Google Colab For Data Science & Ai Using Python
Published 3/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Master Python Programming from Scratch Using Google Colab -Hands-on, Beginner-Friendly, and Practical.
What you’ll learn
Understand Python fundamentals, including syntax, data types, and control flow.
Work with Google Colab, an online Jupyter-based platform, without the need for local installations.
Make decisions using conditional statements and automate tasks with loops.
Create reusable functions, work with arguments, and explore lambda functions.
Work with classes, objects, inheritance, polymorphism, and encapsulation.
Read, write, and manage text, CSV, and JSON files while handling errors effectively.
Explore NumPy, Pandas, Matplotlib, Seaborn, and Plotly for data manipulation and visualization.
Load, clean, and analyze datasets using Pandas and visualize insights with Matplotlib and Seaborn.
Fetch and process data from external sources using the requests library.
Apply pattern matching for data validation and text processing.
Learn advanced Python concepts for efficient programming.
Understand fundamental concepts, train simple models, and evaluate their performance.
Requirements
Basic Computer Skills – Comfortable with using a computer, browsing the internet, and managing files.
A Google Account – Required to access Google Colab (free to create).
Internet Access – Since Google Colab runs online, a stable internet connection is necessary.
Curiosity and Willingness to Learn – A problem-solving mindset and eagerness to explore Python.
Basic Mathematics Understanding (Optional) – A general understanding of basic math concepts (addition, subtraction, multiplication, division) will be helpful, but not mandatory.
No Software Installation Required! – Since we will be using Google Colab, there is no need to install Python or any additional software on your local machine. Everything runs in the cloud!
Description
Overview
Section 1: Introduction to Python and Google Colab
Lecture 1 Origins and evolution of Python
Lecture 2 Why Python is popular ?
Lecture 3 Compare and contrast Python with other languages
Lecture 4 Showcase real-world applications
Lecture 5 Procedural Programming
Lecture 6 Object-Oriented Programming (OOP)
Lecture 7 Functional Programming
Lecture 8 Local Installation
Lecture 9 Dependency Management
Lecture 10 Virtual Environments
Lecture 11 What is Google Colab It_s purpose & Advantages ?
Lecture 12 Setting up a Google Colab account
Lecture 13 Detailed tour of the Colab interface (menus, toolbar, file explorer).
Lecture 14 Explain the concept of cells
Lecture 15 Demonstrate basic cell operations (adding, deleting, moving, running).
Lecture 17 Data Types – Integers & Type Conversion.
Lecture 19 String Manipulations
Lecture 20 Input() Function
Lecture 21 Print() Function
Lecture 22 Comments in Python
Lecture 23 Python Code Style Guidelines
Lecture 24 Hands On – Write a program that takes the user’s name as input and greets them
Lecture 25 Hands On – Write a program that performs basic arithmetic operations
Lecture 26 Hands On – Format the Notebook
Section 2: Module 2: Control Flow and Functions
Lecture 27 Conditional Statements : If , Elif , Else
Lecture 28 Conditional Statements : Nested If Statements
Lecture 29 Loops : For Loop
Lecture 30 Loops : While Loop
Lecture 31 Loops : Nested Loops
Lecture 32 Loop Control Statements : Break
Lecture 33 Loop Control Statements : Continue
Lecture 34 Loop Control Statements : Pass
Lecture 35 Functions : Definition
Lecture 36 Functions: Parameter and Return Values
Lecture 37 Functions: Default Arguments
Lecture 38 Functions: Keyword Arguments
Lecture 40 Lambda Functions : Anonymous Functions
Lecture 41 Lamdba Functions : Syntax of Lambda Functions
Lecture 42 Lambda Functions : Use Cases
Lecture 43 Hands On – Write a function to calculate the factorial of a number
Lecture 44 Hands On – Write a function to check if a number is prime.
Lecture 45 Hands On – Write a function to reverse a string
Lecture 46 Hands On – Create a function to calculate the area of different shapes.
Section 3: Module 3: Data Structures
Lecture 47 Lists : Creating lists using Square brackets
Lecture 48 Lists : Indexing and slicing accessing elements by their position
Lecture 49 Lists : Modifying Lists
Lecture 50 Lists : List Methods
Lecture 51 Lists : List Comprehensions
Lecture 52 Lists : Iterating over Lists
Lecture 53 Tuples : Creating tuples using Parentheses()
Lecture 54 Tuples : Accessing Elements
Lecture 55 Tuples : Immutability
Lecture 56 Tuples : Tuples use cases
Lecture 57 Dictionaries : Creating Dictionaries
Lecture 58 Dictionaries : Accessing Values
Lecture 59 Dictionaries : Modifying Dictionaries
Lecture 60 Dictionaries : Dictionary Methods
Lecture 61 Dictionaries : Dictionary Comprehensions
Lecture 62 Dictionaries : Iterating over Dictionaries
Lecture 63 Sets : Iterating over Dictionaries
Lecture 64 Sets : Uniqueness
Lecture 65 Set Operations
Lecture 66 Set Method
Lecture 67 Hands On – Create a dictionary to store student information
Lecture 68 Hands On – Use sets to find common elements between two lists.
Lecture 70 Hands On – Write a program to find the most frequent element in a list
