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Algorithms for Data Science (Paperback, Softcover reprint of the original 1st ed. 2016)
Loot Price: R1,791
Discovery Miles 17 910
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Algorithms for Data Science (Paperback, Softcover reprint of the original 1st ed. 2016)
Expected to ship within 18 - 22 working days
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This textbook on practical data analytics unites fundamental
principles, algorithms, and data. Algorithms are the keystone of
data analytics and the focal point of this textbook. Clear and
intuitive explanations of the mathematical and statistical
foundations make the algorithms transparent. But practical data
analytics requires more than just the foundations. Problems and
data are enormously variable and only the most elementary of
algorithms can be used without modification. Programming fluency
and experience with real and challenging data is indispensable and
so the reader is immersed in Python and R and real data analysis.
By the end of the book, the reader will have gained the ability to
adapt algorithms to new problems and carry out innovative analyses.
This book has three parts:(a) Data Reduction: Begins with the
concepts of data reduction, data maps, and information extraction.
The second chapter introduces associative statistics, the
mathematical foundation of scalable algorithms and distributed
computing. Practical aspects of distributed computing is the
subject of the Hadoop and MapReduce chapter.(b) Extracting
Information from Data: Linear regression and data visualization are
the principal topics of Part II. The authors dedicate a chapter to
the critical domain of Healthcare Analytics for an extended example
of practical data analytics. The algorithms and analytics will be
of much interest to practitioners interested in utilizing the large
and unwieldly data sets of the Centers for Disease Control and
Prevention's Behavioral Risk Factor Surveillance System.(c)
Predictive Analytics Two foundational and widely used algorithms,
k-nearest neighbors and naive Bayes, are developed in detail. A
chapter is dedicated to forecasting. The last chapter focuses on
streaming data and uses publicly accessible data streams
originating from the Twitter API and the NASDAQ stock market in the
tutorials. This book is intended for a one- or two-semester course
in data analytics for upper-division undergraduate and graduate
students in mathematics, statistics, and computer science. The
prerequisites are kept low, and students with one or two courses in
probability or statistics, an exposure to vectors and matrices, and
a programming course will have no difficulty. The core material of
every chapter is accessible to all with these prerequisites. The
chapters often expand at the close with innovations of interest to
practitioners of data science. Each chapter includes exercises of
varying levels of difficulty. The text is eminently suitable for
self-study and an exceptional resource for practitioners.
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