Recursos del curso

Bibliografía base (libros)

• Ciencia de Datos para Gente Sociable (Brust, 2023)
• Mining of Massive Datasets (Leskovec, Rajaraman, Ullman, 2023)
• Feature Engineering for Machine Learning (Zheng, Casari, 2018)
• Representation in Machine Learning (Murty, Avinash, 2023)

Documentación oficial (herramientas)

• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn (User Guide)
• Prefect 2 – Flows
• Tasks
• Apache Airflow
• PySpark API
• Spark Docs
• Spark Structured Streaming
• Fairlearn
• DVC
• Docker
• pytest
• MkDocs
• Material for MkDocs

Rutas de aprendizaje Kaggle (notebooks/lecturas)

• Pandas
• Creating, Reading and Writing
• Indexing, Selecting & Assigning
• Summary Functions and Maps
• Grouping and Sorting
• Data Visualization
• Hello Seaborn
• Distributions
• Choosing Plot Types and Custom Styles
• Data Cleaning
• Handling Missing Values
• Inconsistent Data Entry
• Scaling and Normalization
• Intermediate ML
• Data Leakage
• Pipelines
• Feature Engineering
• Creating Features
• What is Feature Engineering?
• Target Encoding
• Principal Component Analysis
• Time Series
• Trend
• Seasonality
• Forecasting with Machine Learning
• Hybrid Models
• Intro to AI Ethics
• Identifying Bias in AI
• AI Fairness
• Model Cards
• ML Explainability
• Use Cases for Model Insights
• Permutation Importance