├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | This is free and unencumbered software released into the public domain. 2 | 3 | Anyone is free to copy, modify, publish, use, compile, sell, or 4 | distribute this software, either in source code form or as a compiled 5 | binary, for any purpose, commercial or non-commercial, and by any 6 | means. 7 | 8 | In jurisdictions that recognize copyright laws, the author or authors 9 | of this software dedicate any and all copyright interest in the 10 | software to the public domain. We make this dedication for the benefit 11 | of the public at large and to the detriment of our heirs and 12 | successors. We intend this dedication to be an overt act of 13 | relinquishment in perpetuity of all present and future rights to this 14 | software under copyright law. 15 | 16 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, 17 | EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF 18 | MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 19 | IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR 20 | OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, 21 | ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR 22 | OTHER DEALINGS IN THE SOFTWARE. 23 | 24 | For more information, please refer to 25 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome Data Science and Python :snake: 2 | 3 | [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 4 | 5 | 6 | Passos iniciais: 7 | * [Telegram - Data Science & Python](https://t.me/datasciencepython) 8 | * [Como Fazer Perguntas Inteligentes](http://wiki.python.org.br/ComoFazerPerguntasInteligentes) 9 | * [Python - Por onde começar?](http://aprenda-python.blogspot.com.br/p/por-onde-comecar.html) 10 | > [por Vinicius Assef](https://twitter.com/viniciusban) 11 | * [Pro Git](https://git-scm.com/book/pt-br/v2) [(CC)](https://creativecommons.org/) 12 | > The entire Pro Git book, written by Scott Chacon and Ben Straub and published by Apress 13 | * [Open Source Guides](https://opensource.guide/) 14 | > Open source software is made by people just like you. Learn how to launch and grow your project. 15 | 16 | --- 17 | 18 | ## Table of Contents 19 | 20 | 21 | 22 | * [Articles](#articles) 23 | * [Awesome Lists](#awesome-lists) 24 | * [Books](#books) 25 | * [Courses](#courses) 26 | * [Podcasts](#podcasts) 27 | * [Youtube channels](#youtubechannels) 28 | * [Videos](#videos) 29 | 30 | 31 | 32 | --- 33 | 34 | ## Articles 35 | | Number | Name | Author | 36 | | :---: | :--- | :---: | 37 | 38 | ## Awesome Lists 39 | 42 | - Python 43 | - [by @kirang89](https://github.com/kirang89/pycrumbs) 44 | - [by @svaksha](https://github.com/svaksha/pythonidae) 45 | - [by @vinta](https://github.com/vinta/awesome-python) 46 | - [Asyncio](https://github.com/timofurrer/awesome-asyncio) - Asynchronous I/O in Python 3. 47 | - Big Data 48 | - [by @onurakpolat](https://github.com/onurakpolat/awesome-bigdata) 49 | - [by @zenkay](https://github.com/zenkay/bigdata-ecosystem) 50 | - [Hadoop](https://github.com/youngwookim/awesome-hadoop) 51 | - [Public Datasets](https://github.com/caesar0301/awesome-public-datasets) 52 | - Deep Learning 53 | - [by @ChristosChristofidis](https://github.com/ChristosChristofidis/awesome-deep-learning) 54 | - [by @endymecy](https://github.com/endymecy/awesome-deeplearning-resources) 55 | - [Data Engineering](https://github.com/igorbarinov/awesome-data-engineering) 56 | - [Streaming](https://github.com/manuzhang/awesome-streaming) 57 | 58 | ## Books 59 | | Number | Name | Author | 60 | | :---: | :--- | :---: | 61 | | 01 | [Use a Cabeça! Programação](http://www.altabooks.com.br/use-a-cabeca-programacao.html)| `Paul