Open-sourced course notes for Artificial Intelligence and Data Science related topics, prepared in LaTeX
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Updated
Jul 30, 2026 - Jupyter Notebook
Open-sourced course notes for Artificial Intelligence and Data Science related topics, prepared in LaTeX
This repository contains code powering "The Next-Generation Data Science Education with WebAssembly" Quarto website demonstration.
Taller sobre el uso de R para sensoramiento remoto con aplicaciones en agricultura
Materials for the "Teaching Data Science Masterclass" at posit::conf(2023)
ODSC AI+: NLP Fundamentals in Python
An introductory text for project-based R and Python programming within a Data Science workflow
This is the instructor guide for the Data Science and AI Academy at North Carolina State University (NC State, NCSU)
📚 Explore essential NLP techniques through hands-on projects, from web scraping to machine learning classifiers, for building real-world applications.
Example Projects and their solutions in field of Artificial Intelligence with using python
Quarto templates for statistics and data science teaching material at Université Laval.
Data and code for Wilkerson, M. H. (2025) "Mapping the Conceptual Foundation(s) of Data Science Education." Harvard Data Science Review.
📘 [University Course Exemplar] A comprehensive, step-by-step technical manual for building and publishing interactive dashboards in Tableau Public.
Quarto course site for applied data analysis and statistical modelling.
A data-driven approach to designing an optimal Data Science curriculum. This project extracts skills from job postings, applies NLP and clustering techniques (K-Means, Hierarchical, DBSCAN), and maps industry demands to educational recommendations. Uses Python, Scikit-learn, OpenAI embeddings, and Seaborn for visualization.
An original Arabic Python learning experience inspired by DataCamp's Introduction to Python course, featuring educational videos, PDF slides, exercises, practice files, and structured learning materials for Arabic-speaking learners.
Python notebooks and supplemental materials for Wilkerson, Erickson, Lee, & Finzer (2025). How to be Choosy: Wrangling Big Datasets for the Classroom. In Teaching Statistics.
Materials accompanying 2022 ACM Conference on International Computing Education Research in Lugano, Switzerland
Chrome+ Ollama para Jupyter: traducción técnica, explicaciones claras de código, docstrings instantáneos. 100% local. Hardware Democrático 8GB RAM. Privacidad e inmersión total para estudiantes data science.
R package with datasets and utilities for statistics and data science teaching at Université Laval.
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