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https://github.com/datascience-python/awesome-datascience-python

Awesome list Data Science and Python. :snake:
https://github.com/datascience-python/awesome-datascience-python

List: awesome-datascience-python

artificial-intelligence awesome-list books data-science machine-learning podcast python statistics youtube-channel

Last synced: about 2 months ago
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Awesome list Data Science and Python. :snake:

Host: GitHub
URL: https://github.com/datascience-python/awesome-datascience-python
Owner: datascience-python
License: unlicense
Created: 2017-03-10T14:31:13.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2017-08-10T21:08:15.000Z (almost 7 years ago)
Last Synced: 2024-05-09T02:25:10.259Z (2 months ago)
Topics: artificial-intelligence, awesome-list, books, data-science, machine-learning, podcast, python, statistics, youtube-channel
Homepage: https://datascience-python.github.io/
Size: 11.7 KB
Stars: 64
Watchers: 16
Forks: 9
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

ultimate-awesome - awesome-datascience-python - Awesome list Data Science and Python. :snake:. (Other Lists / Julia Lists)

README

        # Awesome Data Science and Python :snake: 

[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

Passos iniciais:

* [Telegram - Data Science & Python](https://t.me/datasciencepython)

* [Como Fazer Perguntas Inteligentes](http://wiki.python.org.br/ComoFazerPerguntasInteligentes)

* [Python - Por onde começar?](http://aprenda-python.blogspot.com.br/p/por-onde-comecar.html)

	> [por Vinicius Assef](https://twitter.com/viniciusban)

* [Pro Git](https://git-scm.com/book/pt-br/v2) [(CC)](https://creativecommons.org/) 

	> The entire Pro Git book, written by Scott Chacon and Ben Straub and published by Apress

* [Open Source Guides](https://opensource.guide/) 

	> Open source software is made by people just like you. Learn how to launch and grow your project. 

---

## Table of Contents

  * [Articles](#articles)

  * [Awesome Lists](#awesome-lists)

  * [Books](#books)

  * [Courses](#courses)

  * [Podcasts](#podcasts)

  * [Youtube channels](#youtubechannels)

  * [Videos](#videos)

---

## Articles

| Number | Name |  Author | 

| :---: | :--- | :---: | 

## Awesome Lists

- Python

	- [by @kirang89](https://github.com/kirang89/pycrumbs)

	- [by @svaksha](https://github.com/svaksha/pythonidae)

	- [by @vinta](https://github.com/vinta/awesome-python)

	- [Asyncio](https://github.com/timofurrer/awesome-asyncio) - Asynchronous I/O in Python 3.

- Big Data

	- [by @onurakpolat](https://github.com/onurakpolat/awesome-bigdata)

	- [by @zenkay](https://github.com/zenkay/bigdata-ecosystem)

	- [Hadoop](https://github.com/youngwookim/awesome-hadoop)

- [Public Datasets](https://github.com/caesar0301/awesome-public-datasets)

- Deep Learning

	- [by @ChristosChristofidis](https://github.com/ChristosChristofidis/awesome-deep-learning)

	- [by @endymecy](https://github.com/endymecy/awesome-deeplearning-resources)

- [Data Engineering](https://github.com/igorbarinov/awesome-data-engineering)

- [Streaming](https://github.com/manuzhang/awesome-streaming)

## Books

| Number | Name |  Author | 

| :---: | :--- | :---: | 

| 01 | [Use a Cabeça! Programação](http://www.altabooks.com.br/use-a-cabeca-programacao.html)| `Paul Barry` |

| 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` |

| 03 | [Automate the Boring Stuff with Python](https://automatetheboringstuff.com/) [CC](https://creativecommons.org/)| `Al Sweigart` |

| 04 | [Practical Data Science in Python](http://radimrehurek.com/data_science_python/)| `Radim Řehůřek` |

| 05 | [Learn Data Science](http://learnds.com/)| `Nitin Borwankar`|

## Courses

| Number | Name | Platform  | Author | 

| :---: | :--- | :---: | :---: |

| 01 | [Machine Learning - Stanford University](https://www.coursera.org/learn/machine-learning)|[Coursera](https://www.coursera.org/)| `Andrew Ng` |