Barry` | 62 | | 02 | [Introdução à Programação com Python - 2ª Edição](https://www.amazon.com.br/gp/product/8575224085/ref=as_li_qf_sp_asin_il_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=8575224085&linkCode=as2&tag=livropython-20)| `Nilo Ney Coutinho Menezes` | 63 | | 03 | [Automate the Boring Stuff with Python](https://automatetheboringstuff.com/) [CC](https://creativecommons.org/)| `Al Sweigart` | 64 | | 04 | [Practical Data Science in Python](http://radimrehurek.com/data_science_python/)| `Radim Řehůřek` | 65 | | 05 | [Learn Data Science](http://learnds.com/)| `Nitin Borwankar`| 66 | 67 | ## Courses 68 | | Number | Name | Platform | Author | 69 | | :---: | :--- | :---: | :---: | 70 | | 01 | [Machine Learning - Stanford University](https://www.coursera.org/learn/machine-learning)|[Coursera](https://www.coursera.org/)| `Andrew Ng` | 71 | | 02 | [Data Science Math Skills](https://www.coursera.org/learn/datasciencemathskills)|[Coursera](https://www.coursera.org/)| `Daniel Egger, Paul Bendich` | 72 | | 03 | [Python for Data Science and Machine Learning Bootcamp](https://www.udemy.com/python-for-data-science-and-machine-learning-bootcamp/)|[Udemy](https://www.udemy.com/)| `Jose Portilla` | 73 | | 04 | [Machine Learning A-Z™: Hands-On Python & R In Data Science](https://www.udemy.com/machinelearning)|[Udemy](https://www.udemy.com/)| `Kirill Eremenko` | 74 | | 05 | [Data Science and Machine Learning with Python - Hands On!](https://www.udemy.com/data-science-and-machine-learning-with-python-hands-on/)|[Udemy](https://www.udemy.com/)| `Sundog Education by Frank Kane, Frank Kane` | 75 | | 06 | [Introduction to Data Science in Python](https://www.coursera.org/learn/python-data-analysis)|[Coursera](https://www.coursera.org/)| `Christopher Brooks` | 76 | | 07 | [Applied Machine Learning in Python](https://www.coursera.org/learn/python-machine-learning)|[Coursera](https://www.coursera.org/)| `Kevyn Collins-Thompson` | 77 | | 08 | [Applied Plotting, Charting & Data Representation in Python](https://www.coursera.org/learn/python-plotting)|[Coursera](https://www.coursera.org/)| `Christopher Brooks` | 78 | | 09 | [Applied Text Mining in Python](https://www.coursera.org/learn/python-text-mining)|[Coursera](https://www.coursera.org/)| `V. G. Vinod Vydiswaran` | 79 | | 10 | [Applied Social Network Analysis in Python](https://www.coursera.org/learn/python-social-network-analysis)|[Coursera](https://www.coursera.org/)| `Daniel Romero` | 80 | | 11 | [Machine Learning Foundations: A Case Study Approach](https://www.coursera.org/learn/ml-foundations)|[Coursera](https://www.coursera.org/)| `Carlos Guestrin, Emily Fox` | 81 | | 12 | [Machine Learning: Regression](https://www.coursera.org/learn/ml-foundations)|[Coursera](https://www.coursera.org/)| `Carlos Guestrin, Emily Fox` | 82 | | 13 | [Machine Learning: Classification](https://www.coursera.org/learn/ml-classification)|[Coursera](https://www.coursera.org/)| `Carlos Guestrin, Emily Fox` | 83 | | 14 | [Machine Learning: Clustering & Retrieval](https://www.coursera.org/learn/ml-clustering-and-retrieval)|[Coursera](https://www.coursera.org/)| `Carlos Guestrin, Emily Fox` | 84 | | 15 | [Neural Networks and Deep Learning](https://www.coursera.org/learn/neural-networks-deep-learning)|[Coursera](https://www.coursera.org/)| `Andrew Ng` | 85 | | 16 | [Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization]()(Próxima sessão: Aug 15 — Sep 11.)|[Coursera](https://www.coursera.org/)| `Andrew Ng` | 86 | | 17 |[Structuring Machine Learning Projects]()(Próxima sessão: Aug 15 — Sep 4.)