Data Science Notes Part-4 | Python LIBRARIES

Chapter 1: Introduction to Data Science Libraries

1.1 What is Data Science?

Definition and Scope

Importance of Data Science in Today's World

1.2 Introduction to NumPy

What is NumPy?

Key Features and Advantages

Applications of NumPy in Data Science

1.3 Introduction to Pandas

What is Pandas?

Key Features and Advantages

Applications of Pandas in Data Science

1.4 Introduction to Matplotlib and Seaborn

What are Matplotlib and Seaborn?

Key Features and Advantages

Applications of Visualization in Data Science

1.5 Introduction to Scikit-learn

What is Scikit-learn?

Key Features and Advantages

Applications of Scikit-learn in Machine Learning

Chapter 2: Setting up the Development Environment

2.1 Installation of Python

Choosing the Right Python Version

Installation Steps for Windows, macOS, and Linux

2.2 Installing NumPy, Pandas, Matplotlib, and Scikit-learn

Using pip and Conda for Package Management

Verifying Installation

2.3 Setting up Integrated Development Environments (IDEs)

Introduction to IDEs like Jupyter Notebook, Spyder, and PyCharm

Configuring the Development Environment for Efficient Coding

Chapter 3: NumPy Arrays and Data Types

3.1 Introduction to NumPy Arrays

Creating NumPy Arrays

Accessing and Manipulating Array Elements

Array Operations and Broadcasting

3.2 Understanding Data Types in NumPy

Numeric Data Types: int, float, complex

Specifying Data Types in NumPy Arrays

Conversion between Data Types and its Implications

3.3 Working with Multidimensional Arrays

Creating Multidimensional Arrays

Indexing and Slicing Multidimensional Arrays

Reshaping and Concatenating Arrays

3.4 Array Manipulation and Operations

Arithmetic Operations on Arrays

Aggregation and Reduction Functions

Array Comparison and Boolean Indexing

3.5 Handling Missing Data with NumPy

Identifying and Handling Missing Values

Using Masked Arrays for Missing Data