{"id":36808,"date":"2019-10-31T22:13:56","date_gmt":"2019-10-31T19:13:56","guid":{"rendered":"https:\/\/prohoster.info\/blog\/kurs-start-v-data-science-pervyj-shag-v-rabote-s-dannymi\/"},"modified":"2019-10-31T22:13:56","modified_gmt":"2019-10-31T19:13:56","slug":"kurs-start-v-data-science-pervyj-shag-v-rabote-s-dannymi","status":"publish","type":"post","link":"https:\/\/prohoster.info\/de\/blog\/news\/kurs-start-v-data-science-pervyj-shag-v-rabote-s-dannymi","title":{"rendered":"Der Kurs \u201eStart in Data Science\u201c: der erste Schritt zur Arbeit mit Daten","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Wir starten einen neuen Kurs f\u00fcr Anf\u00e4nger \u2013 \u201e<noindex><a rel=\"nofollow\" href=\"https:\/\/netology.ru\/programs\/start-ds?utm_source=blog&amp;utm_medium=1492&amp;utm_campaign=bds_obds_ou_habr_19072019startds\">Start in Data Science<\/a><\/noindex>\u201c. F\u00fcr nur 990 Rubel tauchen Sie in die Welt von Data Science ein: Sie erfahren mehr \u00fcber Spezialisierungen, w\u00e4hlen eine Profession und verbessern Ihre F\u00e4higkeiten in der Datenverarbeitung.\u00a0<\/p>\n<p>Data Science ist die Wissenschaft von Daten und deren Analyse. Viele glauben, dass der Einstieg in diesen Bereich sehr schwierig ist: langwierig, und man ben\u00f6tigt eine Ausbildung in Physik und Mathematik. Aber das ist nicht wahr \u2013 jeder kann einen Job finden, der ihm gef\u00e4llt, oder bestimmte F\u00e4higkeiten in der Datenbearbeitung verbessern.<\/p>\n<p>Der Kurs \u201e<noindex><a rel=\"nofollow\" href=\"https:\/\/netology.ru\/programs\/start-ds?utm_source=blog&amp;utm_medium=1492&amp;utm_campaign=bds_obds_ou_habr_19072019startds\">Start in Data Science<\/a><\/noindex>\u201c richtet sich an alle, die einen gefragten und gut bezahlten Beruf erlernen m\u00f6chten, aber nicht wissen, welche Richtung sie w\u00e4hlen sollen, wo sie lernen k\u00f6nnen und wie sie im Bereich vorankommen. Au\u00dferdem ist er f\u00fcr alle geeignet, die in ihrem Beruf mit Big Data, maschinellem Lernen, Datenanalyse in Ber\u00fchrung kommen oder einfach nur an diesem Thema interessiert sind.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h2>Sie werden erfahren<\/h2>\n<p><\/p>\n<ul>\n<li>was Data Science ist;<\/li>\n<li>welche Fachrichtungen und Berufe es gibt;<\/li>\n<li>Tipps zur Formatierung von Zellen und Berechnungen ohne Formeln;<\/li>\n<li>wie man wichtige Informationen verbirgt;<\/li>\n<li>Methoden zum \u00dcbertragen von Daten aus einer Tabelle in eine andere;<\/li>\n<li>welche Python-Bibliotheken es zur Datenanalyse gibt;<\/li>\n<li>wo und wof\u00fcr SQL verwendet wird, welche Probleme damit gel\u00f6st werden k\u00f6nnen und welche analytischen Funktionen es hat.<\/li>\n<\/ul>\n<p><\/p>\n<h2>Sie werden lernen<\/h2>\n<p><\/p>\n<ul>\n<li>sch\u00f6ne interaktive Berichte in Power BI ohne Programmierung zu erstellen;<\/li>\n<li>data-driven Denken in der Arbeit anzuwenden;<\/li>\n<li>Big Data zu verwalten: sammeln, speichern, Qualit\u00e4t bewerten, sch\u00fctzen, skalieren und umwandeln;<\/li>\n<li>einfachen Code zu schreiben.