🧠 How to Build a Data Science Portfolio (with GitHub & Streamlit)

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In 2025, having a strong data science portfolio is more important than ever. Whether you're a fresher, career switcher, or someone looking to break into the AI industry, your portfolio acts as your practical resume. It shows recruiters you don’t just “know” data science, you can apply it.

This guide will help you build a standout data science portfolio using GitHub and Streamlit, both free and beginner-friendly tools.

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📌 Why a Portfolio Matters in 2025

• 💼 Employers want proof of skills, not just certificates

• 🧠 Projects show how you think, solve problems, and communicate

• 🌐 A shareable portfolio gives you visibility online (GitHub, LinkedIn, etc.)

• 🚀 It builds confidence and increases your chances of landing interviews

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1️⃣ Choose 3–5 Real-World Projects

Start with these project ideas:

Project Type	Example Idea
EDA & Visualization	COVID-19 Data Dashboard with Plotly
ML Classification	Loan Approval Predictor
NLP	Resume Keyword Extractor
Time Series	Weather Forecast Model
Streamlit App	Diabetes Risk Prediction App

Use public datasets from Kaggle, UCI, or APIs like OpenWeather, IMDb, etc.

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2️⃣ . Upload Projects to GitHub

Use a clean folder structure:

Your README.md should include:

• Project Title

• Problem Statement

• Dataset Source

• Tools Used

• Step-by-Step Approach

• Key Results

• Screenshot or Live Demo

Example GitHub Repo:
👉 https://github.com/yourusername/loan-prediction-app

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3️⃣ Build & Deploy with Streamlit

Turn your ML or EDA project into an interactive web app.

Steps:

1. Install Streamlit:
   pip install streamlit

2. Create app.py file:

3. Run it locally:
   streamlit run app.py

4. Deploy on Streamlit Cloud:
   https://streamlit.io/cloud

Now you have a live link to share your project great for resumes!

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4️⃣ . Organize a Portfolio Page

Use:

• GitHub Pages

• Notion Page

• Medium Blog

• Personal Website (optional)

Include:

• About Me

• GitHub Projects

• Resume (PDF)

• LinkedIn Profile

• Contact Info

Example Portfolio:
👉 https://yourusername.github.io

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5️⃣ . Promote Your Work

Once you’ve built your portfolio:

• Post updates on LinkedIn, X (Twitter), and GitHub

• Write short blogs or threads explaining your projects

• Join DS communities on Reddit, Discord, etc.

• Engage with hashtags like #DataSciencePortfolio, #100DaysOfData

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✅ Final Tips

• Keep your code clean and documented

• Add screenshots or GIFs to show output

• Use requirements.txt so others can run your code

• Update regularly, treat your portfolio like your LinkedIn!

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🎯 Conclusion

A well-built data science portfolio will speak louder than your resume. With GitHub and Streamlit, you don’t need to be a full-stack developer to show your work. Start small, grow weekly, and be consistent.

Your portfolio is your gateway to opportunities . Make it count.