Data Statistics with Full Stack Python

Table of contents

1. Chapter 1 : Python Fundamentals
   1. Installation of Python and Anaconda
   2. Python Introduction
   3. Variables in Python
   4. Numeric Operations in Python
   5. Logical Operations
   6. If else Loop
   7. for while Loop
   8. Functions
   9. String Part1
   10. String Part2
   11. List Part1
   12. List Part2
   13. List Part3
   14. List Part4
   15. Tuples
   16. Sets
   17. Dictionaries
   18. Comprehensions
2. Chapter 2 : Numpy
   1. Introduction
   2. Numpy Operations Part1
   3. Numpy Operations Part2
3. Chapter 3 : Pandas
   1. Introduction
   2. Series
   3. DataFrame
   4. Operations Part1
   5. Operations Part2
   6. Indexes
   7. loc and iloc
   8. Reading CSV
   9. Merging Part 1
   10. groupby
   11. Merging Part2
   12. Pivot Table
4. Chapter 4 : Some Fun With Maths
   1. Linear Algebra: Vectors
   2. Linear Algebra: Matrix Part1
   3. Linear Algebra: Matrix Part2
   4. Linear Algebra: Going From 2D to nD Part1
   5. Linear Algebra: 2D to nD Part2
5. Chapter 5 : Inferential Statistics
   1. Inferential Statistics
   2. Probability Theory
   3. Probability Distribution
   4. Expected Values Part1
   5. Expected Values Part2
   6. Without Experiment
   7. Binomial Distribution
   8. Commulative Distribution
   9. PDF
   10. Normal Distribution
   11. z Score
   12. Sampling
   13. Sampling Distribution
   14. Central Limit Theorem
   15. Confidence Interval Part1
   16. Confidence Interval Part2
6. Chapter 6 : Hypothesis Testing
   1. Introduction
   2. NULL and Alternate Hypothesis
   3. Examples
   4. One/Two Tailed Tests
   5. Critical Value Method
   6. z Table
   7. Examples
   8. More Examples
   9. p Value
   10. Types of Error
   11. t- distribution Part1
   12. t- distribution Part2
7. Chapter 7 : Data Visualization
   1. Matplotlib
   2. Seaborn
   3. Case Study
   4. Seaborn on Time Series Data
8. Chapter 8 : Exploratory Data Analysis
   1. Introduction
   2. Data Sourcing and Cleaning part1
   3. Data Sourcing and Cleaning part2
   4. Data Sourcing and Cleaning part3
   5. Data Sourcing and Cleaning part4
   6. Data Sourcing and Cleaning part5
   7. Data Sourcing and Cleaning part6
   8. Data Cleaning part1
   9. Data Cleaning part2
   10. Univariate Analysis Part1
   11. Univariate Analysis Part2
   12. Segmented Analysis
   13. Bivariate Analysis
   14. Derived Columns
9. Chapter 9 : Simple Linear Regression
   1. Installing Anaconda using Jupyter Notebook
   2. Introduction to Machine Learning
   3. Types of Machine Learning
   4. Introduction to Linear Regression (LR)
   5. How LR Works?
   6. Some Fun With Maths Behind LR
   7. R Square
   8. LR Case Study Part1
   9. LR Case Study Part2
   10. LR Case Study Part3
   11. Residual Square Error (RSE)
10. Chapter 10 : Real World Problem - Investment Requirement Analysis for a Company
    1. Investment Project Brief
    2. Investment Project_Data Cleaning Part 1
    3. Investment Project_Data Cleaning - Part 2
    4. Investment Project_Funding_Country_Sector Analysis Part 1
    5. Investment Project_Funding_Country_Sector Analysis Part 2
11. Chapter 11 : Loan Analysis Project
    1. Problem Statement
    2. Lending Club Default Analysis - Data Understanding and Data Cleaning
    3. Data Analysis - Univariate Bivariate Analysis
    4. Segmented Univariate Analysis