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Interest in predictive analytics of big data has grown
exponentially in the four years since the publication of
Statistical and Machine-Learning Data Mining: Techniques for Better
Predictive Modeling and Analysis of Big Data, Second Edition. In
the third edition of this bestseller, the author has completely
revised, reorganized, and repositioned the original chapters and
produced 13 new chapters of creative and useful machine-learning
data mining techniques. In sum, the 43 chapters of simple yet
insightful quantitative techniques make this book unique in the
field of data mining literature. What is new in the Third Edition:
The current chapters have been completely rewritten. The core
content has been extended with strategies and methods for problems
drawn from the top predictive analytics conference and statistical
modeling workshops. Adds thirteen new chapters including coverage
of data science and its rise, market share estimation, share of
wallet modeling without survey data, latent market segmentation,
statistical regression modeling that deals with incomplete data,
decile analysis assessment in terms of the predictive power of the
data, and a user-friendly version of text mining, not requiring an
advanced background in natural language processing (NLP). Includes
SAS subroutines which can be easily converted to other languages.
As in the previous edition, this book offers detailed background,
discussion, and illustration of specific methods for solving the
most commonly experienced problems in predictive modeling and
analysis of big data. The author addresses each methodology and
assigns its application to a specific type of problem. To better
ground readers, the book provides an in-depth discussion of the
basic methodologies of predictive modeling and analysis. While this
type of overview has been attempted before, this approach offers a
truly nitty-gritty, step-by-step method that both tyros and experts
in the field can enjoy playing with.
Interest in predictive analytics of big data has grown
exponentially in the four years since the publication of
Statistical and Machine-Learning Data Mining: Techniques for Better
Predictive Modeling and Analysis of Big Data, Second Edition. In
the third edition of this bestseller, the author has completely
revised, reorganized, and repositioned the original chapters and
produced 13 new chapters of creative and useful machine-learning
data mining techniques. In sum, the 43 chapters of simple yet
insightful quantitative techniques make this book unique in the
field of data mining literature. What is new in the Third Edition:
The current chapters have been completely rewritten. The core
content has been extended with strategies and methods for problems
drawn from the top predictive analytics conference and statistical
modeling workshops. Adds thirteen new chapters including coverage
of data science and its rise, market share estimation, share of
wallet modeling without survey data, latent market segmentation,
statistical regression modeling that deals with incomplete data,
decile analysis assessment in terms of the predictive power of the
data, and a user-friendly version of text mining, not requiring an
advanced background in natural language processing (NLP). Includes
SAS subroutines which can be easily converted to other languages.
As in the previous edition, this book offers detailed background,
discussion, and illustration of specific methods for solving the
most commonly experienced problems in predictive modeling and
analysis of big data. The author addresses each methodology and
assigns its application to a specific type of problem. To better
ground readers, the book provides an in-depth discussion of the
basic methodologies of predictive modeling and analysis. While this
type of overview has been attempted before, this approach offers a
truly nitty-gritty, step-by-step method that both tyros and experts
in the field can enjoy playing with.
In an accessible yet fastidiously researched intervention Early
Detection sets out the urgent necessity of fundamentally
re-directing the US’s approach to cancer treatment if President
Biden’s recently announced prioritization of the issue is to be
successful. Catching cancer early remains the single best approach
to fighting this deadly disease, the second-leading killer both in
the US and worldwide. Yet, the health system often fails to do so,
even when the necessary tools are available. Early Detection looks
at shortcomings in cancer screening efforts and how early
detection procedures can be expanded and improved.. Early Detection
explores cancer screening systematically and scientifically,
examining the subject from the level of individual tests all the
way up to the roles and incentives of large healthcare systems and
the federal government. It looks not only at the scientific
challenges involved but also the social and organizational
challenges, an angle that has been traditionally under-covered but
is especially relevant in light of the COVID-19 pandemic. The book
also highlights the disparities of race and economic class that
affect access to early screening. This problem exists throughout
the medical system overall but, when it comes to early detection,
the problem becomes especially far-reaching from both an ethical
and an economic point of view. In teaming together, Bruce Ratner
and Adam Bonislawski combine the passions of someone touched deeply
by the experience of cancer and the cool analysis of an expert in
medical policy and science. They tackle the subject with a
combination of breadth and granularity, exploring why early
detection has not been given the level of priority it deserves, and
the ways it can dramatically reduce cancer deaths in this country.
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