binomial Problems

Lecture 50 Applying the Binomial Distribution to Real-World Problems

Lecture 51 Conditional Probability with the Binomial Distribution

Lecture 52 The Poisson Distribution - Intro

Lecture 53 Using Excel to Solve Poisson Problems

Lecture 54 Applying the Poisson Distribution Real-World Problems

Lecture 55 Conditional Probability with the Poisson Distribution

Lecture 56 The Geometric Distribution

Lecture 57 Expectation and Variance of Distributions

Lecture 58 Approximating the Binomial Distribution with the Poisson Distribution

Lecture 59 Derivation of the Poisson Formula

Section 8: Continuous Distributions

Lecture 60 What is a Continuous Distribution?

Lecture 61 The Normal Distribution - Intro

Lecture 62 Calculating Probabilities with the Normal Distribution

Lecture 63 The Inverse Normal Distribution

Lecture 64 Z-Scores

Lecture 65 Finding Unknown Means and Standard Deviations

Lecture 66 Conditional Probability with the Normal Distribution

Lecture 67 Normal Approximations to Binomial Distributions - Part 1

Lecture 68 Normal Approximations to Binomial Distributions - Part 2

Lecture 69 Normal Approximations to Poisson Distributions

Lecture 70 The Central Limit Theorem

Lecture 71 The Limitations of the Central Limit Theorem

Lecture 72 Continuous Random Variables - Probability Density Functions

Lecture 73 Continuous Random Variables - Cumulative Distribution Functions

Lecture 74 Continuous Random Variables - Expectation and Variance

Lecture 75 Continuous Random Variables - Medians and Quartiles

Section 9: Hypothesis Tests

Lecture 76 Introduction to Hypothesis Tests - P-Values

Lecture 77 Binomial Hypothesis Tests - Part 1

Lecture 78 Binomial Hypothesis Tests - Part 2

Lecture 79 Binomial Hypothesis Tests - Critical Regions

Lecture 80 Two-Tailed Tests

Lecture 81 Poisson Hypothesis Tests

Lecture 82 Poisson Critical Regions

Lecture 83 Normal Hypothesis Tests

Lecture 84 Normal Hypothesis Tests - Critical Regions

Lecture 85 T-Tests

Lecture 86 Confidence Intervals

Section 10: Regression

Lecture 87 Correlation

Lecture 88 Linear Regression

Lecture 89 Evaluating a Regression Line

Lecture 90 Correlation Hypothesis Tests - Intro

Lecture 91 Carrying Out a Test for Correlation

Lecture 92 Correlation Confidence Intervals

Lecture 93 Working with Non-Linear Data - Exponential Models

Lecture 94 Working with Non-Linear Data - Polynomial Models

Section 11: Quality of Tests

Lecture 95 Type I Errors

Lecture 96 Type II Errors

Lecture 97 Size and Power

Lecture 98 P-Hacking

Section 12: Chi-Squared Tests

Lecture 99 The Chi-Squared Distribution

Lecture 100 Chi-Squared Tests for Goodness of Fit

Lecture 101 Grouping

Lecture 102 Using Estimated Parameters in Chi-Squared Tests

Lecture 103 Chi-Squared Tests for Association

Data Scientists,Business Analysts,Business Students,People studying Statistics,Anyone looking to power their decision making with a thorough understanding of statistics.