📊 Turn Data Science Skills Into Proof of Work Do not build only Titanic or Iris projects. Use real datasets, competitions, and deployed dashboards to show that you can solve a real problem. Competitions & practice platforms 1. Kaggle • Datasets, notebooks, competitions, and community solutions • Start with playground or beginner competitions • Link: https://www.kaggle.com/ 2. DrivenData • AI competitions focused on health, climate, education, and social impact • Includes beginner practice challenges and prize competitions • Link: https://www.drivendata.org/competitions/ 3. Zindi • AI challenges with prizes, rankings, and hiring opportunities • Good for Africa-focused and global datasets • Link: https://zindi.world/ 4. UCI Machine Learning Repository • Classic datasets for learning and experiments • Link: https://archive.ics.uci.edu/ 5. Google Dataset Search • Find public datasets across the web • Link: https://datasetsearch.research.google.com/ 6. data.gov.in • Indian government open datasets • Useful for India-specific projects • Link: https://www.data.gov.in/ DrivenData runs machine-learning competitions with social-impact partners and offers both prize competitions and beginner practice challenges; its listed prize competitions include projects with prizes such as $300,000 for geologic fault mapping. Zindi positions itself as an AI challenge platform where participants can build portfolios, compete for prizes, and connect with hiring opportunities. Top 1% proof-of-work portfolio 🎯 BUILD A DATA SCIENCE PORTFOLIO THAT GETS NOTICED For every project, include: 1. Problem • What real-world question are you answering? 2. Dataset • Source, size, date range, and license 3. Data cleaning • Missing values, duplicates, outliers, and assumptions 4. Analysis • EDA, charts, and key insights 5. Model • Baseline model, final model, and why you chose it 6. Evaluation • Correct metric, validation method, and error analysis 7. Deployment • Streamlit, Gradio, FastAPI, or Hugging Face demo 8. Repository • Clean README, requirements.txt, and reproducible notebook Example: “Predicted water-pump failures using Tanzania open data. Improved F1-score from 0.71 to 0.83. Deployed an interactive dashboard for field teams.” A strong data-science portfolio should show the full workflow: data cleaning, EDA, modelling, visualization, and deployment—not only a notebook with a final accuracy score. Projects that include a live Streamlit or FastAPI demo demonstrate that you can ship work, not just train a model. Weekly action ✅ This week: 1. Pick one real dataset. 2. Define one business question. 3. Clean the data and document every assumption. 4. Build one simple baseline model. 5. Deploy a small dashboard or demo. 6. Write a clear README with results and limitations. 📌 Rule: A deployed project with honest limitations is more impressive than a notebook claiming 99% accuracy. Claim your Free $5 Bonus Here: https://bit.ly/3wUxw09 LinkedIn profile 👇 https://www.linkedin.com/in/subarno-roy-3b2251374 Join our WhatsApp Channel 👇 https://whatsapp.com/channel/0029VbAi27y0lwghBe9mE42i WhatsApp Community Link 👇 https://chat.whatsapp.com/G8wPqAwwm1qHo1AdM8YPkM 1️⃣ Web Development and Web Design 📎 Channel Link: [ https://t.me/webdevelopment_official ] --- 2️⃣ DevOps 📎 Channel Link: [ https://t.me/DevOpsofficial ] --- 3️⃣ Software Development 📎 Channel Link: [ https://t.me/softwaredevelopmentofficial ] --- 4️⃣ Data Science 📎 Channel Link: [ https://t.me/datascience_official ] Share with your College Whatsapp Groups & Friends too All the best 👍👍

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