Title From Data to Decision
Subtitle A Handbook for the Modern Business Analyst
Author Marco Vriens, Chad Vidden, Song Chen
ISBN 9781516520633
List price USD 123.95
Price outside India Available on Request
Original price
Binding Paperback
No of pages 326
Book size 204 X 254 mm
Publishing year 2019
Original publisher Cognella Academic Publishing (Eurospan Group)
Published in India by .
Exclusive distributors Viva Books Private Limited
Sales territory India, Sri Lanka, Bangladesh, Pakistan, Nepal, .
Status New Arrival
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Description:

From Data to Decision: A Handbook for the Modern Business Analyst provides readers with a comprehensive guide to understanding the inherent value of business analytics, building critical skill sets to conduct effective analyses, deriving valuable insight from analyses, and guiding management and other personnel toward well-informed, strategic decisions that bolster the health of a company or organization.

The text begins with a chapter that outlines the rise of analytics as a dedicated discipline, its role in business decision-making, and various types of analyses. Additional chapters introduce readers to data strategy, a framework for and process for analytics, and how to apply insights for maximum impact within companies and organizations. Students examine analysis methods including linear regression, logistic regression, decision trees, multi-dimensional scaling, factor analysis, text analytics, time-series analysis, and neural nets. Throughout, readers are challenged to connect the dots between analysis and its effective application within business settings.

A robust guide to modern analysis, From Data to Decision is an ideal textbook for courses in business and analytics, and suitable for both undergraduate and graduate studies.



Contents:

Chapter 1: Introduction: The Business of Analytics • What am I going to learn? • The Rise and Scope of Analytics • Defining Analytics • What and How • General Purpose • Data • The Analytical Challenge • The Value of Analytics • Proactive Benefits • Reactive Benefits • Empirical Evidence • Quiz • References

Chapter 2: Data and Data Strategy • What am I going to learn? • What Data Is Available? • External Data Versus Internal Data • Levels of Aggregation • Cross-Sectional Versus Longitudinal Versus Time Series Data • Hard Data Versus Soft Data • Scanner Data • Structured Versus Unstructured Data • Observational Data • RFID Data • MtM SIM Card Data (IoT) • Spatial Data • Path Data • Big Data • Data Governance • Data Inventory • Data Processes and Evaluation • Data Integration • Privacy and Ethical Considerations • Quiz and Exercises • References

Chapter 3: A Framework and Process for Analytics • What am I going to learn? • How it works • Target 1: Market Dynamics • Market Definition and Sizing • Target 2: Market Share Dynamics •
Aggregate-Level Market Share Models • Survey-Based Disaggregate Brand Choice Models • Predicting New (or Improved) Product Success • Target 3: Marketing Campaign Dynamics • Target 4: : Customer Dynamics • Analytical Project Execution • Business Problem and Understanding • Reviewing What We Already Know • Data Review and Preparation • Analysis and Modeling • Interpretation and the So What • Validation • Deployment • Advanced Topics: Data-Driven Versus Theory-Driven • Better Research Design • Guiding Analysis and Modeling • Helping with the Interpretation and Validation • A Data-Driven Approach • A Rapidly Expanding Field • Quiz • References

Chapter 4: From Insights to Impact • What am I going to learn? • Introduction • Intention to Use • Insights Knowledge • Insights Credibility • Advisor Credibility and Trust • Acceptability • Driving Decisions Quality • Outcome and Insights Uncertainty • Decision Failure Example • Managing Analytical Insights • Quiz and Exercises • References

Chapter 5: Basic Analysis • What am I going to learn? • How it works • Dataset • Types of Variables • Data Exploration and Visualization • Categorical Variables and Cross Tabulation • Numeric Variables and Distributions • Data Transformations • Missing Data • Bivariate Relationships • Correlation • Statistical Tests for Comparing Groups • (X2, T-test, and ANOVA) • ?2-Test • Student’s T-test • Analysis of Variance (ANOVA) • Advanced Topics • ANOVA Extensions • Multivariate Analyses • Business Applications • Assessing if a Medical Adherence Regimen Works • Assessing if Executives Should be Rewarded for the Performance Gains • Quiz • References

Chapter 6: Linear Regression • What am I going to learn? • How it works • Linear Regression • Implementing Linear Regression • Multiple Linear Regression • Is My Result Usable? • Prediction and Forecasting • Causality and Driver Analysis • External Validation • Troubleshooting • Advanced Topics • Business Applications: Predicting the Success of Movies • Quiz and Exercises • References

