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Welcome to a treasure trove of Python programming expertise, Data Science mastery, and essential survival skills for navigating the dynamic world of programming. Dive into the depths of this repository to unlock the knowledge and tools you need to thrive in your coding journey.

This repository isn’t just a collection of code snippets; it’s a comprehensive resource designed to empower learners and professionals alike. You’ll find invaluable insights and guidance for:

• Novices - Explore the fundamentals of Python programming.
• Data Enthusiasts - Delve into the intricacies of data science.
• Seasoned Developers - Hone your craft with advanced techniques and tips.

• Python Proficiency - Elevate your Python skills from beginner to advanced levels with my curated tutorials, exercises, and real-world examples.
• Data Science Expertise - Harness the power of data with our in-depth guides, projects, and best practices in data analysis, machine learning, and beyond.
• Survival Toolkit - Navigate the complexities of the programming world with confidence, thanks to our tips, tricks, and advice on career development, productivity, and staying ahead of the curve.

• Comprehensive Learning - Whether you're starting your journey or aiming for mastery, this repository provides the resources you need to succeed.
• Community Support - Join a vibrant community of learners, mentors, and experts passionate about sharing knowledge and helping each other grow.
• Practical Application - From theory to practice, our hands-on approach ensures that you understand concepts and how to apply them effectively in real-world scenarios.

Ready to embark on your Python journey? Explore our repository, contribute your expertise, and connect with fellow enthusiasts. Together, we'll sharpen our skills, unravel the mysteries of programming, and unlock new opportunities in the ever-evolving tech landscape.

The Python Mastery Repository is more than just a collection of code; it's a gateway to endless possibilities. Start your exploration today and discover the boundless potential of Python programming, data science, and beyond.

Below are some tools you can download before getting started with Python. It's all about personal preference, so feel free to choose the ones that work best for you!

Name of the tools
Python download
Visual Studio Code
Jupyter Notebook
Google Colab

I use Google Colab for Python programming, and it's one of the best interactive tools out there. I love it because it allows me to add more documentation to my code and create quality tutorials, making it easier to share knowledge and improve learning.

Follow the steps below to get started coding in Python!!!

Expand and Collapse the above Chapters for more details

Chapter 1️⃣ ⮕ Basic Concepts

• Python Input, Output and Import functions

• Python Variables

  • Python Global, Local and Nonlocal Variables

• Python Strings

• Python Lists

• Python Tuples

• Python Dictionary

• Python Operators

• Mastering Python Decorators

Chapter 2️⃣ ⮕ Built-in Functions

• Python Input, Output and Import built-in-functions

• Eval built-in-function

• Range built-in-function

• Python Lambda Function

• Python Enumerate Function

• Python len function

Chapter 3️⃣ ⮕ Libraries

• Numpy library

• Pandas library

• Math Module

• JSON library

Chapter 4️⃣ ⮕ API's

• Google Translate API for Python

• Google Trends API for Python

• Wikipedia API for Python

• Google Search API for Python

• General Transit Feed Specification - General Transit Feed Specification (GTFS)

• Unlocking Time Series Forecasting with Facebook Prophet

Chapter 5️⃣ ⮕ Additional Materials

• How to get started coding in Python?

• Is Python Object Oriented?

• Speech Recognition using Python

• One-Hot encoding in Python

• Reading An Image In Python (Without Using Special Libraries)

• Rendering Images inside a Pandas DataFrame

• Using the Pandas Data Frame as a Database

• Using the Pandas Data Frame in Day-To-Day Life

• Presenting Python code using RISE

• Cheat Sheet for Google Colab

• Pick-Up Line Generator using Python Dictionaries

• Optimizing Python Code with List Comprehensions

• Understanding Virtual Environments in Python

• Hidden Markov Models in Python

• Mastering Feature Maps in Convolutional Neural Networks

• Build a Rule-Based System with Python

Chapter 6️⃣ ⮕ Exercises

• String Concatenation Questions

  • String Concatenation Answers

• Built-In Functions Exercise Questions

Chapter 7️⃣ ⮕ Quiz

• Quiz - 1

• Quiz - 2

• Quiz - 3

Chapter 8️⃣ ⮕ Interview Preparation

• Python Coding Interview Questions (Beginner to Advanced)

• Crack Python Interviews Like a Pro!

• 35 Python interview questions for experienced

• Python Interview Questions - Strings

• Python Theoretical Interview Questions

• 15 Python Interview Questions and Answers

• Assigning Candies to Children Problem with Solution

• Basic Calculator

• Text Justification

• Removing an Element

• Vowel Count in a Sentence

• Pick-Up Line Generator - Based on Sentiments

• Sentimental Analysis

• Creating a Regular Polygon Visualizer Using Matplotlib in Python

Chapter 9️⃣ ⮕ Design Principles

• Modular Pipeline Architecture or Clean Architecture

• Dependency Inversion Principle

• Open-Closed Principle

• Liskov Substitution Principle

• Interface Segregation Principle

• Single Responsibility Principle

• Law of Demeter

• Composition Over Inheritance

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Follow the steps below to get started learning Data Science!!!

