fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf
fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf
fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf
fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf
fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf

Site Breadcrumb

Verkauf durch Sack Fachmedien

Huntsinger

Business Analytics

Medium: Buch
ISBN: 978-1-316-51215-9
Verlag: Cambridge University Press
Erscheinungstermin: 02.01.2025
In den Warenkorb
Lieferfrist: bis zu 10 Tage
Auf die Merkliste Drucken

Leseprobe Vorwort
Überschrift

Beschreibung

Kostenloser Versand

nach Deutschland, Österreich und in die Schweiz

Bequem zahlen

Rechnung, Kreditkarte, PayPal oder Bankeinzug

Wir sind gerne für Sie da

0800 000-1378 (Inland)
service@fachmedien.de
<<Kontaktformular>>

Fragen

Fragen Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua
Anwtorten

Anwtorten At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet

Business analytics is all about leveraging data analysis and analytical modeling methods to achieve business objectives. This is the book for upper division and graduate business students with interest in data science, for data science students with interest in business, and for everyone with interest in both. A comprehensive collection of over 50 methods and cases is presented in an intuitive style, generously illustrated, and backed up by an approachable level of mathematical rigor appropriate to a range of proficiency levels. A robust set of online resources, including software tools, coding examples, datasets, primers, exercise banks, and more for both students and instructors, makes the book the ideal learning resource for aspiring data-savvy business practitioners.

Produkteigenschaften

  • Artikelnummer: 9781316512159
  • Medium: Buch
  • ISBN: 978-1-316-51215-9
  • Verlag: Cambridge University Press
  • Erscheinungstermin: 02.01.2025
  • Sprache(n): Englisch
  • Auflage: Erscheinungsjahr 2025
  • Produktform: Gebunden
  • Gewicht: 1660 g
  • Seiten: 700
  • Format (B x H x T): 209 x 257 x 33 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Autoren

Dr. Richard Huntsinger is an author, professor, expert witness, Silicon Valley entrepreneur, Fortune 500 R&D executive, and management consultant with broad international business and technology experience leading programs in data analytics, process automation, and enterprise software development. He now serves as Faculty Director and Distinguished Teaching Fellow at the University of California, Berkeley, where he lectures and oversees research on data strategy and data science applied to business, law, and public policy.

Executive Overview; 1. Data and Decisions; 1.1 Learning Objectives; 1.2 Introduction; 1.3 Data-to-Decision Process Model; 1.4 Decision Models; 1.5 Sensitivity Analysis; 2. Data Preparation; 2.1 Learning Objectives; 2.2 Data Objects; 2.3 Selection; 2.4 Amalgamation; 2.5 Synthetic Variables; 2.6 Normalization; 2.7 Dummy Variables; 2.8 CASE High-Tech Stocks; 3. Data Exploration; 3.1 Learning Objectives; 3.2 Descriptive Statistics; 3.3 Similarity; 3.4 Cross-Tabulation; 3.5 Data Visualization; 3.6 Kernel Density Estimation; 3.7 CASE Fundraising Strategy; 3.8 CASE Iowa Liquor Sales; 4. Data Transformation; 4.1 Learning Objectives; 4.2 Balance; 4.3 Imputation; 4.4 Alignment; 4.5 Principal Component Analysis; 4.6 CASE Loan Portfolio; 5. Classification I; 5.1 Learning Objectives; 5.2 Classification Methodology; 5.3 Classifier Evaluation; 5.4 k-Nearest Neighbors; 5.5 Logistic Regression; 5.6 Decision Tree; 5.7 CASE Loan Portfolio Revisited; 6. Classification II; 6.1 Learning Objectives; 6.2 Naive Bayes; 6.3 Support Vector Machine; 6.4 Neural Network; 6.5 CASE Telecom Customer Churn; 6.6 CASE Truck Fleet Maintenance; 7. Classification III; 7.1 Learning Objectives; 7.2 Multinomial Classification; 7.3 CASE Facial Recognition; 7.4 CASE Credit Card Fraud; 8. Regression; 8.1 Learning Objectives; 8.2 Regression Methodology; 8.3 Regressor Evaluation; 8.4 Linear Regression; 8.5 Regression Versions; 8.6 CASE Call Center Scheduling; 9. Ensemble Assembly; 9.1 Learning Objectives; 9.2 Bagging; 9.3 Boosting; 9.4 Stacking; 10. Cluster Analysis; 10.1 Learning Objectives; 10.2 Cluster Analysis Methodology; 10.3 Cluster Model Evaluation; 10.4 k-Means; 10.5 Hierarchical Agglomeration; 10.6 Gaussian Mixture; 10.7 CASE Fortune 500 Diversity; 10.8 CASE Music Market Segmentation; 11. Special Data Types; 11.1 Learning Objectives; 11.2 Text Data; 11.3 Time Series Data; 11.4 Network Data; 11.5 PageRank for Network Data; 11.6 Collaborative Filtering for Network Data; 11.7 CASE Deceptive Hotel Reviews; 11.8 CASE Targeted Marketing.