# Statistics 2023 AZ For Data Science with Both Python R

Beginner to Expert Guide for Data Science and Business Analysis with Case Studies and Hands-on Exercise Using Python & R

4.9/5

## Statistics 2023 AZ For Data Science with Both Python R

### What you’ll learn

Statistics
Data Analysis
Regression Analysis
Descriptive Statistics
Inferential Statistics
Hypothesis Testing
T-Test
Chi Square Test
AnOVa
Linear Regression
Logistic Regression
Machine Learning
Data Science

### Requirements

Knowledge Of Basic Python and R
Motivation to Learn

### Description

Data Science and Analytics is a highly rewarding career that allows you to solve some of the world’s most interesting problems and Statistics the base for all the analysis and Machine Learning models. This makes statistics a necessary part of the learning curve. Analytics without Statistics is baseless and can anytime go in the wrong direction.For a majority of Analytics professionals and Beginners, Statistics comes as the most intimidating, doubtful topic, which is the reason why we have created this course for those looking forward to learn Statistics and apply various statistical methods for analysis with the most elaborate explanations and examples!This course is made to give you all the required knowledge at the beginning of your journey, so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career.This course provides Full-fledged knowledge of Statistics, we cover it all.Our exotic journey will include the concepts of:1. What’s and Why’s of Statistics – Understanding the need for Statistics, difference between Population and Samples, various Sampling Techniques.2. Descriptive Statistics will include the Measures Of central tendency – Mean, Median, Mode and the Measures of Variability – Variance, SD, IQR, Bessel’s Correction3. Further you will learn about the Shapes Of distribution – Bell Curve, Kurtosis, Skewness.4. You will learn about various types of variables, their interactions like Correlation, Covariance, Collinearity, Multicollinearity, feature creation and selection.5. As part of Inferential statistics, you will learn various Estimation Techniques, Properties of Normal Curve, Central Limit Theorem calculation and representation of Z Score and Confidence Intervals.6. In Hypothesis Testing you will learn how to formulate a Null Hypothesis and the corresponding Alternate Hypothesis.7. You will learn how to choose and perform various hypothesis tests like Z – test, One Sample T Test, Independent T Test, Paired T Test, Chi Square – Goodness Of Fit, Chi-Square Test for Independence, ANOVA8. In regression Analysis you will learn about end-to-end variable creation selection data transformation, model building and Evaluation process for both Linear and Logistic Regression.9. In-depth explanation for Statistical Methods with all the real-life tips and tricks to give you an edge from someone who has just the introductory knowledge which is usually not provided in a beginner course.10. All explanations provided in a simple language to make it easy to understand and work on in future.11. Hands-on practice on more than 15 different Datasets to give you a quick start and learning advantage of working on different datasets and problems.

### Overview

Section 1: Introduction to Course

Lecture 1 Introduction

Section 2: Descriptive Statistics Explained

Lecture 2 Introduction to Statistics_Population & Sampling

Lecture 3 Measure Of Central Tendencies Mean Median Mode

Lecture 4 Measure Of Variability – Variance Standard Deviation IQR

Lecture 5 Data Diatributions Correlation & Covariance

Lecture 6 Practice Questions: Descriptive Statistics

Section 3: Intro to Inferential Statistics

Lecture 7 Intro to Inferential Statistics

Lecture 8 Variable Types

Section 4: Inferential Statistics: Central Limit Theorem,Z-Score,Confidence Interval

Lecture 9 Central Limit Theorem

Lecture 10 Z-Score

Lecture 11 Confidence Interval

Lecture 12 CI examples

Section 5: Hypothesis Testing

Lecture 13 Hypothesis Testing Introduction

Lecture 14 Hypothesis Testing Theory Explained

Lecture 15 Type of Errors and Significant Difference

Section 6: T-test Family

Lecture 16 One Sample, Independent, Paired T Test

Section 7: Chi-Square Tests

Lecture 17 Chi Square test of Goodness of Fit

Lecture 18 Chi Square test of Independance

Section 8: ANOVA

Lecture 19 ANOVA

Lecture 20 Which test to pick?

Section 9: Practice Questions in Python: Descriptive and Inferential Statistics

Lecture 22 Z-Score questions

Lecture 23 T-tests questions

Lecture 24 Chi Test, Anova, Cov, Correlation questions

Section 10: Statistics using Python – Case Studies

Lecture 25 House Prices Dataset – Case Study -1

Lecture 26 City Payroll Dataset – Case Study -2

Section 11: Descriptive Statistics Using R -Practice

Lecture 27 Descriptive Statistics using R Practice Questions

Section 12: Inferential Statistics Using R – Practice

Lecture 28 Inferential Statistics Using R Practice Questions

Section 13: Statistics using R – Case Studies

Lecture 29 Census Income Dataset – Case Study -1

Section 14: Linear Regression Analysis using Python

Lecture 30 Regression Analysis Explained – Linear Regression

Lecture 31 Linear Regression Cost, Gradient and Cross Validation

Lecture 32 Linear Regression from scratch

Lecture 33 Linear Regression Regularization

Section 15: Logistic Regression Analysis using Python

Lecture 34 Logistic Regression Introduction

Lecture 35 06_Logistic Regression_Mathematics

Lecture 36 07 Logistic Regression Metrics

Lecture 37 Logistic Regression Implementation

Section 16: Linear Regression Analysis using R

Lecture 38 Linear Regression Analysis using R

Section 17: Logistic Regression Analysis using R

Lecture 39 Logistic Regression Analysis using R