Chapter 7: Logistic Regression • What am I going to learn? • How it works • Data Exploration • Why Linear Regression Fails • Logistic Regression • Driver Analysis • Model Features • Key Driver Strength • Key Driver Stability • Is My Result Usable? • Accuracy and the Confusion Matrix • Choosing the Best Probability Threshold • Decision Costs and Benefits • A Business Example: Competitive Drivers and the Switchable Consumer • The Switchable Consumer for Restaurants • Advanced Topics • Multi-class Logistic Regression • Quiz and Exercises • Multinomial Logistic Regression • Ordinal Classification • References

Chapter 8: Decision Tress • What am I going to learn? • How it works • Decision Tree Algorithm • Model Interpretation • Under-Fitting and Over-Fitting • When to Use Decision Trees? • Decision Tree Advantages • Decision Tree Disadvantages • Advanced Topics • Types of Decision Trees • Marketing Segmentation • Random Forests • Business Applications • Quiz • References

Chapter 9: Multi-Dimensional Scaling (MDS) • What am I going to learn? • Terminology • How it works • The Basic MDS Idea • Evaluating MDS Maps • Further Discussion • Advanced Topics • Variations of MDS • Other Dimension Reduction Tools • Business Application • Dataset • Quiz and Exercises • References

Chapter 10: Principal Component Analysis and Factor Analysis • What am I going to learn? • How it works • Scaling Data • PCA • Basic Example • Selecting Principal Components • Visualizing Principal Components • Exploratory Factor Analysis • Rotation and Uniqueness • PCA and Multicollinearity • Comparing PCA and FA • Advanced Topics • Confirmatory Factor Analysis • Parallel Analysis • Business Applications • Quiz • References

Chapter 11: Cluster Analysis • What am I going to learn? • How it works • Select the Segmentation Variables • Decide Whether to Standardize • Decide How to Measure the Similarity Between Respondents • Selection of a Cluster Analysis Algorithm • K-means • Hierarchical Clustering • Hierarchical vs. K-means • Evaluation Clustering Solutions • The Use of Core Members • Typing Tools • Advanced Topics • K-modes • Latent Class Analysis • Additive and Extended Trees • Ensemble Method • Practical Considerations • Large Number of Variables • A Business Application • Analysis and Evaluation • Results • Quiz and Exercises • References

Chapter 12: Time Series Analysis • What am I going to learn? • How it works • What Is ARIMA? • Auto-Regression • Stationary Series • Moving Average • Data Exploration • Linear Regression • Decomposition • Stationary Series • ARIMA Model Order • Fit and Model Testing • Seasonality • Advanced Topics • Exponential Smoothing State Smooth Time Series Model • Business Application • Marketing-Mix Models • Quiz • References

Chapter 13: Neural Networks And Machine Learning • What am I going to learn? • How it works • Terminology • What Is Machine Learning? • Neural Networks • The basic model idea • Architecture and complexity • Advanced Topics • Business Applications • Quiz • References

Chapter 14: Text Analytics • What am I going to learn? • How it works • Dataset • Text Analytics Process • Text Cleaning • Word Frequency • Document Term Matrix • LDA Topic Models • Comments on LDA Models • Advanced Topics • Natural Language Processing • Business Applications • Quiz • References

Index


About the Authors:

Marco Vriens is an assistant professor at the University of Wisconsin-La Crosse and the founder of Kwantum, an analytics firm. He earned a masters in psychology from Leiden University and a Ph.D. in business administration from the University of Groningen. He specializes in marketing analytics, brand research, research methodology, and consumer psychology and decision-making. He is the author of The Insights Advantage (2012), and editor of the
Handbook of Marketing Research (2006).

Chad Vidden is an associate professor of mathematics and statistics at the University of Wisconsin-La Crosse. He earned his Ph.D. in applied mathematics from Iowa State University. He specializes in machine learning, data science, numerical analysis, and computational mathematics.

Song Chen is an associate professor of mathematics and statistics at the University of Wisconsin-La Crosse. He earned his Ph.D. in applied mathematics from Auburn University. He specializes in data science and scientific computing.


Target Audience:

This is an ideal textbook for courses in business and analytics, and suitable for both undergraduate and graduate studies.

 

 
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