Data Mining For Business Analytics Concepts Techniques and Aons with XLMiner 3rd editon by Galit Shmueli , Peter Bruce, Nitin Patel 1118729277 9781118729274
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ISBN 10: 1118729277
ISBN 13: 9781118729274
Author: Galit Shmueli; Peter C. Bruce; Nitin R. Patel
Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
Praise for the Second Edition
“…full of vivid and thought-provoking anecdotes… needs to be read by anyone with a serious interest in research and marketing.”
– Research Magazine
“Shmueli et al. have done a wonderful job in presenting the field of data mining – a welcome addition to the literature.”
– ComputingReviews.com
“Excellent choice for business analysts…The book is a perfect fit for its intended audience.”
– Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization
“…extremely well organized, clearly written and introduces all of the basic ideas quite well.”
– Robert L. Phillips, Professor of Professional Practice, Columbia Business School
Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data.
Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes:
- Real-world examples to build a theoretical and practical understanding of key data mining methods
- End-of-chapter exercises that help readers better understand the presented material
- Data-rich case studies to illustrate various applications of data mining techniques
- Completely new chapters on social network analysis and text mining
- A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides
- Free 140-day license to use XLMiner for Education software
Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.
Table of contents:
Part I: Preliminaries
Chapter 1: Introduction
1.1 What is Business Analytics?
1.2 What is Data Mining?
1.3 Data Mining and Related Terms
1.4 Big Data
1.5 Data Science
1.6 Why are There so Many Different Methods?
1.7 Terminology and Notation
1.8 Road Maps to This Book
Chapter 2: Overview of the Data Mining Process
2.1 Introduction
2.2 Core Ideas in Data Mining
2.3 The Steps in Data Mining
2.4 Preliminary Steps
2.5 Predictive Power and Overfitting
2.6 Building a Predictive Model with XLMiner
2.7 Using Excel for Data Mining
2.8 Automating Data Mining Solutions
Problems
Part II: Data Exploration and Dimension Reduction
Chapter 3: Data Visualization
3.1 Uses of Data Visualization
3.2 Data Examples
3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots
3.4 Multidimensional Visualization
3.5 Specialized Visualizations
3.6 Summary: Major Visualizations and Operations, by Data Mining Goal
Problems
Chapter 4: Dimension Reduction
4.1 Introduction
4.2 Curse of Dimensionality
4.3 Practical Considerations
4.4 Data Summaries
4.5 Correlation Analysis
4.6 Reducing the Number of Categories in Categorical Variables
4.7 Converting a Categorical Variable to a Numerical Variable
4.8 Principal Components Analysis
4.9 Dimension Reduction Using Regression Models
4.10 Dimension Reduction Using Classification and Regression Trees
Problems
Part III: Performance Evaluation
Chapter 5: Evaluating Predictive Performance
5.1 Introduction
5.2 Evaluating Predictive Performance
5.3 Judging Classifier Performance
5.4 Judging Ranking Performance
5.5 Oversampling
Problems
Part IV: Prediction and Classification Methods
Chapter 6: Multiple Linear Regression
6.1 Introduction
6.2 Explanatory vs. Predictive Modeling
6.3 Estimating the Regression Equation and Prediction
6.4 Variable Selection in Linear Regression
Problems
Chapter 7: k-Nearest-Neighbors (k-NN)
7.1 The k-NN Classifier (Categorical Outcome)
7.2 k-NN for a Numerical Response
7.3 Advantages and Shortcomings of k-NN Algorithms
Problems
Chapter 8: The Naive Bayes Classifier
8.1 Introduction
8.2 Applying the Full (Exact) Bayesian Classifier
8.3 Advantages and Shortcomings of the Naive Bayes Classifier
Problems
Chapter 9: Classification and Regression Trees
9.1 Introduction
9.2 Classification Trees
9.3 Evaluating the Performance of a Classification Tree
9.4 Avoiding Overfitting
9.5 Classification Rules from Trees
9.6 Classification Trees for More Than two Classes
9.7 Regression Trees
9.8 Advantages, Weaknesses, and Extensions
9.9 Improving Prediction: Multiple Trees
Problems
Chapter 10: Logistic Regression
10.1 Introduction
10.2 The Logistic Regression Model
10.3 Evaluating Classification Performance
10.4 Example of Complete Analysis: Predicting Delayed Flights
10.5 Appendix: Logistic Regression for Profiling
Problems
Chapter 11: Neural Nets
11.1 Introduction
11.2 Concept and Structure of a Neural Network
11.3 Fitting a Network to Data
11.4 Required User Input
11.5 Exploring the Relationship Between Predictors and Response
11.6 Advantages and Weaknesses of Neural Networks
Problems
Chapter 12: Discriminant Analysis
12.1 Introduction
12.2 Distance of an Observation from a Class
12.3 Fishers Linear Classification Functions
12.4 Classification Performance of Discriminant Analysis
12.5 Prior Probabilities
12.6 Unequal Misclassification Costs
12.7 Classifying More Than Two Classes
12.8 Advantages and Weaknesses
Problems
Chapter 13: Combining Methods: Ensembles and Uplift Modeling
13.1 Ensembles
13.2 Uplift (Persuasion) Modeling
13.3 Summary
Problems
Part V: Mining Relationships among Records
Chapter 14: Association Rules and Collaborative Filtering
14.1 Association Rules
14.2 Collaborative Filtering
14.3 Summary
Problems
Chapter 15: Cluster Analysis
15.1 Introduction
15.2 Measuring Distance Between Two Observations
15.3 Measuring Distance Between Two Clusters
15.4 Hierarchical (Agglomerative) Clustering
15.5 Non-hierarchical Clustering: The k-Means Algorithm
Problems
Part VI: Forecasting Time Series
Chapter 16: Handling Time Series
16.1 Introduction
16.2 Descriptive vs. Predictive Modeling
16.3 Popular Forecasting Methods in Business
16.4 Time Series Components
16.5 Data Partitioning and Performance Evaluation
Problems
Chapter 17: Regression-Based Forecasting
17.1 A Model with Trend
17.2 A Model with Seasonality
17.3 A Model with Trend and Seasonality
17.4 Autocorrelation and ARIMA Models
Problems
Chapter 18: Smoothing Methods
18.1 Introduction
18.2 Moving Average
18.3 Simple Exponential Smoothing
18.4 Advanced Exponential Smoothing
Problems
Part VII: Data Analytics
Chapter 19: Social Network Analytics
19.1 Introduction
19.2 Directed Vs. Undirected Networks
19.3 Visualizing and Analyzing Networks
19.4 Social Data Metrics and Taxonomy
19.5 Using Network Metrics in Prediction and Classification
19.6 Advantages and Disadvantages
Problems
Chapter 20: Text Mining
20.1 Introduction
20.2 The Spreadsheet Representation of Text: “Bag-of-Words”
20.3 Bag-of-Words Vs. Meaning Extraction at Document Level
20.4 Preprocessing the Text
20.5 Implementing Data Mining Methods
20.6 Example: Online Discussions on Autos and Electronics
20.7 Summary
Problems
Part VIII: Cases
Chapter 21: Cases
21.1 Charles Book Club
21.2 German Credit
21.3 Tayko Software Cataloger
21.4 Political Persuasion
21.5 Taxi Cancellations
21.6 Segmenting Consumers of Bath Soap
21.7 Direct-Mail Fundraising
21.8 Catalog Cross-Selling
21.9 Predicting Bankruptcy
21.10 Time Series Case: Forecasting Public Transportation Demand
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