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mais sobre o Google Collab, a ferramenta ideal para voc\u00ea explorar seus conhecimentos em an\u00e1lise de dados, aprendizado de m\u00e1quina e intelig\u00eancia artificial.","_yoast_wpseo_title":"Google Collab: O que \u00e9 e o que tem a ver com Ci\u00eancia de Dados? | Le Wagon","contenu_de_larticle":"

O Google Collab \u00e9 uma das melhores ferramentas para quem deseja codar em Python sem complica\u00e7\u00f5es e realizar a an\u00e1lise de dados de maneira mais assertiva. 

<\/div>
Esse software \u00e9 disponibilizado de maneira gratuita pela gigante de tecnologia<\/strong>, o Google, que tem o objetivo de incentivar o aprendizado de m\u00e1quina ou machine learn, <\/em>como tamb\u00e9m \u00e9 conhecido no Brasil.  

<\/div>
Se voc\u00ea quer saber mais sobre o Google Collab e como ele pode te ajudar a explorar o campo de data science<\/em> <\/a>esse artigo foi feito para voc\u00ea.  Continue a leitura e saiba os principais detalhes desta ferramenta. 

<\/div>

O que \u00e9 o Google Collab? <\/h2>

<\/div>
O software desenvolvimento pelo Google fica hospedado em nuvem e permite que os programadores trabalhem com a linguagem Python<\/a> sem ter que instalar nada na sua m\u00e1quina, tudo \u00e9 feito de maneira online<\/strong>. 

<\/div>
A grande vantagem do notebook virtual \u00e9 que os programadores n\u00e3o precisam ter uma m\u00e1quina com uma mem\u00f3ria super potente para fazer an\u00e1lise de dados ou conhecer mais sobre o aprendizado de m\u00e1quina. 

<\/div>
Al\u00e9m disso, voc\u00ea tem mais liberdade para acessar o Google Collab em qualquer lugar do mundo que tenha acesso a internet.

<\/div>
O Google Collaboraty, como tamb\u00e9m \u00e9 chamado, possui uma interface parecida com seu concorrente o Jupyter Notebook. A principal diferen\u00e7a entre essas duas ferramentas \u00e9 que o Google Collab trabalha em ambiente cloud. 
<\/strong>
<\/div>
Dentro desta plataforma voc\u00ea pode criar diversos notebooks, ou seja, voc\u00ea tem a possibilidade de criar estruturas com diferentes objetivos. Seja para explorar o data science<\/em> ou intelig\u00eancia artificial, voc\u00ea pode criar um notebook para cada objetivo. 

<\/div>
Al\u00e9m de criar os notebooks, voc\u00ea tamb\u00e9m pode importar um diretamente do Github<\/a>, um software online onde os programadores podem hospedar seus c\u00f3digos, fazer o seu versionamento e ainda permitir a colabora\u00e7\u00e3o de times trabalhando num mesmo projeto simultaneamente. 

<\/div>
Um outro ponto interessante \u00e9 que, assim como outras ferramentas gratuitas da gigante de buscas, voc\u00ea pode compartilhar seus notebooks com v\u00e1rias pessoas para trabalharem em conjunto.<\/strong> 

<\/div>

Como usar o Google Collab?<\/h2>

<\/div>
Para quem est\u00e1 come\u00e7ando a aprender<\/a> mais sobre a ci\u00eancia de dados e deseja explorar o Google Collab, constru\u00edmos um passo a passo para come\u00e7ar a trabalhar nesta ferramenta:

<\/div>
  1. acesse o site da ferramenta<\/a>; <\/li>
  2. Caso voc\u00ea n\u00e3o esteja logado em uma conta de email do Google, ser\u00e1 necess\u00e1rio efetuar o login ou criar uma conta; <\/li>
  3. Quando aparecer o menu de notebooks voc\u00ea pode observar os exemplos, ver os notebooks recentes, acessar um notebook que esteja salvo no seu drive, fazer a importa\u00e7\u00e3o direta do Github ou fazer o upload do seu computador. <\/li>
  4. Caso voc\u00ea n\u00e3o queria acessar nenhuma dessas op\u00e7\u00f5es voc\u00ea pode criar um novo notebook clicando em \u201cNovo Notebook\u201d na parte inferior da tela suspensa. <\/li>
  5. Depois \u00e9 s\u00f3 abrir o c\u00f3digo e fazer as modifica\u00e7\u00f5es necess\u00e1rias conforme seu objetivo. 

    <\/li><\/ol>
    No ambiente online da ferramenta voc\u00ea pode salvar o notebook no Google Drive, espa\u00e7o de armazenamento em nuvem no Google. 

    <\/div>
    Existem 2 formas de utilizar um conjunto de c\u00f3digos que voc\u00ea j\u00e1 tenha salvo em um pendrive ou no seu HD.

    <\/div>
    A primeira op\u00e7\u00e3o \u00e9 tempor\u00e1ria, nela voc\u00ea deve clicar no \u00edcone de pasta na canto lateral esquerdo da interface e arrastar o documento para o campo que aparecer, ou clicando em \u201cupload\u201d no topo dessa barra lateral. 

