http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
a practitioner's guide to transforming big data into added value
Maximize profit and optimize decisions with advanced business analytics Profit-Driven Business Analytics provides actionable guidance on optimizing the use of data to add value and drive better business. Combining theoretical and technical insights into daily operations and long-term strategy, this book acts as a development manual for practitioners seeking to conceive, develop, and manage advanced analytical models. Detailed discussion delves into the wide range of analytical approaches and modeling techniques that can help maximize business payoff, and the author team draws upon their recent research to share deep insight about optimal strategy. Real-life case studies and examples illustrate these techniques at work, and provide clear guidance for implementation in your own organization. From step-by-step instruction on data handling, to analytical fine-tuning, to evaluating results, this guide provides invaluable guidance for practitioners seeking to reap the advantages of true business analytics. Despite widespread discussion surrounding the value of data in decision making, few businesses have adopted advanced analytic techniques in any meaningful way. This book shows you how to delve deeper into the data and discover what it can do for your business.-Reinforce basic analytics to maximize profits -Adopt the tools and techniques of successful integration -Implement more advanced analytics with a value-centric approach -Fine-tune analytical information to optimize business decisions Both data stored and streamed has been increasing at an exponential rate, and failing to use it to the fullest advantage equates to leaving money on the table. From bolstering current efforts to implementing a full-scale analytics initiative, the vast majority of businesses will see greater profit by applying advanced methods. Profit-Driven Business Analytics provides a practical guidebook and reference for adopting real business analytics techniques.
Cover ; Title Page ; Copyright ; Contents ; Foreword; Acknowledgments; Chapter 1: A Value-Centric Perspective Towards Analytics ; Introduction ; Business Analytics ; Profit-Driven Business Analytics ; Analytics Process Model ; Analytical Model Evaluation ; Analytics Team ; Profiles.
Data Scientists Conclusion ; Review Questions; Multiple Choice Questions; Open Questions; References ; Chapter 2: Analytical Techniques ; Introduction ; Data Preprocessing ; Denormalizing Data for Analysis ; Sampling ; Exploratory Analysis ; Missing Values ; Outlier Detection and Handling.
Principal Component Analysis Types of Analytics ; Predictive Analytics ; Introduction ; Linear Regression ; Logistic Regression ; Decision Trees ; Neural Networks ; Ensemble Methods ; Bagging ; Boosting ; Random Forests ; Evaluating Ensemble Methods.
Evaluating Predictive Models Splitting Up the Dataset ; Performance Measures for Classification Models ; Performance Measures for Regression Models ; Other Performance Measures for Predictive Analytical Models ; Descriptive Analytics ; Introduction ; Association Rules.
Sequence Rules Clustering ; Survival Analysis ; Introduction ; Survival Analysis Measurements ; Kaplan Meier Analysis ; Parametric Survival Analysis ; Proportional Hazards Regression ; Extensions of Survival Analysis Models ; Evaluating Survival Analysis Models.
Wouter Verbeke, Bart Baesens, Cristián Bravo.
Includes bibliographical references and index.
Hoboken, New Jersey : John Wiley & Sons, Inc., [2018]
7C6F2756-CE32-4DE6-9FFA-D3C3F2D44C38 OverDrive, Inc.