\u00a0<\/li>\n<\/ul>\n<p><\/p>\n<h2>Dozenten<\/h2>\n<p>\n<b>Elena Gerasimova<\/b>, Programmleiterin f\u00fcr Data Science bei Netology.<\/p>\n<p><b>Daria Mukhina<\/b>, Produktanalystin bei Skyeng.\u00a0<\/p>\n<p><b>Alexey Tschernobrowov<\/b>, Berater f\u00fcr Entwicklung bei Pult.ru, Mazda, Skyeng.<\/p>\n<p><b>Pavel Kozlov<\/b>, Trainer des CIE-Projekts bei Microsoft.<\/p>\n<p><b>Dmitry Jakushev<\/b>, Trainer und Entwickler von Schulungskursen bei der Excel Akademie.<\/p>\n<p><b>Alexey Kuzmin<\/b>, Entwicklungsleiter bei DomKlik.\u00a0<\/p>\n<h2>Preis<\/h2>\n<p>\n<b>Preis des Kurses:<\/b> nur 990 Rubel. Wenn Sie Ihre Ausbildung bei Netology fortsetzen, wird der Betrag als Rabatt beim Kauf anderer Data Science-Kurse angerechnet.<br \/>\n<br \/>Quelle: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/netologyru\/blog\/460701\/\">habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041c\u044b \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0435\u043c \u043d\u043e\u0432\u044b\u0439 \u043a\u0443\u0440\u0441 \u0434\u043b\u044f \u043d\u043e\u0432\u0438\u0447\u043a\u043e\u0432 \u2014 \u00ab\u0421\u0442\u0430\u0440\u0442 \u0432 Data Science\u00bb. \u0412\u0441\u0435\u0433\u043e \u0437\u0430 990 \u0440\u0443\u0431\u043b\u0435\u0439 \u0432\u044b \u043f\u043e\u0433\u0440\u0443\u0437\u0438\u0442\u0435\u0441\u044c \u0432 Data Science: \u0443\u0437\u043d\u0430\u0435\u0442\u0435 \u043e \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f\u0445, \u0432\u044b\u0431\u0435\u0440\u0438\u0442\u0435 \u043f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u044e \u0438 \u043f\u0440\u043e\u043a\u0430\u0447\u0430\u0435\u0442\u0435 \u043d\u0430\u0432\u044b\u043a\u0438 \u0432 \u0440\u0430\u0431\u043e\u0442\u0435 \u0441 \u0434\u0430\u043d\u043d\u044b\u043c\u0438.\u00a0 Data Science \u2014 \u043d\u0430\u0443\u043a\u0430 \u043e \u0434\u0430\u043d\u043d\u044b\u0445 \u0438 \u0438\u0445 \u0430\u043d\u0430\u043b\u0438\u0437\u0435. \u041c\u043d\u043e\u0433\u0438\u0435 \u0441\u0447\u0438\u0442\u0430\u044e\u0442, \u0447\u0442\u043e \u0432\u043e\u0439\u0442\u0438 \u0432 \u0441\u0444\u0435\u0440\u0443 \u043e\u0447\u0435\u043d\u044c \u0442\u044f\u0436\u0435\u043b\u043e: \u043d\u0443\u0434\u043d\u043e, \u0434\u043e\u043b\u0433\u043e \u0438 \u043d\u0443\u0436\u043d\u043e \u0444\u0438\u0437\u043c\u0430\u0442-\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435. \u041d\u043e [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-36808","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.1.1 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u041c\u044b \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0435\u043c \u043d\u043e\u0432\u044b\u0439 \u043a\u0443\u0440\u0441 \u0434\u043b\u044f \u043d\u043e\u0432\u0438\u0447\u043a\u043e\u0432 \u2014 \u00ab\u0421\u0442\u0430\u0440\u0442 \u0432 Data.\" \/>\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta name=\"author\" content=\"Yuri Gagarin\"\/>\n\t<link rel=\"canonical\" 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