Week 1: Data Architectures, Management & Ingestion

• Seminal & Core References:
• Kleppmann, M. (2017). Designing Data-Intensive Applications. O’Reilly.
• Zaharia, M., et al. (2020). Spark: The Definitive Guide. O’Reilly.
• Armbrust, M., et al. (2021). Delta Lake: ACID Storage Over Cloud Object Stores. VLDB.
• Inclusive & Global South References:
• Viegas, E., Silva, D.F., & Soares, C. (2021). AutoML Trends in Latin America. LatinX in AI @ NeurIPS.
• Delgado, L.M. & Arévalo, J. (2022). MLOps Challenges in Healthcare AI Projects in Colombia. Revista Colombiana de Computación.

Week 2: Distributed Processing, Querying & Feature Engineering

• Seminal & Core References:
• Karau, H. & Warren, R. (2022). High Performance Spark. O’Reilly.
• Stonebraker, M. (2010). SQL vs NoSQL. Communications of the ACM.
• Zhang, C., et al. (2020). Real-Time Water Quality Prediction. Water Research.
• Inclusive & Global South References:
• Marulanda, C., Múnera, L., & Restrepo, J. (2020). Monitoring Industrial Wastewater with AI. Revista DYNA.

Week 3: ML/DL Pipelines, MLOps, Forecasting

• Seminal & Core References:
• Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow. O’Reilly.
• Taylor, S. & Letham, B. (2018). Prophet: Forecasting at Scale. The American Statistician.
• Breck, E., et al. (2019). ML Test Score. Google Research.
• Inclusive & Global South References:
• Ortiz, B., Reyes-Bastidas, J., & López, M.L. (2021). Hydrological Forecasting in Colombia. Revista Facultad de Ingeniería.

Week 4: Cloud, Geospatial Analytics & Capstone Delivery

• Seminal & Core References:
• Gorelick, N., et al. (2017). Google Earth Engine. Remote Sensing of Environment.
• Zhu, X., et al. (2020). Deep Learning in Remote Sensing. IEEE GRSM.
• Li, W., et al. (2021). Big Data for Environmental Monitoring. Journal of Environmental Informatics.
• Inclusive & Global South References:
• Espinosa, R., López-Bonilla, L., & García, C. (2023). Deforestation Detection in the Amazon with ML. Revista Peruana de Computación.