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.
- 📘 Step 1: Learn Python Fundamentals
- ⚙️ Step 2: Set Up the Python Environment
- ☁️ Optional: Use Google Colab (No Setup Required)
- 🧠 Step 3: Practice with Core Python Programs
- 📊 Step 4: Data Cleaning, Preparation & Visualization
- 📂 Step 5: Work with Real Datasets from Kaggle
- 🚀 Step 6: Deploy with Streamlit
- 🧭 Learning Roadmap
Start with a structured introduction to the language:
🔗 W3Schools – Python Introduction
Reference Notes for Beginners:
Learn to install Python on any OS:
🔗 RealPython – Installing Python
Jupyter Notebook is beginner-friendly and widely used in data science:
Tip
📝 Tip: After creating a .ipynb file, right-click → "Open with Jupyter Notebook" for quick access.
Run Python notebooks in the browser without installing anything:
- 🔗 TutorialsPoint – Google Colab Intro
- 🔗 GeeksforGeeks – Getting Started with Colab
- 🔗 Google Colab – Official Site
Sharpen your skills with programs covering:
✅ Loops, conditionals, lists, dictionaries ✅ Functions, file handling, exception handling
🔗 Python Programming Repository
🔗 Practice Python Problems – PracticePython.org
Learn to clean and preprocess messy data:
🔗 Data Cleaning & Visualization Folder
🔗 Towards Data Science – Data Cleaning Guide
Use Python libraries like matplotlib, seaborn, and plotly to visualize data:
🔗 Seaborn Tutorial – Official Docs
🔗 Plotly in Python – Intro Guide
Kaggle is a hub for datasets and machine learning competitions.
- Create a Kaggle account.
- Download datasets (
.zip) and extract them. - Use
pandas.read_csv()to load CSV files.
Build interactive web apps for data science projects with Streamlit.
🔗 Streamlit Apps – Code Repository
| 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 |
Stay connected, explore beginner-friendly projects, and learn Python on the go:
📘 Python Programming App (CodeFobe) – Play Store: Interactive Python tutorials and a built-in code editor – great for learning anywhere!
👨💻 Learn Python: Coding App – Play Store: Practice Python basics and mini-projects through fun mobile challenges.
-
🧪 5 Fun Python Projects for Absolute Beginners – KDnuggets: Try simple projects like number guessing, countdown timer, and quiz games to get started.
-
🧰 Python
functools&itertools: 7 Super Handy Tools – KDnuggets: Discover powerful standard libraries to simplify and optimize your Python code. -
🤖 Python Automation: A Complete Guide – DataCamp: Learn to automate tasks such as file handling, web browsing, and data reporting.
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⚙️ 6 Python Task Automation Ideas with Examples – Monterail: Implement real-world automation like image resizing, bulk emails, and folder monitoring.
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🧠 Top 100 Python Projects with Source Code – CopyAssignment: A huge collection of practical projects for all skill levels — from beginners to advanced.
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📊 Publish Interactive Data Visualizations for Free – Towards Data Science: Learn how to share your visualizations online using Marimo, Python, and zero hosting cost.
📢 Follow the Python Programming Channel on WhatsApp Get updates, daily coding challenges, tips, and community support right on your phone.
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