✅ Data Science Resume Tips 📊💼

To land data science roles, your resume should highlight problem-solving, tools, and real insights.

1️⃣ Contact Info (Top)
• Name, email, GitHub, LinkedIn, portfolio/Kaggle
• Optional: location, phone

2️⃣ Summary (2–3 lines)
Brief overview showing your skills + value
➡ “Data scientist with strong Python, ML & SQL skills. Built projects in healthcare & finance. Proven ability to turn data into insights.”

3️⃣ Skills Section
Group by type:
• Languages: Python, R, SQL
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
• Tools: Jupyter, Git, Tableau, Power BI
• ML/Stats: Regression, Classification, Clustering, A/B testing

4️⃣ Projects (Most Important)
List 3–4 impactful projects:
• Clear title
• Dataset used
• What you did (EDA, model, visualizations)
• Tools used
• GitHub + live dashboard (if any)

Example:
Loan Default Prediction – Used logistic regression + feature engineering on Kaggle dataset to predict defaults. 82% accuracy.
GitHub: [link]

5️⃣ Work Experience / Internships
Show how you used data to create value:
• “Built churn prediction model → reduced churn by 15%”
• “Automated Excel reports using Python, saving 6 hrs/week”

6️⃣ Education
• Degree or certifications
• Mention bootcamps, if relevant

7️⃣ Certifications (Optional)
• Google Data Analytics
• IBM Data Science
• Coursera/edX Machine Learning

💡 Tips:
• Show impact: “Increased accuracy by 10%”
• Use real datasets
• Keep layout clean and focused

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