Great — here’s a compact, practical list of hands-on data-science projects for beginners, each with what you’ll learn, suggested datasets or sources, and easy extensions to level up. Pick 3–5 to build a portfolio that shows breadth (cleaning, visualization, modeling, communication).

1. Titanic survival prediction (classification)

• Skills: data cleaning, feature engineering, EDA, logistic regression / decision trees.
• Datasets: Titanic dataset (Kaggle).
• Extensions: try ensemble methods, explain predictions with SHAP/LIME, build a simple web app for prediction.
• Time: 4–10 hours.

2. House prices prediction (regression)

• Skills: feature engineering, handling skewed targets, regularization (Ridge/Lasso), model evaluation.
• Datasets: Boston alternatives (Kaggle House Prices), Zillow public data.
• Extensions: build feature pipeline, compare tree-based models vs linear, create interactive dashboard of predicted vs actual prices.
• Time: 8–15 hours.

3. Customer churn analysis (classification + business framing)

• Skills: cohort analysis, imbalance handling (SMOTE, class weights), ROC/AUC, business metrics.
• Datasets: Telco Customer Churn (Kaggle) or synthetic telecom datasets.
• Extensions: construct retention strategy recommendations, compute cost-benefit for interventions.
• Time: 6–12 hours.

4. Exploratory analysis of a public dataset (EDA + storytelling)

• Skills: data cleaning, visualization, statistical summaries, writing a data story.
• Datasets: NYC 311 complaints, NYC taxi trips, US Census, COVID data, open government datasets.
• Extensions: publish a blog post or slide deck with visuals and insights; add predictive elements.
• Time: 4–12 hours.

5. Text sentiment analysis (NLP, classification)

• Skills: text cleaning, TF-IDF/word embeddings, simple classifiers (Naive Bayes, logistic), evaluation.
• Datasets: IMDB movie reviews, Twitter sentiment datasets.
• Extensions: try transformer embeddings (Sentence-BERT), build a simple sentiment API or dashboard.
• Time: 6–15 hours.

6. Image classification (intro to computer vision)

• Skills: image preprocessing, CNN basics (transfer learning), accuracy/confusion matrix.
• Datasets: MNIST, CIFAR-10, Fashion-MNIST, Kaggle image challenges.
• Extensions: fine-tune pre-trained models, explainability (Grad-CAM), deploy model to mobile/web.
• Time: 8–20 hours.

7. Time series forecasting (sales or weather)

• Skills: time series decomposition, lag features, ARIMA/Prophet/ETS, cross-validation for time series.
• Datasets: Retail sales datasets, NOAA weather, M4/M5 competitions.
• Extensions: build dashboard with forecasts and confidence intervals, compare Prophet vs ML models.
• Time: 6–18 hours.

8. Movie recommender system (collaborative + content-based)

• Skills: matrix factorization, similarity metrics, cold-start handling, evaluation metrics (precision@k).
• Datasets: MovieLens.
• Extensions: hybrid recommendation, create web UI showing recommendations.
• Time: 8–20 hours.

9. Credit scoring / loan default prediction (classification + ethics)

• Skills: imbalanced classes, model fairness, feature importance, regulatory awareness.
• Datasets: Lending Club (historical), UCI credit datasets.
• Extensions: analyze model fairness across groups; build explainability reports.
• Time: 8–20 hours.

10. Bike-share demand analysis and prediction

• Skills: EDA, time features, weather joins, regression/time-series modeling.
• Datasets: Citi Bike, Capital Bikeshare, Kaggle bike-sharing datasets.
• Extensions: realtime dashboard of expected demand by station; suggest rebalancing plan.
• Time: 6–15 hours.

11. Build an interactive dashboard (communication + visualization)

• Skills: dashboard tools (Tableau, Power BI, or Plotly Dash/Streamlit), KPI design, data refresh.
• Datasets: any of the above projects.
• Extensions: add filters, drilldowns, scheduled data updates, export features.
• Time: 4–12 hours.

12. Web scraping + analysis project

• Skills: scraping (BeautifulSoup/requests/Selenium), data cleaning, respectful scraping and rate-limiting.
• Data targets: product prices, job listings, news headlines.
• Extensions: set up a pipeline to scrape periodically and monitor trends or price changes; alerting.
• Time: 6–15 hours.

13. A/B test analysis (statistics + experimentation)

• Skills: hypothesis testing, power analysis, lift calculation, Bayesian vs frequentist approaches.
• Datasets: simulated experiments or public A/B datasets.
• Extensions: design an experiment, compute required sample size, analyze uplift and significance.
• Time: 4–8 hours.

14. Fraud detection (anomaly detection)

• Skills: outlier detection, unsupervised methods (isolation forest), precision/recall tradeoffs.
• Datasets: credit card fraud (Kaggle), synthetic fraud datasets.
• Extensions: build a small pipeline to flag transactions and prioritize alerts.
• Time: 8–18 hours.

15. End-to-end mini product: “Predict & Explain” app

• Skills: everything above plus deployment (Streamlit/Flask), Docker basics, simple CI.
• Idea: choose one predictive model (e.g., house prices, churn) and ship a web UI that inputs features, returns prediction plus explanation.
• Extensions: add authentication, logging, dataset upload, model retraining trigger.
• Time: 12–40 hours.

Practical tips for beginners

• Start simple: focus first on clear EDA and a baseline model. Then iterate.
• Use proper train/validation/test splits and track metrics in a notebook or lightweight experiment tracker (MLflow, Weights & Biases free tier).
• Keep projects reproducible: include a README, requirements.txt, and a notebook + cleaned dataset sample.
• Showcase: host code on GitHub, include a short project description and 2–3 clean visuals; optionally add a deployed demo.
• Tech stack suggestions: Python (pandas, scikit-learn, Matplotlib/Seaborn, Plotly), Jupyter/VS Code; for NLP: nltk/spacy, for CV: PyTorch or TensorFlow (or use transfer learning via Keras); for dashboards: Streamlit or Dash.

If you want, I can:

• Suggest a 4-project beginner roadmap (which to do in what order), or
• Generate a starter checklist and skeleton repo (README + folder structure + sample notebook) for one project you pick.

Which would you like next?