{"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\/it\/must-read-data-science-articles-september-2017\/","title":{"rendered":"10 articoli da leggere sulla scienza dei dati di settembre 2017"},"content":{"rendered":"

Il settore della scienza dei dati sta trovando rapidamente applicazioni in una variet\u00e0 di discipline che vanno da biotecnologia<\/a> a scienza sociale<\/a>. Ha rivoluzionato il modo in cui prendiamo decisioni sia come aziende che come ricercatori. La scienza dei dati \u00e8 attualmente il \"miglior lavoro in America\", secondo Glassdoor. La quantit\u00e0 di dati che raccogliamo \u00e8 sbalorditiva, non solo attraverso modi tradizionali ma anche attraverso piattaforme digitali e social media. L'analisi, la visualizzazione e l'interpretazione dei dati sono competenze che sono molto richieste - e gli esperti spesso si specializzano in discipline specifiche come l'informatica o anche l'astrofisica. A Kolabtree, abbiamo aiutato i proprietari di progetti a trovare freelance altamente qualificati esperti di scienza dei dati<\/a> per aiutarli ad analizzare un set di dati o scrivere un algoritmo - e questa \u00e8 la prova che le organizzazioni soffrono di un deficit di competenze o di risorse in questo campo.<\/p>\n

Gli scienziati dei dati devono tenersi aggiornati sulle ultime novit\u00e0 ricerca<\/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.\u00a0Ecco le 10 competenze necessarie per diventare uno scienziato dei dati, il no. 1 lavoro in America<\/a>
\n(Alison DeNisco, TechRepublic)<\/p>\n

2.\u00a0L'abisso dell'analisi\u00a0<\/a>
\n(Jon Evans, TechCrunch)<\/p>\n

3.\u00a0Un nuovo strumento utilizza l'apprendimento automatico e l'intelligenza artificiale per migliorare le operazioni IT<\/a>
\n(Bernard Marr, Forbes)<\/p>\n

4.\u00a0Differenza tra apprendimento automatico, scienza dei dati, AI, apprendimento profondo e statistica<\/a>
\n(Vincent Granville, Data Science Central)<\/p>\n

5.\u00a0\u00c8 arrivata una Wikipedia per le visualizzazioni dei dati<\/a>
\n(Katharine Schwab, Co.Design)<\/p>\n

6.\u00a0Se vuoi migliorare le tue capacit\u00e0 di analisi dei dati, quale linguaggio di programmazione dovresti imparare?<\/a>\u00a0[R vs Python]\n(Dan Kopf, Quartz)<\/p>\n

7.\u00a030 schede essenziali di scienza dei dati, apprendimento automatico e apprendimento profondo<\/a>
\n(Matthew Mayo, KDNuggets)<\/p>\n

8.\u00a0Lo studio A.I. \"Gaydar\" e i veri pericoli dei Big Data<\/a>
\n(Alan Burdick, The New Yorker)<\/p>\n

9.\u00a0Assicurare il vantaggio competitivo con l'apprendimento automatico<\/a>
\n(Ronald van Loon, Dataconomy)<\/p>\n

10.\u00a0I predatori della tempesta: La scienza dei dati dietro la previsione del tempo<\/a>
\n(George Anadiotis, ZDNet)<\/p>\n

Avete altri articoli da aggiungere alla lista? Lasciali nei commenti qui sotto o Twittaci<\/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

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