Dataseries continues to explore a diverse range of commonly usedapproaches to making and communicating decisions from data. Delvinginto more technical topics, this book equips readers with advanceddata mining methods that are needed to successfully translate rawdata into smart decisions across various fields of researchincluding business, engineering, finance, and the socialsciences.Following a comprehensive introduction that details how todefine a problem, perform an analysis, and deploy the results,Making Sense of Data II addresses the following keytechniques for advanced data analysis:Data Visualization reviews principles and methods forunderstanding and communicating data through the use ofvisualization including single variables, the relationship betweentwo or more variables, groupings in data, and dynamic approaches tointeracting with data through graphical user interfaces.Clustering outlines common approaches to clustering datasets and provides detailed explanations of methods for determiningthe distance between observations and procedures for clusteringobservations. Agglomerative hierarchical clustering,partitioned-based clustering, and fuzzy clustering are alsodiscussed.Predictive Analytics presents a discussion on how tobuild and assess models, along with a series of predictiveanalytics that can be used in a variety of situations includingprincipal component analysis, multiple linear regression,discriminate analysis, logistic regression, and NaïveBayes.Applications demonstrates the current uses of data miningacross a wide range of industries and features case studies thatillustrate the related applications in real-world scenarios.Each method is discussed within the context of a data miningprocess including defining the problem and deploying the results,and readers are provided with guidance on when and how each methodshould be used. The related Web site for the series(www.makingsenseofdata.com) provides a hands-on data analysis anddata mining experience. Readers wishing to gain more practicalexperience will benefit from the tutorial section of the book inconjunction with theTraceisTM software, which is freelyavailable online.With its comprehensive collection of advanced data miningmethods coupled with tutorials for applications in a range offields, Making Sense of Data II is an indispensable book forcourses on data analysis and data mining at the upper-undergraduateand graduate levels. It also serves as a valuable reference forresearchers and professionals who are interested in learning how toaccomplish effective decision making from data and understanding ifdata analysis and data mining methods could help theirorganization.

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