{"id":28429910,"url":"https://github.com/appsilon/datascience-python","last_synced_at":"2026-03-07T18:04:54.127Z","repository":{"id":48030968,"uuid":"516478547","full_name":"Appsilon/datascience-python","owner":"Appsilon","description":"Introduction to Data Science in Python by Appsilon","archived":false,"fork":false,"pushed_at":"2023-11-08T15:03:49.000Z","size":19403,"stargazers_count":11,"open_issues_count":0,"forks_count":4,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-07-04T18:44:46.361Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Appsilon.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2022-07-21T18:19:19.000Z","updated_at":"2024-01-21T18:47:11.000Z","dependencies_parsed_at":"2023-01-18T20:15:56.370Z","dependency_job_id":"84aa36da-f35e-46d2-838a-9010e111e2a9","html_url":"https://github.com/Appsilon/datascience-python","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Appsilon/datascience-python","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Appsilon%2Fdatascience-python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Appsilon%2Fdatascience-python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Appsilon%2Fdatascience-python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Appsilon%2Fdatascience-python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Appsilon","download_url":"https://codeload.github.com/Appsilon/datascience-python/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Appsilon%2Fdatascience-python/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30225491,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-07T17:00:40.062Z","status":"ssl_error","status_checked_at":"2026-03-07T17:00:39.026Z","response_time":53,"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":"2025-06-05T13:39:22.861Z","updated_at":"2026-03-07T18:04:54.121Z","avatar_url":"https://github.com/Appsilon.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Introduction to Data Science in Python by Appsilon\n\n## Introduction\n\nWelcome to the course _Introduction to Data Science in Python by Appsilon_!\n\n## Target audience\n\nThis course aims to introduce people that know how to code in Python into the Data Science world.\nIn particular I show tricks and tips useful for STEM/economic students.\nOne of secondary goals is to show students how use **free** tools that are **industry standards** at the same time instead of Matlab/Statistica/SAS and so on.\n\n## Covered topics\n\n0. The course starts with introducing what does Data Scientist do in his work and why this job is so important in XXI century. Then we start the technical part of the course.\n1. `numpy` - numbers and vectors, fundamentals of all calculations in Python\n2. `pandas` - data frames - SQL-like, in-memory data, fundamentals of data processing in Python\n3. `matplotlib` and `plotly` - plots, basics of data visualization\n4. `scikit-learn` - introduction to machine learning, examples from the go-to library in Python\n5. `streamlit`, `quarto`, `fastapi` - simple, useful and creative ways to share your work in Python and to generate beautiful reports\n\nApart from those libraries I present and benchmark the `polars` library - a high-performant replacement for `pandas` if you work datasets of sizes 0.5GB - 5GB and pandas starts to be too slow.\n\n## Course materials\n\nAll course materials are located either here or on google drive.\nCode and small datasets are in repo, while large size datasets are located on google drive.\n\nI suggest using `html` files, generated from `qmd` and `ipynb` with `quarto`.\n\nGuide to setup an environment included in the introduction presentation.\n\ntl;dr You can try \n```\nconda create -n ds-course python=3.10\nconda activate ds-course\npip install -r requirements.txt\n```\n\n### Homeworks\n\nEach lecture has also some homework assignment.\nFor every homework, there's provided solution in a separate directory.\nNote that solutions are not necessarily the best possible, but may present some interesting approach.\nVery often there are multiple ways you can approach the same problem. \n\n## License\n\nThe course has been prepared by [Piotr Pasza Storożenko](https://pstorozenko.github.io/) from [Appsilon](http://appsilon.com/).\nIt is available under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.\nFeel free to use these materials for your use, you just have to attribute the original author.\n\nSome exercise have been inspired by the exercises author had to solve while studying.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fappsilon%2Fdatascience-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fappsilon%2Fdatascience-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fappsilon%2Fdatascience-python/lists"}