{"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\/es\/articulos-cientificos-de-datos-de-lectura-imprescindible-septiembre-2017\/","title":{"rendered":"10 art\u00edculos de ciencia de datos imprescindibles de septiembre de 2017"},"content":{"rendered":"

La industria de la ciencia de los datos est\u00e1 encontrando r\u00e1pidamente aplicaciones en una variedad de disciplinas que van desde biotecnolog\u00eda<\/a> a ciencias sociales<\/a>. Ha revolucionado la forma en que tomamos decisiones, tanto como empresas como investigadores. La ciencia de los datos es actualmente el \"mejor trabajo de Estados Unidos\", seg\u00fan Glassdoor. La cantidad de datos que recopilamos es alucinante, no s\u00f3lo por las v\u00edas tradicionales, sino tambi\u00e9n por las plataformas digitales y las redes sociales. El an\u00e1lisis, la visualizaci\u00f3n y la interpretaci\u00f3n de los datos son habilidades muy demandadas, y los expertos suelen especializarse en disciplinas espec\u00edficas, como la inform\u00e1tica o incluso la astrof\u00edsica. En Kolabtree, hemos ayudado a los propietarios de proyectos a encontrar freelance altamente cualificados expertos en ciencia de datos<\/a> para ayudarles a analizar un conjunto de datos o escribir un algoritmo, y esto es una prueba de que las organizaciones adolecen de un d\u00e9ficit de competencias o recursos en este campo.<\/p>\n

Data scientists need to keep themselves updated on the latest investigaci\u00f3n<\/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.\u00a0Estas son las 10 habilidades que necesitas para convertirte en un cient\u00edfico de datos, el trabajo n\u00fam. 1 en Estados Unidos<\/a>
\n(Alison DeNisco, TechRepublic)<\/p>\n

2.\u00a0El abismo de la anal\u00edtica\u00a0<\/a>
\n(Jon Evans, TechCrunch)<\/p>\n

3.\u00a0Una nueva herramienta utiliza el aprendizaje autom\u00e1tico y la inteligencia artificial para mejorar las operaciones de TI<\/a>
\n(Bernard Marr, Forbes)<\/p>\n

4.\u00a0Diferencia entre aprendizaje autom\u00e1tico, ciencia de datos, IA, aprendizaje profundo y estad\u00edstica<\/a>
\n(Vincent Granville, Data Science Central)<\/p>\n

5.\u00a0Llega una Wikipedia para las visualizaciones de datos<\/a>
\n(Katharine Schwab, Co.Design)<\/p>\n

6.\u00a0Si quiere mejorar sus conocimientos de an\u00e1lisis de datos, \u00bfqu\u00e9 lenguaje de programaci\u00f3n deber\u00eda aprender?<\/a>\u00a0R vs Python]\n(Dan Kopf, Quartz)<\/p>\n

7.\u00a030 hojas de trucos esenciales de ciencia de datos, aprendizaje autom\u00e1tico y aprendizaje profundo<\/a>
\n(Matthew Mayo, KDNuggets)<\/p>\n

8.\u00a0El estudio de la inteligencia artificial \"Gaydar\" y los verdaderos peligros del Big Data<\/a>
\n(Alan Burdick, The New Yorker)<\/p>\n

9.\u00a0Asegurar la ventaja competitiva con el aprendizaje autom\u00e1tico<\/a>
\n(Ronald van Loon, Dataconomy)<\/p>\n

10.\u00a0Los asaltantes de la tormenta: La ciencia de los datos detr\u00e1s de la predicci\u00f3n meteorol\u00f3gica<\/a>
\n(George Anadiotis, ZDNet)<\/p>\n

\u00bfTiene m\u00e1s art\u00edculos que a\u00f1adir a la lista? D\u00e9jelos en los comentarios de abajo o Tuitea con nosotros<\/a>!<\/p>","protected":false},"excerpt":{"rendered":"

La industria de la ciencia de los datos est\u00e1 encontrando r\u00e1pidamente aplicaciones en una variedad de disciplinas que van desde la biotecnolog\u00eda hasta las ciencias sociales. Ha revolucionado la forma en que tomamos decisiones tanto como empresas como investigadores. La ciencia de los datos es actualmente el \"mejor trabajo de Estados Unidos\", seg\u00fan Glassdoor. La cantidad de datos que recogemos es alucinante, no s\u00f3lo por<\/p>\n

Leer m\u00e1s<\/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\/es\/must-read-data-science-articles-september-2017\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\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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