Introduction to Python and Google Colab | Google Colab for Data Science & AI using Python

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Google Colab for Data Science & AI using Python

By : Anand Rao Nednur

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Google Colab for Data Science & AI using Python

By: Anand Rao Nednur

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Overview of this book

This course begins by exploring Python’s origins and its rise as one of the most popular programming languages. The course covers the key factors behind Python's success, such as its simplicity, versatility, and active community. After covering the basics, you’ll dive into practical Python programming using Google Colab, a cloud-based tool that allows you to code without installing anything locally. As you progress, you’ll explore various programming paradigms, including procedural, object-oriented programming (OOP), and functional programming, supported by real-world examples. Key Python concepts like variables, data types, functions, and control flow will be covered. You’ll also learn to work with advanced data structures like lists, tuples, dictionaries, and sets, helping you manage complex data. Additionally, you’ll dive into data analysis and create visualizations using libraries like Pandas, NumPy, Matplotlib, and Seaborn. The course continues with advanced topics, including OOP principles, file handling, exception management, and API integration. You’ll also explore Python’s capabilities for data analysis and interactive data visualization using Plotly. Whether you're automating tasks, analyzing data, or building web apps, this course equips you with the skills needed for real-world Python projects.

Introduction to Python and Google Colab

Origins and evolution of Python

Why Python is popular?

Compare and contrast Python with other languages

Showcase real-world applications

Procedural Programming

Object-Oriented Programming (OOP)

Functional Programming

Local Installation

Dependency Management

Virtual Environments

What is Google Colab? Its purpose & Advantages

Setting up a Google Colab account

Detailed tour of the Colab interface (menus, toolbar, file explorer)

Explain the concept of cells

Demonstrate basic cell operations (adding, deleting, moving, running)

Python Basics - Variables

Data Types - Integers & Type Conversion

Operators - Arithmetic, Comparison & Logical Operators

String Manipulations

Input() Function

Print() Function

Comments in Python

Python Code Style Guidelines

Hands On - Write a program that takes the user's name as input and greets them

Hands On - Write a program that performs basic arithmetic operations

Hands On - Format the Notebook

Module 2: Control Flow and Functions

Conditional Statements: If, Elif, Else

Conditional Statements: Nested If Statements

Loops: For Loop

Loops: While Loop

Loops: Nested Loops

Loop Control Statements: Break

Loop Control Statements: Continue

Loop Control Statements: Pass

Functions: Definition

Functions: Parameter and Return Values

Functions: Default Arguments

Functions: Keyword Arguments

Functions: Scope of Variables

Lambda Functions: Anonymous Functions

Lambda Functions: Syntax of Lambda Functions

Lambda Functions: Use Cases

Hands On - Write a function to calculate the factorial of a number

Hands On - Write a function to check if a number is prime

Hands On - Write a function to reverse a string

Hands On - Create a function to calculate the area of different shapes

Module 3: Data Structures

Lists: Creating lists using Square brackets

Lists: Indexing and slicing accessing elements by their position

Lists: Modifying Lists

Lists: List Methods

Lists: List Comprehensions

Lists: Iterating over Lists

Tuples: Creating tuples using Parentheses()

Tuples: Accessing Elements

Tuples: Immutability

Tuples: Tuples use cases

Dictionaries: Creating Dictionaries

Dictionaries: Accessing Values

Dictionaries: Modifying Dictionaries

Dictionaries: Dictionary Methods

Dictionaries: Dictionary Comprehensions

Dictionaries: Iterating over Dictionaries

Sets: Iterating over Dictionaries

Sets: Uniqueness

Set Operations

Set Method

Hands On - Create a dictionary to store student information

Hands On - Use sets to find common elements between two lists

Hands On - Implement a shopping cart using a dictionary to store items

Hands On - Write a program to find the most frequent element in a list

Module 4: Working with Libraries in Colab

Libraries in Python

Installing Libraries in Colab

Importing the Libraries

Essential Libraries: Numpy

Essential Libraries: Pandas

Essential Libraries: Matplotlib

Essential Libraries: Seaborn

Hands-on - Use NumPy to perform matrix operations

Hands-on - Load a CSV dataset into a Pandas Data Frame and perform Analysis

Hands-on - Create a line plot and a bar chart using Matplotlib to visualize data

Hands-On - Use Seaborn to create a scatter plot with a regression line

Module 5: Object-Oriented Programming (OOP)

What is OOP

Principles of OOP

Classes and Objects

Constructors

Instance Variables

Methods

Advanced OOP Concepts - Inheritance

Advanced OOP Concepts - Polymorphism

Advanced OOP Concepts - Encapsulation

Advanced OOP Concepts - Abstraction

Modules

Packages

Importing Modules & Functions

Hands On - Create a DOG class with attributes

Hands On - Create a CAT Class and demonstrate Inheritance

Hands On - Building a Simple Banking System

Module 6: File Handling and Exception Management

File Handling - Opening Files

File Handling - Reading Files

File Handling - Writing Files

File Handling - Closing Files

File Handling - Working with Different file types

Errors and Exceptions

Try and Except Blocks

Raising Exceptions

Custom Exceptions

Hands On - Write a program to read a text file and count the number of words

Hands On - Write a program to read a CSV file and calculate the average of a column

Hands On - Create a program to handle potential errors

Hands On - Implement a custom exception for invalid file formats

Module 7: Intermediate Python

Iterators and Generators: Iterators

Iterators and Generators: Generators

Decorators

Syntax: Using the @decorator syntax

Use Cases of Decorators

Context Managers - The With Statement

enter() and exit() methods: Defining custom context managers

Use Cases of Context Managers

Hands On - Create a generator that yields Fibonacci numbers

Hands On - Write a decorator to log function calls with time stamps

Hands On - Implement a context manager to ensure a file is always closed

Advanced Python Concepts

Introduction to regular expressions

Basic syntax

The Re Module

Use Cases

What is an API (Application Programming Interface)

Introduction to REST APIs

The requests library

API keys and authentication

Hands On - Use regular expressions to validate email addresses

Hands On - Fetch data from a public API (e.g., a joke API) and display it

Hands On - Use the requests library to interact with a REST API

Data Analysis and Visualization

Advanced Pandas

Introduction to Plotly and Explain its advantages

Installation: Show how to install Plotly in Colab

Basic plotting: Create scatter plots, line charts, bar charts with Plotly

Customization: Add titles, labels, legends, and annotations

Interactive features: Enable zooming, panning, hovering, and click events

Advanced plots: Create 3D plots, geographic maps, and other complex visualizations

Hands-on Exercises-1

Hands-on Exercises-2

Google Colab for Data Science & AI using Python

By : Anand Rao Nednur

Google Colab for Data Science & AI using Python

By: Anand Rao Nednur

Overview of this book

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