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Mathematik für Data Scientists

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Mathematik für Data ScientistsNield,Thomas: Mathematik fr Data ScientistsVerlag: dpunkt. VerlagISBN 9783960092155 Neue Ware, Mngelexemplar, Zustand: gut Retourware: wegen minimaler Lagerspuren und Transportschden (wie leichte Kratzer) als Mngelexemplar gekennzeichnet. Text vom Verlag Dieses Buch richtet sich an angehende und fortgeschrittene Data Scientists sowie Programmierer_innen, die sich die mathematischen Grundlagen der Data Science aneignen wollen Besonders gut

Nield,Thomas:
Mathematik für Data Scientists
Verlag: dpunkt.Verlag
ISBN 9783960092155


Neue Ware, Mängelexemplar, Zustand: gut

Retourware: wegen minimaler Lagerspuren und Transportschäden
(wie leichte Kratzer) als Mängelexemplar gekennzeichnet.

Text vom Verlag

Dieses Buch richtet sich an angehende und fortgeschrittene Data Scientists sowie Programmierer_innen, die sich die mathematischen Grundlagen der Data Science aneignen wollen Besonders gut nachvollziehbar durch minimale mathematische Fachterminologie, praxisnahe Beispiele und zahlreiche Abbildungen Mit Übungen und Lösungen, um das Gelernte zu vertiefen Für Studium und Beruf
Um als Data Scientist erfolgreich zu sein, müssen Sie über ein solides mathematisches Grundwissen verfügen. Dieses Buch bietet einen leicht verständlichen Überblick über die Mathematik, die Sie in der Data Science benötigen. Thomas Nield führt Sie Schritt für Schritt durch Bereiche wie Infinitesimalrechnung, Wahrscheinlichkeit, lineare Algebra, Statistik und Hypothesentests und zeigt Ihnen, wie diese Mathe-Basics beispielsweise in der linearen und logistischen Regression und in neuronalen Netzen eingesetzt werden. Zusätzlich erhalten Sie Einblicke in den aktuellen Stand der Data Science und erfahren, wie Sie dieses Wissen für Ihre Karriere als Data Scientist nutzen.
Verwenden Sie Python-Code und Bibliotheken wie SymPy, NumPy und scikit-learn, um grundlegende mathematische Konzepte wie Infinitesimalrechnung, lineare Algebra, Statistik und maschinelles Lernen zu erkunden Verstehen Sie Techniken wie lineare und logistische Regression und neuronale Netze durch gut nachvollziehbare Erklärungen und ein Minimum an mathematischer Terminologie Wenden Sie deskriptive Statistik und Hypothesentests auf einen Datensatz an, um p-Werte und statistische Signifikanz zu interpretieren Manipulieren Sie Vektoren und Matrizen und führen Sie Matrixzerlegung durch Vertiefen Sie Ihre Kenntnisse in Infinitesimal- und Wahrscheinlichkeitsrechnung, Statistik und linearer Algebra und wenden Sie sie auf Regressionsmodelle einschließlich neuronaler Netze an Erfahren Sie, wie Sie Ihre Kenntnisse und Fähigkeiten in der Datenanalyse optimieren und gängige Fehler vermeiden, um auf dem Data-Science-Arbeitsmarkt zu überzeugen

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