Applied Statistics For Data Scientists

A Hands-On Approach Using Python

What you'll learn

Perform elaborate and involved Data Analysis on any dataset.

Build an intuitive understanding of concept in Statistics: Sample, Population, Correlation, P-value, Significance, and others.

Be able to write Python code that generates elaborate and beautiful Visuals.

Make Simulations using Python code that showcase various Statistical Concepts.

Be able to perform various Statistical Tests using Python (Student T-test, Welsh's Test, Levene's Test, Shapiro-Wilk test, …)

Be able to build a Machine Learning model to predict outcomes based on linear and logistic regression.

Requirements

Statistics Prerequisite: virtually none, you will learn everything you need to know.

Coding Prerequisites: the very basics of Python code, all code will be explained.

Description

Welcome to the course on Statistics For Data Scientists!Learn about the key concepts in statistics, and how to apply them to your data analysis.A highly practical and hands-on approach.A focus on building an intuitive understanding of each topic.Learn to use Python code to simulate various scenarios in a plug-and-play manner.What is included in the course:Detailed Course Notes (100 page textbook with 50+ illustrative figures)Deck of 360 slides Lectures with 10h+ content spread over 40+ videosAll of the code in Jupyter Notebooks (7 notebooks, 2000+ lines of code)Bonus Chapter: Introduction to Machine LearningTopics that the course covers:The HistogramGenerating artificial Data setsThe central tenet of StatisticsThe Central Limit TheoremDistribution functionsPercentilesData RangesCumulative Distribution FunctionDifferent Distribution types:Normal DistributionUniform DistributionExponential DistributionPoisson DistributionBernoulli DistributionRayleigh DistributionStatistical TestingReasoning behind statistical testingP-valueStatistical SignificanceDifferent Statistical Tests:Shapiro-Wilk testLevene's testStudent T-test/ Welsh T-testANOVA testKolmogorov Smirnov testNon-parametric testsTwo real-life examplesDetect a biased coin with 95% certaintyReal-life A/B testingCorrelationLinear correlation - Pearson correlation coefficient + alternativesCategorical correlation - Chi-Squared test + contingency tablesEXTRA: Regression and intro to Machine LearningLinear RegressionLogistic Regression + ML pipelineWho is this course for:Students on a data science track, or any other technical field.Professionals that want to pivot into a data science career.Managers that want to be able to make data driven decisions.Practicing Data Scientists that want to add this value skill to their tool belt.

Overview

Section 1: Course Introduction

Lecture 1 Course Introduction

Section 2: Chapter 1: Why Data science?

Lecture 2 Introduction

Section 3: Chapter 2: The Histogram

Lecture 3 How to build a histogram.

Lecture 4 Introducing the Probability Density Function.

Section 4: Chapter 3: Generating Artificial Data

Lecture 5 Different types of Data.

Lecture 6 How to Generate Artificial Data?

Lecture 7 Sample Versus Population!

Lecture 8 Let's Compute some Basic Statistics!

Lecture 9 Visualisation of Sample Statistics.

Lecture 10 Simulate Sample Statistics Fluctuations!

Section 5: Chapter 4: The Central Limit Theorem

Lecture 11 Simulating the Central Limit Theorem!

Lecture 12 The Strength and Weakness of the Central Limit Theorem

Section 6: Chapter 5: Distribution Functions

Lecture 13 Data Distributions: Introduction

Lecture 14 Percentiles and Data Intervals

Lecture 15 What is the Standard Deviation, really?

Lecture 16 The Cumulative Distribution Function

Lecture 17 Distribution Zoo #1 : Normal Distribution

Lecture 18 Distribution Zoo #2 : Uniform Distribution

Lecture 19 Distribution Zoo #3 : Exponential Distribution

Lecture 20 Distribution Function #4 : Poisson Distribution

Lecture 21 Distribution Zoo #5 : Binomial Distribution

Lecture 22 Distribution Zoo #6 : Rayleigh Distribution

Section 7: Intermediate Break

Lecture 23 You're doing great!

Section 8: Chapter 6: Statistical Testing

Lecture 24 Introduction to Statistical Testing

Lecture 25 The P-value and Statistical Significance

Lecture 26 Implementing the P-value in Python

Lecture 27 Testing the P-value through simulation!

Lecture 28 Statistical Test 1: Normalcy

Lecture 29 Statistical Test 2: Equal Variances

Lecture 30 Statistical Test 3: Equal Means

Lecture 31 Statistical Test 4: ANOVA Test

Lecture 32 Statistical Test 5: Testing Equal Distributions

Lecture 33 Non-parametric Statistical Tests

Section 9: Chapter 7: Two Concrete Real-Life Examples!

Lecture 34 Example 1: Detecting A Biased Coin!

Lecture 35 Implementing Coin Flipping in Python.

Lecture 36 Playing Around with the Simulation!

Lecture 37 Example 2: A/B testing

Section 10: Chapter 8: Correlation between Variables

Lecture 38 Introduction to Correlation

Lecture 39 Linear Correlation

Lecture 40 Linear Correlation in Python

Lecture 41 Pearson Correlation Coefficient

Lecture 42 Correlation between Categorical Variables

Lecture 43 Categorical Correlation: Chi-Squared test

Section 11: EXTRA: Regression and Machine Learning Introduction

Lecture 44 Linear Regression

Lecture 45 Logistic Regression & ML-pipeline

Students on a Data Science track or other technical field.,Professionals that want to pivot towards a data science career.,Active Data Scientists that want to add statistical knowledge and intuition to their tool belt.,Managerial Roles in technical fields that want to up their skill to make better decisions about data.