    <\/div>
    \u00c9 importante lembrar que fazendo esse m\u00e9todo seu arquivo ser\u00e1 apagado assim que voc\u00ea encerrar a sess\u00e3o no Google Collab.

    <\/div>
    A segunda op\u00e7\u00e3o \u00e9 salvar seu documento no Google Drive e fazer a importa\u00e7\u00e3o desse documento no notebook criado. <\/strong>Dessa forma, mesmo que a sess\u00e3o seja encerrada voc\u00ea n\u00e3o perder\u00e1 nenhuma informa\u00e7\u00e3o.  

    <\/div>

    O que o Google Collab tem a ver com ci\u00eancia de dados? <\/h2>

    <\/div>
    Talvez essa seja uma d\u00favida de muitas pessoas, afinal, o que essa ferramenta tem de t\u00e3o especial para a realiza\u00e7\u00e3o das an\u00e1lises de dados? 

    <\/div>
    Ele permite que os cientistas de dados criem e visualizem equa\u00e7\u00f5es, imagens, textos e c\u00f3digos sendo executado no pr\u00f3prio documento.  
    <\/strong>
    <\/div>
    Essas opera\u00e7\u00f5es consomem bastante mem\u00f3ria de processamento do GPU e por isso, caso o profissional opte por utilizar uma m\u00e1quina offline, ter\u00e1 que equipar bem o seu computador para que ele tenha uma boa performance. Como o Collaboraty \u00e9 online e gratuito o profissional n\u00e3o ter\u00e1 esse problema. 
    <\/strong>
    <\/div>
    Entenda quais s\u00e3o os benef\u00edcios do Google Collab para os cientistas de dados.

    <\/div>

    \u00d3tima capacidade de processamento<\/h3>
    O Google Collab permite que o programador efetue o aceleramento de GPU. Ele ser\u00e1 o respons\u00e1vel pelo processamento das informa\u00e7\u00f5es que voc\u00ea adicionar em seu notebook. 
    <\/strong>
    <\/div>
    Ou seja, voc\u00ea poder\u00e1 aumentar sua produtividade realizando as opera\u00e7\u00f5es na m\u00e1quina em fra\u00e7\u00e3o de segundos. 

    <\/div>

    Grande espa\u00e7o de mem\u00f3ria RAM<\/h3>
    Para quem usa o computador offline, o ideal \u00e9 que o desktop tenha uma mem\u00f3ria RAM de aproximadamente 16GB para que o computador rode os programas de maneira confort\u00e1vel. 

    <\/div>
    O PC com essa configura\u00e7\u00e3o podem custar o dobro das m\u00e1quinas populares que possuem uma mem\u00f3ria RAM entre 4GB a 8GB. O Google Collab disponibiliza at\u00e9 12GB de mem\u00f3ria RAM totalmente gratuita<\/strong>. 

    <\/div>
    Assim, voc\u00ea pode aproveitar todos os recursos da ferramenta online e economizar um valor ao dispensar a compra de um computador t\u00e3o robusto. 

    <\/div>

    Bibliotecas pr\u00e9-instaladas<\/h3>
    A linguagem de programa\u00e7\u00e3o Python disponibiliza muitas bibliotecas que podem ser utilizadas em diferentes tipos de aplica\u00e7\u00f5es. 

    <\/div>
    O Google Collab possui as principais bibliotecas dessa linguagem como Numpy, Pandas e Matplotlib instaladas em seu ambiente.<\/strong> Voc\u00ea ainda tem a possibilidade de instalar novas bibliotecas.

    <\/div>

    Sem configura\u00e7\u00f5es<\/h3>
    O Collaboraty, al\u00e9m de rodar em cloud e n\u00e3o precisar instalar nada na m\u00e1quina, ainda \u00e9 pr\u00e9 configurado<\/strong>. Basta acessar e come\u00e7ar a usar no mesmo instante. 

    <\/div>
    J\u00e1 o seu concorrente, o Jupyter Notebook, requer o download da aplica\u00e7\u00e3o, instala\u00e7\u00e3o na m\u00e1quina e configura\u00e7\u00e3o do software de forma cir\u00fargica para que ele funcione da maneira esperada.  

    <\/div>
    Se voc\u00ea quer aprender mais sobre aprendizado de m\u00e1quina, intelig\u00eancia artificial e an\u00e1lise de dados, com certeza o Google Collab ser\u00e1 uma boa op\u00e7\u00e3o para come\u00e7ar os estudos.
    <\/strong>
    <\/div>
    Aproveite para expandir seus conhecimentos na \u00e1rea de tecnologia e se inscreva no Bootcamp de programa\u00e7\u00e3o<\/a> da Le Wagon<\/a>. Aprenda linguagens de programa\u00e7\u00e3o como Python e Ruby, frameworks mais utilizados no mercado e muito mais.

    <\/div>

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