Praktische Statistik für Data Scientists
SKU: 73952926822

Praktische Statistik für Data Scientists

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Praktische Statistik für Data ScientistsBruce, Peter , Bruce, Andrew , Gedeck, Peter ISBN: 9783960091530 50+ essenzielle Konzepte mit R und Python, Kartoniertbersetzung: Fraa, Marcus, bersetzung der 2. AuflageDeutsch 2021 dpunkt, 358 S. Neue Ware, Mngelexemplar, LPA (Ladenpreisaufgehoben), Zustand: gut Das Buch verbindet ntzliche statistische Prinzipien mit der heutigen Praxis der DatenanalyseEs ermglicht Data Scientists, ihr Wissen ber Statistik auf ein neues Level zu bringenAls bersetzung

Bruce, Peter , Bruce, Andrew , Gedeck, Peter

ISBN: 9783960091530

50+ essenzielle Konzepte mit R und Python, Kartoniert

Übersetzung: Fraaß, Marcus, Übersetzung der 2. Auflage
Deutsch 2021 - dpunkt, 358 S.

Neue Ware, Mängelexemplar, LPA (Ladenpreisaufgehoben), Zustand: gut

Das Buch verbindet nützliche statistische Prinzipien mit der heutigen Praxis der DatenanalyseEs ermöglicht Data Scientists, ihr Wissen über Statistik auf ein neues Level zu bringenAls Übersetzung der 2. Auflage des US-Bestsellers enthält es Beispiele in Python und R


Statistische Methoden sind ein zentraler Bestandteil der Arbeit mit Daten, doch nur wenige Data Scientists haben eine formale statistische Ausbildung. In Kursen und Büchern werden die Grundlagen der Statistik allerdings selten aus der Sicht von Data Scientists behandelt. Dieses praxisorientierte Handbuch erklärt Ihnen anhand zahlreicher Beispiele in Python und R, wie Sie verschiedene statistische Methoden speziell in den Datenwissenschaften anwenden.

Das Buch erläutert, welche statistischen Konzepte für die Datenwissenschaften besonders relevant sind, und zeigt Ihnen auch, wie Sie den falschen Gebrauch von statistischen Verfahren vermeiden. Wenn Sie mit R oder Python vertraut sind, ermöglicht Ihnen diese gut lesbare Referenz ein tieferes Verständnis Ihrer Daten und Sie werden Ihr Statistikwissen für die Praxis deutlich ausbauen.

In diesem Buch erfahren Sie:
warum die explorative Datenanalyse ein wichtiger vorbereitender Arbeitsschritt in der Datenwissenschaft istwie Zufallsstichproben Verzerrungen reduzieren und einen qualitativ hochwertigeren Datensatz liefernwie Sie mit den Prinzipien des experimentellen Designs eindeutige Antworten auf Ihre Forschungsfragen erhaltenwie Sie eine Regression verwenden, um Prognosen zu treffen oder Anomalien zu erkennenauf welche Weise statistische Methoden aus dem Bereich des Machine Learning es ermöglichen, aus Daten zu lernenwie Sie Unsupervised Learning nutzen, um Erkenntnisse aus ungelabelten Daten zu gewinnen.

Text vom Verlag

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