|[Coursera](https://www.coursera.org/)| `Andrew Ng` | 87 | | 18 | [Convolutional Neural Networks]()(Em Breve)|[Coursera](https://www.coursera.org/)| `Andrew Ng` | 88 | | 19 | [Sequence Models]()(Em Breve)|[Coursera](https://www.coursera.org/)| `Andrew Ng` | 89 | 90 | ## Podcasts 91 | | Number | Name | Platform | Author | 92 | | :---: | :--- | :---: | :---: | 93 | |01|[DatabaseCast 2: Mineração de dados](http://databasecast.com.br/wp/databasecast-2-mineracao-de-dados/)| [DatabaseCast](http://databasecast.com.br/wp/sample-page/) | `Mauro Pichiliani, Wagner Crivelini, Ary Bressane` | 94 | |02|[DatabaseCast 53: Cientista de dados](http://databasecast.com.br/wp/databasecast-53-cientista-de-dados/)| [DatabaseCast](http://databasecast.com.br/wp/sample-page/) | `Mauro Pichiliani, Wagner Crivelini, Marcelo Glauco` | 95 | |03|[DatabaseCast 67: Data science na prática](http://databasecast.com.br/wp/databasecast-67-data-science-na-pratica/)| [DatabaseCast](http://databasecast.com.br/wp/sample-page/) | `Mauro Pichiliani, Wagner Crivelini, Diego Nogare, Tantravahi Aditya` | 96 | |04|[DatabaseCast 72: Ecossistema Hadoop](http://databasecast.com.br/wp/databasecast-72-ecossistema-hadoop/)| [DatabaseCast](http://databasecast.com.br/wp/sample-page/) | `Mauro Pichiliani, Wagner Crivelini, Felipe Gasparini` | 97 | |05|[DatabaseCast 74: Estatísticas](http://databasecast.com.br/wp/databasecast-74-estatisticas/)| [DatabaseCast](http://databasecast.com.br/wp/sample-page/) | `Mauro Pichiliani, Wagner Crivelini, Ricardo Rezende, Fabiano Amorim` | 98 | |06|[Dev na estrada #56 - Data Science](http://devnaestrada.com.br/2016/06/03/data-science.html)| [DNE](http://devnaestrada.com.br/) |`Fellipe Azambuja, Igor Leroy, Ramon Sanches, Raony Guimaraes` | 99 | |07|[Dragões de Garagem #43 Estatística](http://dragoesdegaragem.com/podcast/dragoes-de-garagem-43-estatistica/)| [Dragões de Garagem](http://dragoesdegaragem.com/sobre/) | `Luciano Queiroz, Lucas Camargos, Bruno Spacek, Rafael Calsaverini` | 100 | |08|[Dragões de Garagem #92 Inteligência artificial](http://dragoesdegaragem.com/podcast/dragoes-de-garagem-92-inteligencia-artificial/)| [Dragões de Garagem](http://dragoesdegaragem.com/sobre/) | `Lucas Camargos, Victor Caparica, Camila Laranjeira, Kherian Gracher, Antonio Nazaré, Igor Bastos` | 101 | |09|[Nerd Tech #5 - Machine Learning](https://jovemnerd.com.br/nerdcast/nerdtech/machine-learning/)| [NerdTech](https://jovemnerd.com.br/playlist/nerdtech/) | `Caio Gomes, Guilherme Silveira, Paulo Silveira` | 102 | |10|[PODEntender #019 Sobre Deep Learning](http://dragoesdegaragem.com/podentender/019-sobre-deep-learning)| [PODEntender](http://dragoesdegaragem.com/podentender) | `Antonio Marinho(Tonho), Carol Lacerda, Fábio Neves(Dalton), Dave Canton` | 103 | 104 |

Youtube channels

105 | 106 | | Number | Name | Author | 107 | | :---: | :--- | :---: | 108 | |01|[Peixe Babel](https://www.youtube.com/user/CanalPeixeBabel)| `Camila Laranjeira` | 109 | |02|[Deep Learning TV](https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ)| `Jagannath Rajagopal` | 110 | |03|[Nat and Friends](https://www.youtube.com/NatAndFriends)| `Natalie Hammel` | 111 | |04|[Sirajology](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A)| `Siraj Raval` | 112 | 113 | ## Videos 114 | | Number | Name | Author | 115 | | :---: | :--- | :---: | 116 | |01|[Getting Started with Machine Learning and Python](https://youtu.be/rCsbaHhvxfI)| `Bruno Godoi Eilliar` | 117 | 118 | 119 | ## License 120 | 121 | [![CC0](http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0/) 122 | --------------------------------------------------------------------------------