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Disciplina de Ciências de Dados da UNINOVE

License: Creative Commons Attribution Share Alike 4.0 International

Jupyter Notebook 99.38% Dockerfile 0.08% Shell 0.54%
aprendizagem-de-maquina aprendizagem-profunda ciencia-de-dados data-science deeplearning machinelearning matplotlib pandas python pytorch scikit-learn tensorflow

ciencia-de-dados's Introduction

I am a software developer with a PhD and background in Computational Statistics. I am interested in performant and reliable software. In other words, I make computers go brrr faster.

In hope of a better future, I dedicate my free time to Bitcoin-related projects. I contribute to the Rust Bitcoin ecosystem, particularly Bitcoin Dev Kit (BDK) and rust-bitcoin. I also work full-time at Portal on Bitcoin-backed smart contracts.

I am a faculty member at the Department of Computer Science at Universidade Nove de Julho (UNINOVE). I am also an ardent Bayesian.

I like Rust, Julia, Nix, and Vim.

I practice Stoic Philosophy daily.

I hate bloatware and the soydev phenomenon.

Everything that I do is either open source or has a permissive Creative Commons license.

I don't have social media, since I think they are overrated and "they sell your data". If you want to contact me, please send an email.

If you want to find out what's recently on my mind, check out my blog.

ciencia-de-dados's People

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ciencia-de-dados's Issues

Expandir Conteúdo para Módulo Ciência de Dados com Python (2023)

Total 12 aulas.
1 aula introdutória e 1 aula de conclusão/encerramento.

Numpy (3 aulas)

  • Aula 6a - Por quê Algebra Linear é importante
  • Aula 6b - NumPy NDArrays, criação, manipulação e transformações
  • Aula 6c - NumPy Números Aleatórios e Estatística

Pandas (4 aulas)

  • Alinhar datasets com @wonderalexandre (titanic e pelo menos mais 1 com série temporal)
  • Aula 7a - Pandas I/O, Dataframe e Series
  • Aula 7b - filtragem e manipulações básicas
  • Aula 7c - groupby, sumarizações, pivot table e crosstabs
  • Aula 7d - Séries Temporais e funções de date e time series

Matplotlib (3 aulas)

  • Usar os mesmos datasets de Pandas
  • Aula 8a - Plots disponíveis e guidelines gerais das funções plt.*
  • Aula 8b - Subplots
  • Aula 8c - Customizações

[Python 3.10] match statement

Para o Futuro.

Aula 2 - Desvio Condicional deverá ser aprimorada com o match statement que sairá em Python 3.10 (próxima versão).

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