CONTENT

Week 1

  1. Introduction : Systems Thinking in Data-Driven Engineering
  1. Modern analytical architectures
  2. Mapping data pipelines to use cases
  3. Assessment: Concept map of a smart engineering system

2. Data Management and Scalable Engineering Systems

  • 3h – Architectures: Data lakes, Delta Lake, streaming vs batch
  • 3h – Tools: SQL, NoSQL, Delta tables, Parquet, ingestion with Kafka/MQTT
  • 3h – Practice: Build a data pipeline using Pandas, SQL, and Kafka
  • 3h – Capstone Kickoff: Define project goal, dataset, and tech stack

Key Outcomes:

  • Design data pipelines for high-volume engineering datasets
  • Efficient querying using SQL-like interfaces
  • Launch capstone project with clear objectives

Week 2 

  • 3. Data Processing, Querying, and Feature Engineering
  • 3h – High Volume Distributed processing (Dask, Spark), time-windowed analytics
  • 3h – Advanced data cleaning, temporal aggregation, joins
  • 3h – Practice: Use Dask/PySpark on sensor & spatial data
  • 3h – Capstone: Build pipeline skeleton, run initial EDA
    • – Satellite/open data integration
    • – Tools: GeoPandas, Rasterio
    • – Assessment: Remote sensing notebook + map visuals

Key Outcomes:

  • Handle large time-series and sensor data efficiently
  • Engineer features for machine learning and forecasting
  • Develop reproducible workflows for data prep

Week 3

  • 4. Analytics, AI Models, and Scalable Inference
  • 3h – ML/DL model selection: forecasting, classification, clustering
  • 3h – MLOps: model versioning, training pipelines, drift detection
  • 3h – Practice: Train & log ML models using MLflow; deploy via FastAPI
  • 3h – Capstone: Model training & first deployment demo

Key Outcomes:

  • Implement and track scalable machine learning workflows
  • Set up real-time or batch inference systems
  • Translate data science models into deployed services

Week 4

5. Real-Time Decision Systems and Final Project Delivery

  • 3h – Decision systems: optimization, control, dashboards
  • 3h – Cloud deployment (Colab, AWS, Streamlit, Docker)
  • 3h – Practice: Build dashboard + run cloud deployment
  • 3h – Capstone: Final testing + presentations

Key Outcomes:

  • Integrate analytics into actionable, real-time dashboards
  • Complete and deploy full-stack capstone project
  • Deliver technical presentation and documentation

Capstone Summary

Deliverables by end of Week 4:

  • A GitHub repo with data pipeline, model, and deployment scripts
  • Streamlit/FastAPI interface or cloud-hosted dashboard
  • Documentation + 10-min presentation (recorded or live)