Real‑World Projects & Next Steps

Capstone Projects – Career Roadmap

Building a portfolio of end‑to‑end projects is the fastest way to land a data‑science role.

Suggested projects (pick at least two)

1. Stock Price Prediction – Use historical price data, engineer features (moving averages, RSI), and train a regression model (Linear Regression, XGBoost, LSTM). Evaluate with RMSE.
2. Movie Recommendation System – Implement collaborative filtering (matrix factorization) and content‑based filtering using the MovieLens dataset.
3. Customer Churn Prediction – Clean a telco dataset, engineer features, train a classification model (Random Forest, XGBoost), and compute ROC‑AUC.
4. Spam Email Classifier – Use NLP preprocessing, TF‑IDF vectors, and train a Logistic Regression or a simple CNN on word embeddings.
5. Image Classifier – Fine‑tune a pre‑trained ResNet50 on a custom dataset (e.g., cats vs. dogs).

Project checklist

• Problem definition – clear business objective.
• Data acquisition – source, download, or API.
• Exploratory Data Analysis – visualizations, summary stats.
• Data cleaning & preprocessing – missing values, scaling, encoding.
• Model building – baseline → tuned model.
• Evaluation – appropriate metrics, cross‑validation.
• Deployment – Flask/FastAPI endpoint, Docker container, CI/CD.
• Documentation – README, notebooks, slides.

Deployment & Production

• APIs: FastAPI to serve model predictions.
• Containerization: Dockerfile example.

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

• Cloud basics: Deploy on AWS Elastic Beanstalk, GCP Cloud Run, or Azure App Service.

Learning path forward

• Advanced topics: Time‑series forecasting (Prophet), NLP with Transformers, Reinforcement Learning.
• Certifications: Google Cloud Professional Data Engineer, AWS Certified Machine Learning.
• Community: Contribute to open‑source, write blog posts, present at meetups.

Final words

"Projects = what gets you hired" – showcase them on GitHub, include clear documentation, and write a short blog post describing the approach and results.