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Business Analytics: Methods and Cases for Data-Driven Decisions [Kõva köide]

(University of California, Berkeley)
  • Formaat: Hardback, 700 pages, kõrgus x laius x paksus: 260x211x32 mm, kaal: 1660 g, Worked examples or Exercises
  • Ilmumisaeg: 02-Jan-2025
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1316512150
  • ISBN-13: 9781316512159
Teised raamatud teemal:
Business Analytics: Methods and Cases for Data-Driven DecisionsSuurem pilt
  • Formaat: Hardback, 700 pages, kõrgus x laius x paksus: 260x211x32 mm, kaal: 1660 g, Worked examples or Exercises
  • Ilmumisaeg: 02-Jan-2025
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1316512150
  • ISBN-13: 9781316512159
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781316512159.html
Märksõnad:
Business analytics is about leveraging data analysis and analytical modeling methods to achieve business objectives. Suitable for senior and graduate students in business or data science, this innovative text presents methods in an intuitive fashion and backs up business applications with an approachable level of mathematical rigor.

Business analytics is all about leveraging data analysis and analytical modeling methods to achieve business objectives. This book is suitable for students with special interest in data science, business, or both. Methods are presented in an intuitive fashion, and business applications are backed up by an approachable level of mathematical rigor. The balance makes the material accessible to students with a broad range of backgrounds: those looking for 'So that's how it works!', 'So that's what it's for!', and 'So that's how to do it!' Students will come away well-positioned and well-differentiated for their future industry, consulting, academic, and government roles as data-savvy business practitioners. Accompanied by a robust set of digital materials including code, data sets, primers, and more for both students and instructors, this text is the ideal learning resource for senior and graduate students in business or data science departments.

Arvustused

'A must-read for anyone in business analytics - this book combines real-life case studies and practical insights from an experienced professional. It's thoughtfully crafted to guide learners through complex concepts with ease, making it invaluable for students and professionals alike.' Raghu Santanam, Arizona State University 'An outstanding compendium for the practice of business analytics, covering all relevant machine learning models with remarkable clarity. The real-world business cases are a rich and unique source of insights, making this an ideal textbook for university classes. A must-have for anyone interested in driving decisions through data.' Emanuele Borgonovo, Bocconi University 'This is the very text I've been looking for and that students in our program ask for - useful, immediately comprehensible, and expertly crafted. Huntsinger provides clear explanations of complex concepts, weaving together theory, data processing, and real-world applications in a seamless way. As business analytics textbooks go, it's near perfect.' Gerald Benoît, Harvard University 'An excellent read with the richness of literature and the utility of a guide. Huntsinger delves into the depths of descriptive, inquisitive, and predictive approaches to data, using relevant and interesting examples. Highly recommended for anyone looking to hone their analytic skills using modern tools.' Karthik Suri, World Economic Forum 'Huntsinger masterfully blends theoretical concepts with practical applications, making complex concepts and analytical methods accessible. The book will become a go-to resource for anyone navigating applied machine learning for business decision-making.' Asish Satpathy, Arizona State University 'A superb teaching resource for big-data business analytics. With engaging, data-rich case studies and a wealth of digital support materials, it is ideal for students and tutors alike.' Tom Kane, University of Stirling 'A great business analytics textbook that connects businesses, decisions, data, and analytical methods effectively. You will find a purpose before learning each method!' Dungang Liu, University of Cincinnati 'An invaluable resource for professionals, offering clear explanations and comprehensive frameworks for addressing complex business challenges through data.' Ryan Orton, Greenwood Management Advisors

Muu info

A comprehensive, innovative textbook suitable for senior and graduate students in business or data science departments.
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.
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.