Business Analytics
Business Analytics
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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.
- Uniquely designed for both the business-oriented and data science-oriented student
- Includes a comprehensive collection of data analysis and analytical modeling methods from the machine learning and artificial intelligence repertoires
- Includes professional-grade business cases based on real industry data
- Accompanied by a rich set of online resources
- Demonstrates business analytics from three angles: 'What is it for?', 'How does it work?', 'How do I do it?'
Reviews & endorsements
'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
Product details
January 2025Paperback
9781009060790
700 pages
253 × 202 × 28 mm
1.48kg
Available
Table of Contents
- 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.
