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Making Sense of Data II - A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications (Paperback)
Loot Price: R2,713
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Making Sense of Data II - A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications (Paperback)
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A hands-on guide to making valuable decisions from data using
advanced data mining methods and techniques This second installment
in the Making Sense of Data series continues to explore a diverse
range of commonly used approaches to making and communicating
decisions from data. Delving into more technical topics, this book
equips readers with advanced data mining methods that are needed to
successfully translate raw data into smart decisions across various
fields of research including business, engineering, finance, and
the social sciences. Following a comprehensive introduction that
details how to define a problem, perform an analysis, and deploy
the results, Making Sense of Data II addresses the following key
techniques for advanced data analysis: Data Visualization reviews
principles and methods for understanding and communicating data
through the use of visualization including single variables, the
relationship between two or more variables, groupings in data, and
dynamic approaches to interacting with data through graphical user
interfaces. Clustering outlines common approaches to clustering
data sets and provides detailed explanations of methods for
determining the distance between observations and procedures for
clustering observations. Agglomerative hierarchical clustering,
partitioned-based clustering, and fuzzy clustering are also
discussed. Predictive Analytics presents a discussion on how to
build and assess models, along with a series of predictive
analytics that can be used in a variety of situations including
principal component analysis, multiple linear regression,
discriminate analysis, logistic regression, and Naive Bayes.
Applications demonstrates the current uses of data mining across a
wide range of industries and features case studies that illustrate
the related applications in real-world scenarios. Each method is
discussed within the context of a data mining process including
defining the problem and deploying the results, and readers are
provided with guidance on when and how each method should be used.
The related Web site for the series (www.makingsenseofdata.com)
provides a hands-on data analysis and data mining experience.
Readers wishing to gain more practical experience will benefit from
the tutorial section of the book in conjunction with the Traceis TM
software, which is freely available online. With its comprehensive
collection of advanced data mining methods coupled with tutorials
for applications in a range of fields, Making Sense of Data II is
an indispensable book for courses on data analysis and data mining
at the upper-undergraduate and graduate levels. It also serves as a
valuable reference for researchers and professionals who are
interested in learning how to accomplish effective decision making
from data and understanding if data analysis and data mining
methods could help their organization.
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