Great — here are curated, practical resources and a compact plan to help you practice and level up data science skills, arranged by skill area and type of activity.

Foundational learning (courses & specializations)

• Intro to data science & Python: “Python for Data Science” (Coursera/IBM or DataCamp intro track) or free CS50’s Introduction to Computer Science (Harvard).
• Machine learning: Andrew Ng’s “Machine Learning” (Coursera) and “Deep Learning Specialization” (Coursera) or fast.ai Practical Deep Learning for Coders.
• Statistics & probability: “Statistics with R” (Coursera/Johns Hopkins) or Khan Academy statistics/probability.
• Data engineering basics: “Google Cloud Data Engineering” on Coursera or DataCamp’s data engineering tracks.
• Applied data science: IBM/Coursera Applied Data Science Specialization or Dataquest’s hands-on paths.

Hands-on practice platforms

• Kaggle — notebooks, datasets, and competitions (great for end-to-end projects and model-sharing).
• DrivenData — competitions with social-impact datasets.
• Zindi — Africa-focused competitions (good variety).
• Coderbyte/LeetCode (for algorithmic/data-structure practice relevant to data engineer/ML roles).
• HackerRank (SQL, Python, statistics challenges).

Datasets & project sources

• Kaggle Datasets (searchable, many cleaned examples).
• UCI Machine Learning Repository (classic datasets).
• Google Dataset Search.
• AWS Open Data Registry, Data.gov, World Bank Open Data, OpenStreetMap.
• GitHub repositories that collect project datasets (search for “awesome-datasets”).

Tools & environments to practice

• Python ecosystem: pandas, NumPy, scikit-learn, matplotlib/seaborn, plotly.
• ML/Deep Learning: TensorFlow, PyTorch, Keras.
• Notebooks & cloud: Jupyter, Google Colab (free GPU), Kaggle Notebooks.
• Databases & SQL: PostgreSQL, SQLite, practice with SQLBolt and Mode Analytics SQL tutorials.
• Big-data tools (when ready): Spark (PySpark), Dask.

Books (practical, project-focused)

• “Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow” — Aurélien Géron.
• “Python for Data Analysis” — Wes McKinney (pandas).
• “An Introduction to Statistical Learning” (ISLR) — Gareth James et al. (great practical intro).
• “Deep Learning” — Goodfellow, Bengio, Courville (theory reference).

Project ideas to build portfolio (in increasing complexity)

• Exploratory data analysis & visualization: analyze a public dataset and produce a short report / notebook.
• Predictive modeling: build and compare multiple models (baseline, tree-based, ensemble, simple NN).
• Time series forecasting: sales/energy/stock with evaluation and backtesting.
• NLP mini-project: sentiment analysis, topic modeling, or simple question-answering pipeline.
• Recommender system: collaborative filtering or hybrid on product/rating data.
• End-to-end project: data pipeline (ingest → clean → feature store → model → dashboard).
• Deploy a model: simple REST API with Flask/FastAPI and Docker, deploy on Hugging Face Spaces, Heroku, or cloud provider.

Competitions & applied practice tips

• Start with Kaggle “Getting Started” competitions or Titanic/House Prices to learn notebooks/workflows.
• Focus first on proper data cleaning, feature engineering, and cross-validation — that’s where most gains come.
• Read winning kernels (notebooks) to learn real-world tricks but reproduce them yourself first.
• Version your work (Git), write READMEs, create clear notebooks that explain decisions.

Learning-by-doing curricula & guided tracks

• fast.ai practical courses (deep learning-focused, strong project orientation).
• DataCamp/DataQuest interactive tracks — good for guided hands-on practice with incremental exercises.
• Coursera Applied Data Science or IBM Data Science Professional Certificate for structured capstone projects.

Interview & practical assessment prep

• SQL practice: Mode Analytics SQL tutorials, LeetCode & HackerRank SQL problems.
• ML system design: study case studies (e.g., recommender, fraud detection) and practice articulating tradeoffs.
• Take-home projects: practice delivering reproducible notebooks, clear visualizations, and a concise one-page summary.

Blogs, newsletters & videos (keep current & learn patterns)

• Blogs: Towards Data Science, Analytics Vidhya, Distill, The Gradient, RStudio blog (if using R).
• Official blogs: Google AI, OpenAI blog, DeepMind for advanced topics.
• YouTube channels: StatQuest (great for stats intuition), Sentdex, 3Blue1Brown (math intuition), Two Minute Papers (research highlights).
• Podcasts: Data Skeptic, Linear Digressions, Not So Standard Deviations.

Practical workflow & portfolio checklist

• Always include: problem statement, data sources, key EDA visuals, modeling approach, evaluation metrics, and conclusions/next steps.
• Use reproducible notebooks and requirements.txt or environment.yml.
• Host code on GitHub and create one-page project summaries (README + 3–5 visuals).
• Optional: blog posts or short videos explaining your projects for non-technical audiences.

Suggested 8-week practice plan (example)

• Weeks 1–2: Core Python/pandas + SQL + one small EDA project.
• Weeks 3–4: Machine learning fundamentals + modeling project (classification/regression).
• Week 5: Deepen with feature engineering + model evaluation; enter a beginner Kaggle competition.
• Week 6: Time series or NLP mini-project.
• Week 7: Model deployment basics (API + simple front-end or Streamlit).
• Week 8: Build portfolio page, polish 2–3 projects, prepare short demo pitch.

Final tips

• Prioritize consistent, small projects over long theory-only study. Real-world messy data is where skills grow fastest.
• Document decisions and failures — they make better portfolio stories than “it worked on first try.”
• Join communities (Kaggle forums, Slack/Discord groups, local meetups) for feedback and collaboration.

If you want, I can:

• Recommend a 4–6 project plan tailored to your current skill level (beginner/intermediate/advanced).
• List 10 data sets and one starter idea each. Tell me which and I’ll prepare it.