milarobotlearningcourse/data_science

How milarobotlearningcourse/data_science works

This is a bilingual course-resource repository, not an application runtime. Its main user journey is to study the recommended demo notebooks, progressing from data-science foundations through analysis and machine learning; separate branches cover project milestones, tutorials, and historical labs. The README documents the content and suggested sequences, but no notebook source was sampled, so internal notebook behavior and cross-notebook data flow are not represented.

Main entry points: 02_data_exploration_and_visualization.ipynb Exploration and plots · 00_setup_git_python.ipynb Setup and Git · 01_numpy.ipynb NumPy basics · 02_pandas.ipynb Pandas basics

Components 20

Course demos

Setup and Git demo_1/notebooks/en/00_setup_git_python.ipynb
NumPy basics demo_1/notebooks/en/01_numpy.ipynb
Pandas basics demo_1/notebooks/en/02_pandas.ipynb
ML introduction demo_1/notebooks/en/03_ml_bridge.ipynb
Data acquisition demo_2/notebooks/en/01_data_acquisition_and_cleaning.ipynb
Exploration and plots demo_2/notebooks/en/02_data_exploration_and_visualization.ipynb
LLM and RAG demo_2/notebooks/en/04_llm_and_rag.ipynb
PandasAI demo_2/notebooks/en/05_pandasai.ipynb
Scikit-learn demo_3/notebooks/en/02_scikit_learn_basics.ipynb
Validation search demo_4/notebooks/en/01_validation_and_hyperparameter_search.ipynb
Bootstrap intervals demo_4/notebooks/en/02_bootstrap_confidence_intervals.ipynb
Titanic project demo_4/notebooks/en/03_titanic_end_to_end.ipynb
GPT from scratch demo_5/notebooks/fr/01_gpt_from_scratch.ipynb
Image and CNN basics demo_6/notebooks/fr/01_caracteristiques_images_cnn.ipynb

Project milestones

NHL data stage milestone-1/README_en.md
Expected-goals models milestone-2/README_en.md

Tutorials

Git tutorial tutoriels/git/en/git_tutorial.ipynb
Python tutorial tutoriels/python/en/python.ipynb

Historical labs

Past course labs anciens_labs/README.md

Other components

Learner

Connections 19

Learner → Setup and Git: starts with README.md
Setup and Git → NumPy basics: then studies README.md
NumPy basics → Pandas basics: then studies README.md
Pandas basics → ML introduction: then studies README.md
Learner → Data acquisition: studies README.md
Data acquisition → Exploration and plots: then studies README.md
Exploration and plots → LLM and RAG: optionally studies README.md
Exploration and plots → PandasAI: optionally studies README.md
Learner → Scikit-learn: studies README.md
Learner → Validation search: studies README.md
Validation search → Bootstrap intervals: then studies README.md
Bootstrap intervals → Titanic project: then studies README.md
Learner → GPT from scratch: studies README.md
Learner → Image and CNN basics: studies README.md
Learner → NHL data stage: reads instructions README.md
NHL data stage → Expected-goals models: progresses to README.md
Learner → Git tutorial: studies README.md
Learner → Python tutorial: studies README.md
Learner → Past course labs: consults README.md

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