Referencias y lecturas recomendadas

Las fuentes acompañan el recorrido desde formulación de problemas y exploración hasta modelado, evaluación y comunicación.

Fundamentos de Ciencia de Datos

• Tukey, J. W. (1977) — Exploratory Data Analysis.
• Provost, F. & Fawcett, T. (2013) — Data Science for Business.
• Donoho, D. (2017) — “50 Years of Data Science”.
• Kelleher, J. D. & Tierney, B. (2018) — Data Science.
• Wickham, H., Çetinkaya-Rundel, M. & Grolemund, G. (2023) — R for Data Science, 2nd ed.
• McKinney, W. (2022) — Python for Data Analysis, 3rd ed.
• Kuhn, M. & Johnson, K. (2019) — Feature Engineering and Selection.
• Kaufman, L. & Rousseeuw, P. J. (1990) — Finding Groups in Data.
• Saito, T. & Rehmsmeier, M. (2015) — Precision-Recall Plot for imbalanced data.

Modelado y aprendizaje estadístico

• James, G., Witten, D., Hastie, T. & Tibshirani, R. (2021) — An Introduction to Statistical Learning, 2nd ed.
• Hastie, T., Tibshirani, R. & Friedman, J. (2009) — The Elements of Statistical Learning, 2nd ed.
• Kuhn, M. & Johnson, K. (2013) — Applied Predictive Modeling.
• Breiman, L. (2001) — “Statistical Modeling: The Two Cultures”.

Visualización y comunicación

• Cleveland, W. S. (1993) — Visualizing Data.
• Cairo, A. (2016) — The Truthful Art.
• Knaflic, C. N. (2015) — Storytelling with Data.

Procesos y reproducibilidad

• Shearer, C. (2000) — “The CRISP-DM Model”.
• Wilson, G. et al. (2017) — “Good Enough Practices in Scientific Computing”.

Fuentes, preparación y exploración

• Lohr, S. L. (2021) — Sampling: Design and Analysis, 3rd ed.
• Cochran, W. G. (1977) — Sampling Techniques, 3rd ed.
• Wickham, H. (2014) — “Tidy Data”.
• McKinney, W. (2022) — Python for Data Analysis, 3rd ed.
• Cleveland, W. S. (1993) — Visualizing Data.
• Freedman, D., Pisani, R. & Purves, R. (2007) — Statistics, 4th ed.

Forecasting, operación y gobierno

• Hyndman, R. J. & Athanasopoulos, G. — Forecasting: Principles and Practice.
• Box, G. E. P., Jenkins, G. M. et al. — Time Series Analysis: Forecasting and Control.
• Kleppmann, M. (2017) — Designing Data-Intensive Applications.
• Sculley, D. et al. (2015) — “Hidden Technical Debt in Machine Learning Systems”.
• Breck, E. et al. — “The ML Test Score: A Rubric for ML Production Readiness”.
• DAMA International — DAMA-DMBOK.

IA, sesgos y documentación responsable

• Mitchell, M. et al. (2019) — “Model Cards for Model Reporting”.
• Gebru, T. et al. (2021) — “Datasheets for Datasets”.
• Barocas, S., Hardt, M. & Narayanan, A. — Fairness and Machine Learning.
• NIST — AI Risk Management Framework.