{"id":2490,"date":"2017-09-29T11:13:38","date_gmt":"2017-09-29T11:13:38","guid":{"rendered":"https:\/\/www.kolabtree.com\/blog\/?p=2490"},"modified":"2017-09-29T12:22:27","modified_gmt":"2017-09-29T12:22:27","slug":"must-read-data-science-articles-september-2017","status":"publish","type":"post","link":"https:\/\/www.kolabtree.com\/blog\/pt\/must-read-data-science-articles-september-2017\/","title":{"rendered":"10 Artigos de leitura obrigat\u00f3ria de setembro de 2017"},"content":{"rendered":"

A ind\u00fastria da ci\u00eancia dos dados est\u00e1 rapidamente encontrando aplica\u00e7\u00f5es em uma variedade de disciplinas que v\u00e3o desde biotecnologia<\/a> para ci\u00eancia social<\/a>. Revolucionou a forma como tomamos decis\u00f5es tanto como empresas quanto como pesquisadores. A ci\u00eancia dos dados \u00e9 atualmente o \"melhor trabalho na Am\u00e9rica\", de acordo com Glassdoor. A quantidade de dados que coletamos \u00e9 alucinante, n\u00e3o apenas atrav\u00e9s dos meios tradicionais, mas tamb\u00e9m atrav\u00e9s de plataformas digitais e m\u00eddias sociais. An\u00e1lise, visualiza\u00e7\u00e3o e interpreta\u00e7\u00e3o de dados s\u00e3o habilidades que est\u00e3o em alta demanda - e os especialistas freq\u00fcentemente se especializam em disciplinas espec\u00edficas, como inform\u00e1tica ou mesmo astrof\u00edsica. Na Kolabtree, ajudamos os propriet\u00e1rios de projetos a encontrar freelancers altamente qualificados especialistas em ci\u00eancia dos dados<\/a> para ajud\u00e1-los a analisar um conjunto de dados ou escrever um algoritmo - e isto \u00e9 a prova de que as organiza\u00e7\u00f5es sofrem de uma lacuna de habilidades ou recursos neste campo.<\/p>\n

Data scientists need to keep themselves updated on the latest pesquisa<\/a> and developments in the field to stay on top of their game. Here is a curated list of the top 10 articles from September 2017 that we think is necessary reading.<\/p>\n

1.\u00a0Aqui est\u00e3o as 10 habilidades que voc\u00ea precisa para se tornar um cientista de dados, o n\u00e3o. 1 emprego na Am\u00e9rica<\/a>
\n(Alison DeNisco, TechRepublic)<\/p>\n

2.\u00a0O Abismo da Anal\u00edtica\u00a0<\/a>
\n(Jon Evans, TechCrunch)<\/p>\n

3.\u00a0Novas ferramentas utilizam o aprendizado de m\u00e1quinas e intelig\u00eancia artificial para melhorar as opera\u00e7\u00f5es de TI<\/a>
\n(Bernard Marr, Forbes)<\/p>\n

4.\u00a0Diferen\u00e7a entre Aprendizagem de M\u00e1quina, Ci\u00eancia de Dados, IA, Aprendizagem Profunda, e Estat\u00edstica<\/a>
\n(Vincent Granville, Data Science Central)<\/p>\n

5.\u00a0Uma Wikip\u00e9dia para visualiza\u00e7\u00e3o de dados est\u00e1 aqui<\/a>
\n(Katharine Schwab, Co.Design)<\/p>\n

6.\u00a0Se voc\u00ea deseja atualizar suas habilidades de an\u00e1lise de dados, que linguagem de programa\u00e7\u00e3o voc\u00ea deve aprender?<\/a>\u00a0[R vs Python]\n(Dan Kopf, Quartz)<\/p>\n

7.\u00a030 Dados Essenciais, Aprendizagem de M\u00e1quina e Folhas de Aldrabice de Aprendizagem Profunda<\/a>
\n(Matthew Mayo, KDNuggets)<\/p>\n

8.\u00a0O Estudo A.I. \"Gaydar\" e os Perigos Reais dos Grandes Dados<\/a>
\n(Alan Burdick, The New Yorker)<\/p>\n

9.\u00a0Garantindo a vantagem competitiva com a aprendizagem de m\u00e1quinas<\/a>
\n(Ronald van Loon, Dataconomy)<\/p>\n

10.\u00a0Ca\u00e7adores da tempestade: A ci\u00eancia dos dados por tr\u00e1s da previs\u00e3o do tempo<\/a>
\n(George Anadiotis, ZDNet)<\/p>\n

Tem mais algum artigo a acrescentar \u00e0 lista? Deixe-os nos coment\u00e1rios abaixo ou Tweet para n\u00f3s<\/a>!<\/p>","protected":false},"excerpt":{"rendered":"

The data science industry is quickly finding applications in a variety of disciplines ranging from biotechnology to social science. It’s revolutionized the way we make decisions both as businesses and researchers. Data science is currently the ‘best job in America’, according to Glassdoor. The amount of data that we collect is mind-boggling, not only through<\/p>\n

Leia mais<\/a><\/div>","protected":false},"author":12,"featured_media":2494,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"acf":[],"yoast_head":"\n10 Must-Read Data Science Articles from September 2017 - The Kolabtree Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.kolabtree.com\/blog\/pt\/must-read-data-science-articles-september-2017\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"10 Must-Read Data Science Articles from September 2017\" \/>\n<meta property=\"og:description\" content=\"The data science industry is quickly finding applications in a variety of disciplines ranging from biotechnology to social science. 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