An Introduction to Statistical Learning
Foundational text for regression, classification, regularization, trees, support vector machines, and deep learning, with R and Python labs.
Readings and reference
Resources
Foundational texts, then complementary educational and programming references.
Statistical learning and predictive modeling
Tidy Modeling with R
R workflows for preprocessing, resampling, tuning, and model comparison.
scikit-learn user guide
Python reference for predictive modeling and evaluation.
Preprocessing and data leakage
Examples of keeping preprocessing inside a validation pipeline.
Educational machine learning
Big Data and Education
Complementary open videos by Ryan S. Baker (2025, 9th edition) on validation, feature engineering, knowledge tracing, and embeddings.
Learning Analytics Methods and Tutorials
Supplementary open-access R tutorials, including predictive modelling in learning analytics (Saqr and López-Pernas, 2024).
Embeddings and language models
Semantic textual similarity
Sentence Transformers documentation for comparing text embeddings with cosine similarity.
Programming tools and reproducibility
R for Data Science
Additional guidance for data preparation and visualization in R.
pandas introductory tutorials
Additional guidance for data preparation in Python.