{"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\/de\/must-read-data-science-articles-september-2017\/","title":{"rendered":"10 unverzichtbare Artikel zur Datenwissenschaft vom September 2017"},"content":{"rendered":"

Die Data-Science-Branche findet schnell Anwendungen in einer Vielzahl von Disziplinen, die von Biotechnologie<\/a> zu Sozialwissenschaft<\/a>. Sie hat die Art und Weise revolutioniert, wie wir Entscheidungen treffen, sowohl in der Wirtschaft als auch in der Forschung. Laut Glassdoor ist Datenwissenschaft derzeit der \"beste Job in Amerika\". Die Menge an Daten, die wir sammeln, ist atemberaubend, nicht nur auf traditionelle Weise, sondern auch \u00fcber digitale Plattformen und soziale Medien. Datenanalyse, -visualisierung und -interpretation sind gefragte F\u00e4higkeiten - und Experten spezialisieren sich oft auf bestimmte Disziplinen wie Informatik oder sogar Astrophysik. Bei Kolabtree haben wir Projektinhabern geholfen, hochqualifizierte Freiberufler zu finden Data-Science-Experten<\/a> um ihnen bei der Analyse eines Datensatzes oder beim Schreiben eines Algorithmus zu helfen - und das ist der Beweis daf\u00fcr, dass Unternehmen in diesem Bereich unter einer Qualifikations- oder Ressourcenl\u00fccke leiden.<\/p>\n

Datenwissenschaftler m\u00fcssen sich \u00fcber die neuesten Entwicklungen auf dem Laufenden halten. Forschung<\/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.\u00a0Hier sind die 10 F\u00e4higkeiten, die Sie brauchen, um Datenwissenschaftler zu werden, die Nr. 1 Job in Amerika<\/a>
\n(Alison DeNisco, TechRepublic)<\/p>\n

2.\u00a0Der Abgrund der Analytik\u00a0<\/a>
\n(Jon Evans, TechCrunch)<\/p>\n

3.\u00a0Neues Tool nutzt maschinelles Lernen und k\u00fcnstliche Intelligenz zur Verbesserung des IT-Betriebs<\/a>
\n(Bernard Marr, Forbes)<\/p>\n

4.\u00a0Unterschied zwischen maschinellem Lernen, Datenwissenschaft, KI, Deep Learning und Statistik<\/a>
\n(Vincent Granville, Data Science Central)<\/p>\n

5.\u00a0Ein Wikipedia f\u00fcr Datenvisualisierungen ist da<\/a>
\n(Katharine Schwab, Co.Design)<\/p>\n

6.\u00a0Welche Programmiersprache sollten Sie lernen, wenn Sie Ihre Kenntnisse in der Datenanalyse verbessern wollen?<\/a>\u00a0[R vs. Python]\n(Dan Kopf, Quartz)<\/p>\n

7.\u00a030 wichtige Spickzettel f\u00fcr Datenwissenschaft, maschinelles Lernen und Deep Learning<\/a>
\n(Matthew Mayo, KDNuggets)<\/p>\n

8.\u00a0Die KI-Studie \"Gaydar\" und die wahren Gefahren von Big Data<\/a>
\n(Alan Burdick, The New Yorker)<\/p>\n

9.\u00a0Mit maschinellem Lernen den Wettbewerbsvorteil sichern<\/a>
\n(Ronald van Loon, Dataconomy)<\/p>\n

10.\u00a0J\u00e4ger des Sturms: Die Datenwissenschaft hinter der Wettervorhersage<\/a>
\n(George Anadiotis, ZDNet)<\/p>\n

Haben Sie weitere Artikel, die Sie der Liste hinzuf\u00fcgen m\u00f6chten? Hinterlassen Sie sie unten in den Kommentaren oder Tweet an uns<\/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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