Data Science Statistics A-Z : Python

Course Curriculum

Chapter 1: Python Fundamentals

Lecture 1: Installation of Python and Anaconda

Lecture 2: Python Introduction

Lecture 3: Variables in Python

Lecture 4: Numeric Operations in Python

Lecture 5: Logical Operations

Lecture 6: If else Loop

Lecture 7: for while Loop

Lecture 8: Functions

Lecture 9: String Part1

Lecture 10: String Part2

Lecture 11: List Part1

Lecture 12: List Part2

Lecture 13: List Part3

Lecture 14: List Part4

Lecture 15: Tuples

Lecture 16: Sets

Lecture 17: Dictionaries

Lecture 18: Comprehentions

Chapter 2: Numpy

Lecture 1: Introduction

Lecture 2: Numpy Operations Part1

Lecture 3: Numpy Operations Part2

Chapter 3: Pandas

Lecture 1: Introduction

Lecture 2: Series

Lecture 3: DataFrame

Lecture 4: Operations Part1

Lecture 5: Operations Part2

Lecture 6: Indexes

Lecture 7: loc and iloc

Lecture 8: Reading CSV

Lecture 9: Merging Part1

Lecture 10: groupby

Lecture 11: Merging Part2

Lecture 12: Pivot Table

Chapter 4: Some Fun With Maths

Lecture 1: Linear Algebra : Vectors

Lecture 2: Linear Algebra : Matrix Part1

Lecture 3: Linear Algebra : Matrix Part2

Lecture 4: Linear Algebra : Going From 2D to nD Part1

Lecture 5: Linear Algebra : 2D to nD Part2

Chapter 5: Inferential Statistics

Lecture 1: Inferential Statistics

Lecture 2: Probability Theory

Lecture 3: Probability Distribution

Lecture 4: Expected Values Part1

Lecture 5: Expected Values Part2

Lecture 6: Without Experiment

Lecture 7: Binomial Distribution

Lecture 8: Commulative Distribution

Lecture 9: PDF

Lecture 10: Normal Distribution

Lecture 11: z Score

Lecture 12: Sampling

Lecture 13: Sampling Distribution

Lecture 14: Central Limit Theorem

Lecture 15: Confidence Interval Part1

Lecture 16: Confidence Interval Part2

Chapter 6: Hypothesis Testing

Lecture 1: Introduction

Lecture 2: NULL And Alternate Hypothesis

Lecture 3: Examples

Lecture 4: One/Two Tailed Tests

Lecture 5: Critical Value Method

Lecture 6: z Table

Lecture 7: Examples

Lecture 8: More Examples

Lecture 9: p Value

Lecture 10: Types of Error

Lecture 11: t- distribution Part1

Lecture 12: t- distribution Part2

Chapter 7: Data Visualisation

Lecture 1: Matplotlib

Lecture 2: Seaborn

Lecture 3: Case Study

Lecture 4: Seaborn On Time Series Data

Chapter 8: Exploratory Data Analysis

Lecture 1: Introduction

Lecture 2: Data Sourcing and Cleaning part1

Lecture 3: Data Sourcing and Cleaning part2

Lecture 4: Data Sourcing and Cleaning part3

Lecture 5: Data Sourcing and Cleaning part4

Lecture 6: Data Sourcing and Cleaning part5

Lecture 7: Data Sourcing and Cleaning part6

Lecture 8: Data Cleaning part1

Lecture 9: Data Cleaning part2

Lecture 10: Univariate Analysis Part1

Lecture 11: Univariate Analysis Part2

Lecture 12: Segmented Analysis

Lecture 13: Bivariate Analysis

Lecture 14: Derived Columns

Chapter 9: Simple Linear Regression

Lecture 1: Introduction to Machine Learning

Lecture 2: Types of Machine Learning

Lecture 3: Introduction to Linear Regression (LR)

Lecture 4: How LR Works?

Lecture 5: Some Fun With Maths Behind LR

Lecture 6: R Square

Lecture 7: LR Case Study Part1