Start your Data Science journey with Python, a simple and powerful language. This guide covers the basics to deployment using real-world projects and tools.

Python_Roadmap_Resources.md

🚀 Getting Started with Python for Data Science

Begin your journey into Data Science with Python, a beginner-friendly and versatile language.

This guide offers a structured path with curated resources, covering fundamentals, hands-on practice, data cleaning, visualization, and deployment. Perfect for students and beginners aiming to work on real-world datasets and build deployable data apps.

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📑 Table of Contents

1. 📘 Step 1: Learn Python Fundamentals
2. ⚙️ Step 2: Set Up the Python Environment
3. ☁️ Optional: Use Google Colab (No Setup Required)
4. 🧠 Step 3: Practice with Core Python Programs
5. 📊 Step 4: Data Cleaning, Preparation & Visualization
6. 📂 Step 5: Work with Real Datasets from Kaggle
7. 🚀 Step 6: Deploy with Streamlit
8. 🧭 Learning Roadmap

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📘 Step 1: Learn Python Fundamentals

Start with a structured introduction to the language:

🔗 W3Schools – Python Introduction

🔗 Programiz – Python Basics

Reference Notes for Beginners:

🔗 Python Notes – Unit 1

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⚙️ Step 2: Set Up the Python Environment

✅ Install Python

Learn to install Python on any OS:

🔗 RealPython – Installing Python

🔗 Python Official Downloads

📓 Work with Jupyter Notebook

Jupyter Notebook is beginner-friendly and widely used in data science:

• 🔗 Dataquest – Jupyter Tutorial
• 🔗 GeeksforGeeks – Jupyter Guide
• 🔗 Jupyter Official Docs

Tip

📝 Tip: After creating a .ipynb file, right-click → "Open with Jupyter Notebook" for quick access.

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☁️ Optional: Use Google Colab (No Setup Required)

Run Python notebooks in the browser without installing anything:

• 🔗 TutorialsPoint – Google Colab Intro
• 🔗 GeeksforGeeks – Getting Started with Colab
• 🔗 Google Colab – Official Site

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🧠 Step 3: Practice with Core Python Programs

Sharpen your skills with programs covering:

✅ Loops, conditionals, lists, dictionaries ✅ Functions, file handling, exception handling

🔗 Python Programming Repository

🔗 Practice Python Problems – PracticePython.org

🔗 Hackerrank Python Practice

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📊 Step 4: Data Cleaning, Preparation & Visualization

🧹 Data Preprocessing and Cleaning

Learn to clean and preprocess messy data:

🔗 Data Cleaning & Visualization Folder

🔗 Towards Data Science – Data Cleaning Guide

📈 Data Visualization with Python

Use Python libraries like matplotlib, seaborn, and plotly to visualize data:

🔗 Data Visualization Projects

🔗 Seaborn Tutorial – Official Docs

🔗 Plotly in Python – Intro Guide

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📂 Step 5: Work with Real Datasets from Kaggle

Kaggle is a hub for datasets and machine learning competitions.

1. Create a Kaggle account.
2. Download datasets (.zip) and extract them.
3. Use pandas.read_csv() to load CSV files.

🔗 Kaggle Datasets

🔗 Kaggle Python Starter Code

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🚀 Step 6: Deploy with Streamlit

Build interactive web apps for data science projects with Streamlit.

🔗 Streamlit Apps – Code Repository

🔗 Streamlit + ML Models

🔗 DataCamp – Streamlit Guide

▶️ YouTube – Streamlit Deployment

🔗 Streamlit Official Docs

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🗺️ Learning Roadmap

Stage	Focus Area
1️⃣	Learn Python fundamentals
2️⃣	Set up Jupyter/Colab
3️⃣	Practice core programs
4️⃣	Understand data preparation
5️⃣	Create visualizations
6️⃣	Analyze & deploy real-world datasets

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