{"id":16872963,"url":"https://github.com/vuillaut/datascience_intro","last_synced_at":"2026-04-17T00:02:28.635Z","repository":{"id":162134602,"uuid":"636736640","full_name":"vuillaut/datascience_intro","owner":"vuillaut","description":"Introductive Course to Data Science","archived":false,"fork":false,"pushed_at":"2026-04-01T14:02:23.000Z","size":91117,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-04-01T14:53:13.188Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/vuillaut.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2023-05-05T14:21:38.000Z","updated_at":"2026-04-01T14:02:32.000Z","dependencies_parsed_at":"2025-02-19T08:34:52.903Z","dependency_job_id":null,"html_url":"https://github.com/vuillaut/datascience_intro","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/vuillaut/datascience_intro","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vuillaut%2Fdatascience_intro","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vuillaut%2Fdatascience_intro/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vuillaut%2Fdatascience_intro/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vuillaut%2Fdatascience_intro/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/vuillaut","download_url":"https://codeload.github.com/vuillaut/datascience_intro/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vuillaut%2Fdatascience_intro/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31909235,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-16T18:22:33.417Z","status":"ssl_error","status_checked_at":"2026-04-16T18:21:47.142Z","response_time":69,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-13T15:18:18.415Z","updated_at":"2026-04-17T00:02:28.578Z","avatar_url":"https://github.com/vuillaut.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Introductive Course to Data Science\n   \nThomas Vuillaume   \n\ncontact me at firstname.name[at]lapp.in2p3.fr\n\n## Running online\n\n- [Google colab](https://colab.research.google.com/github/vuillaut/datascience_intro/)\n- [mybinder](https://mybinder.org/v2/gh/vuillaut/datascience_intro/HEAD)\n\n\n## Courses content:\n\n### Slides\n\n[https://vuillaut.github.io/lectures/data_science_intro/1](https://vuillaut.github.io/lectures/data_science_intro/1)\n\n### Course 0: Python and environment setup\nThese are reminders of prerequisite for this course\n\nPython basics:\n- https://jckantor.github.io/CBE30338/01.02-Python-Basics.html\n- https://www.kaggle.com/learn/python\n\nEnv. setup: \n- [conda](https://www.anaconda.com/products/individual)\n\nGit:\n- https://education.github.com/git-cheat-sheet-education.pdf\n\n\n### Part 1: coding environment and Jupyter \n\n- Introduction and environment setup\n- Working with [Jupyter](1.jupyter)\n\n### Part 2: [Numpy](2.numpy)\n\n### Part 3: [Pandas](3.pandas)\n\n### Part 4: [Matplotlib](4.matplotlib)\n\n### Part 5: [Machine learning](5.machine_learning)\n\n### Part 6: [Practical work on a real case]\n\n\n\n# Resources\n\n- [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)\n- [Machine Learning (Lecture 1)](https://indico.cern.ch/event/619370/) --- [Michael Kagan](https://www.linkedin.com/in/michael-kagan-06292616/) (SLAC)\n- [Machine Learning (Lecture 2)](https://indico.cern.ch/event/619371/) --- [Michael Kagan](https://www.linkedin.com/in/michael-kagan-06292616/) (SLAC)\n- [Deep Learning and Vision](https://indico.cern.ch/event/619372/) --- [Jonathon Shlens](https://research.google.com/pubs/JonathonShlens.html) (Google Research)\n- [Fidle - Deep Learning training course](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvuillaut%2Fdatascience_intro","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvuillaut%2Fdatascience_intro","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvuillaut%2Fdatascience_intro/lists"}