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Module 1- Python for Data Science

Introduction

Module Introduction

Intuition

Constructs

Set-up

Data Science Tools

Introduction to Jupyter notebooks

Basics of Python

Variables

Operations

Python Operations Jupyter Notebook

Quiz - Basics

Conditional Programming-1

Conditional Programming-2

Conditional Programming-3

Functions in Python-1

Functions in Python-2

Conditional Programming and Functions in Python Jupyter Notebook

Data Structures

Data Structures-1

Quiz - Lists

Data Structures-2

Data Structures-3

Quiz - Sets

Data Structures-4

Quiz - Dictionaries

Python data structures Jupyter notebook

Loops-1

Loops-2

Loops-3

Python Loops Jupyter Notebook

Libraries in Python

Numpy-1

Numpy-2

Numpy Jupyter Notebook

Matplotlib

Matplotlib Jupyter Notebook

Quiz - Numpy, Matplotlib

Quiz - End of Chapter Quiz

Coding Assignment - Python Basics

Module 2 - Pandas

Pandas 1 - Basics

Pandas 2 - Row, Column Operations

Pandas 3 - Accessing data

Pandas 4 - Slicing and Sorting

Pandas 5 - Grouping and Aggregation

Pandas 6 - Pivot Tables

Pandas 7 - Joins

Pandas 8 - Concat

Pandas 9 - Merge

Pandas -10

Pandas Quiz

Pandas Jupyter notebooks

Coding Assignment - Python Basics 2

Introduction to Statistics

Introduction to Statistics

Types of variables

Measures of Central Tendency and Spread

Quiz 1 - Basics

Measuring Position

Histograms

Quiz 2 - Position

Types of Distributions

Normal Distribution

Quiz 3 - Distributions

Central Limit Theorem

Quiz 4 - Central Limit Theorem

Confidence Intervals

T- Distribution

Quiz 5 - Confidence Intervals and T distribution

Hypothesis Testing - 1

Hypothesis Testing - 2

Examples on Hypothesis Testing

Quiz 6 - Hypothesis testing

Tutorial - Hypothesis testing

Tutorial - Sampling

Tutorial - Descriptive Primer

Final Quiz

Statistics Assignment

Module 4 - Introduction to Machine Learning

Introduction

Learning process

Somethink to think about!

Quiz 1 ML Basics

EDA_FE

Quiz 2- Basics

Feature Selection

How a model learns

Measuring Performance

Model Performance

Bias-Variance

Quiz 3 - How a model works?

Visualisation

Quiz 4 EDA

Feature Engg Demo

Feature Scaling

Quiz 5 Feature Engineering

Module 3 Assignment - EDA

Module 3 Assignment EDA and Feature Engineering

Module 5 - Supervised Learning -1

Module Intro

Introduction to Linear Regression

Model training - Linear Regression

Model evaluation - Linear Regression

Regularisation - linear regression

Assumptions of Linear Regression

Locally weighted Linear regression

Demonstration - Linear Regression

Linear Regression Quiz -1

Linear Regression Quiz - 2

Introduction to Logistic Regression

Model Training - Logistic Regression

Model evaluation - Logistic Regression

Demonstration - Logistic Regression

Multiclass Classification

Logistic Regression Quiz 1

Logistic Regression Quiz 2

Module 6 - Supervised Learning 2

Introduction to Decision Trees

Classification Trees

Bagging

Boosting

Module 7 - Unsupervised Learning

K-Means Clustering

Demo-K-Means

Hierarchical Clustering

Association Rule Mining

Demo-Association Rule Mining

Recommendation Systems

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