Data Exploration

• Loading a File using Pandas

Data Scraping from the Web

• Scraping Two YouTube Accounts - PewDiePie vs T-Series

• Scraping Rate My Professor Website - My Graduate Professor

• Web Scraping vs API: Which Data Extraction Method is Best for Your Needs?

Cracking Transit Data, Calgary 2025

• How to Decode and Leverage GTFS for Real-Time Transit Insights

Additional Materials (Projects)

• Mastering the Art of Data Preprocessing with Pandas

• Time Series Forecasting with Pandas

• Demystifying Feature Engineering

• Building Your First Machine Learning Model with scikit-learn

• Build a Smart Resume Ranker with Python and Natural Language Processing

• Predicting Loan Default Using Decision Trees with Explainable AI

• How to Efficiently Compute Euclidean Distance in Python Using NumPy (No Loops Needed)

• How to Handle Missing Data in Pandas Like a Pro (Python for Data Science)

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Follow the steps below to get started on your journey to perfect Machine Learning !!!

Image by Author - Canva

Prerequisite

• Main roadmap and guide
• Python dependencies
• Contribution guidelines
• Further reading and links

Math & Python foundations

• Python Review
• Linear Algebra
• Statistics

Regression & classification

• Regression
• Classification
• Advanced Model Evaluation

Clustering & dimensionality reduction

• Clustering
• Dimensionality Reduction

Deep learning with NNs

• Perceptron
• Deep Learning
• Convolutional Neural Networks
• Recurrent Neural Networks

Deployment, tracking, versioning

• Data Versioning
• Model Deployment
• Monitoring Machine Learning Models in Production

Real-world end-to-end projects

• House Price Prediction

  • README.md

• Twitter Sentiment Analysis

  • README.md

• Credit Card Fraud Detection

  • README.md

• Customer Churn Prediction

  • README.md

• Loan Approval Prediction

  • README.md

• TMDB Movie Recommendation System

  • README.md

• Retail Sales Forecasting

  • README.md

• Fake News Detection

  • README.md

• Disease Prediction from Symptoms using Machine Learning

  • README.md

• Emotion Detection from Text

  • README.md

• Handwritten Digit Recognition (MNIST)

  • README.md

Machine Learning Interview Preparation

• Fundamental ML concepts like types of learning, bias-variance, etc.

• Questions on algorithms like Linear Regression, SVM, Decision Trees, etc.

• Covers clustering (K-Means, DBSCAN) and dimensionality reduction (PCA)

• Model evaluation metrics like Precision, Recall, F1, ROC-AUC, etc.

• Covers data cleaning, encoding, handling missing values, scaling, etc.

• Deep Learning, Transfer Learning, Model Deployment, and more

• Project-based questions and soft-skill questions

• Scenario-based and live practice prompts

• Google Colab notebook with hands-on ML implementation tasks

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I’m Tanu Nanda Prabhu, and my passion lies in simplifying complex concepts to make them easier for others to understand. Research is something I deeply enjoy, and after exploring numerous videos, articles, and tutorials, I decided to create a repository dedicated to Google Sheets tips and tricks. I update this repository weekly with valuable insights designed to make your life simpler. I’m also open to contributions from those who wish to help enhance this resource. Feel free to fork the repository and submit updates; I’ll gladly review and consider them.

Tech Is Easy

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If the Jupyter Notebook doesn’t load, don’t worry! Simply copy and paste the link into nbviewer, as most of my notebooks are accessible there.

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1. Kaggle Data Sets

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1. HackerRank Exercise Solved

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1. Python
2. Learn Python
3. Python tips
4. Python coding

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If you have access to ChatGPT premium, there is a GPT Librarian with access to all files here here

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Contact	Info
	tanunprabhu95@gmail.com
	Tanu Nanda Prabhu
	Tanu N Prabhu
	Python Coder

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Below are some of the reviews about this Python GitHub Repository:

‪Elin Uppström - Senior Lecturer at Uppsala University, Sweden.
I found your excellent exercises on your GitHub while preparing an undergraduate course in data analysis. I want to use it in my course.

‪Cole Striler - Data Scientist, Founder of Datafied
I came across your GitHub and love your Jupyter Notebooks, especially the one on "Predicting PewDiePie's daily subscribers". I think you do a great job at explaining your work, which is something others can learn.

Laurence Watson - Co-Founder & CEO, Treebeard
You have a lot of great Jupyter notebook content on GitHub.

Poonam Gupta - Math & AP Computer Science Instructor, Brunswick School
Thank you so much for posting such helpful posts on GitHub. Many, many thanks for all you do to spread the knowledge.

David Okenwa - Mechanical Engineer and currently building in Data Analytics! I recently came across your Medium, GitHub, and portfolio website serendipitously and was enthralled. Inspiring! I want to start writing articles and building my GitHub repositories.

**Do you like the Repository? Please share your valuable reviews by sending an email.

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