| 02 | [Data Science Math Skills](https://www.coursera.org/learn/datasciencemathskills)|[Coursera](https://www.coursera.org/)| `Daniel Egger, Paul Bendich` |

| 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` |

| 04 | [Machine Learning A-Z™: Hands-On Python & R In Data Science](https://www.udemy.com/machinelearning)|[Udemy](https://www.udemy.com/)| `Kirill Eremenko` |

| 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` |

| 06 | [Introduction to Data Science in Python](https://www.coursera.org/learn/python-data-analysis)|[Coursera](https://www.coursera.org/)| `Christopher Brooks` |

| 07 | [Applied Machine Learning in Python](https://www.coursera.org/learn/python-machine-learning)|[Coursera](https://www.coursera.org/)| `Kevyn Collins-Thompson` |

| 08 | [Applied Plotting, Charting & Data Representation in Python](https://www.coursera.org/learn/python-plotting)|[Coursera](https://www.coursera.org/)| `Christopher Brooks` |

| 09 | [Applied Text Mining in Python](https://www.coursera.org/learn/python-text-mining)|[Coursera](https://www.coursera.org/)| `V. G. Vinod Vydiswaran` |

| 10 | [Applied Social Network Analysis in Python](https://www.coursera.org/learn/python-social-network-analysis)|[Coursera](https://www.coursera.org/)| `Daniel Romero` |

| 11 | [Machine Learning Foundations: A Case Study Approach](https://www.coursera.org/learn/ml-foundations)|[Coursera](https://www.coursera.org/)| `Carlos Guestrin, Emily Fox` |

| 12 | [Machine Learning: Regression](https://www.coursera.org/learn/ml-foundations)|[Coursera](https://www.coursera.org/)| `Carlos Guestrin, Emily Fox` |

| 13 | [Machine Learning: Classification](https://www.coursera.org/learn/ml-classification)|[Coursera](https://www.coursera.org/)| `Carlos Guestrin, Emily Fox` |

| 14 | [Machine Learning: Clustering & Retrieval](https://www.coursera.org/learn/ml-clustering-and-retrieval)|[Coursera](https://www.coursera.org/)| `Carlos Guestrin, Emily Fox` |

| 15 | [Neural Networks and Deep Learning](https://www.coursera.org/learn/neural-networks-deep-learning)|[Coursera](https://www.coursera.org/)| `Andrew Ng` |

| 16 | [Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization]()(Próxima sessão: Aug 15 — Sep 11.)|[Coursera](https://www.coursera.org/)| `Andrew Ng` |

| 17 |[Structuring Machine Learning Projects]()(Próxima sessão: Aug 15 — Sep 4.)|[Coursera](https://www.coursera.org/)| `Andrew Ng` |

| 18 | [Convolutional Neural Networks]()(Em Breve)|[Coursera](https://www.coursera.org/)| `Andrew Ng` |

| 19 | [Sequence Models]()(Em Breve)|[Coursera](https://www.coursera.org/)| `Andrew Ng` |

## Podcasts

| Number | Name | Platform  | Author | 

| :---: | :--- | :---: | :---: |

|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` |

|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` |

|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` |

|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` |

|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` |

|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` |

|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` |

|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` |

|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` |

|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` |

Youtube channels


| Number | Name | Author | 

| :---: | :--- | :---: |

|01|[Peixe Babel](https://www.youtube.com/user/CanalPeixeBabel)| `Camila Laranjeira` |

|02|[Deep Learning TV](https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ)| `Jagannath Rajagopal` |

|03|[Nat and Friends](https://www.youtube.com/NatAndFriends)| `Natalie Hammel` |

|04|[Sirajology](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A)| `Siraj Raval` |

## Videos

| Number | Name | Author | 

| :---: | :--- | :---: |

|01|[Getting Started with Machine Learning and Python](https://youtu.be/rCsbaHhvxfI)| `Bruno Godoi Eilliar` |

## License

[![CC0](http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0/)