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Algorithms for data science

Chandler, John.

Algorithms for data science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Algorithms for data scienceby Brian Steele, John Chandler, Swarna Reddy.
Author:	Steele, Brian.
other author:	Chandler, John.
Published:	Cham :Springer International Publishing :2016.
Description:	xxiii, 430 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Quantitative researchMathematics.
Online resource:	http://dx.doi.org/10.1007/978-3-319-45797-0
ISBN:	9783319457970$q(electronic bk.)

Algorithms for data science
Steele, Brian.

Algorithms for data science[electronic resource] /by Brian Steele, John Chandler, Swarna Reddy. - Cham :Springer International Publishing :2016. - xxiii, 430 p. :ill. (some col.), digital ;24 cm.

Introduction -- Data Mapping and Data Dictionaries -- Scalable Algorithms and Associative Statistics -- Hadoop and MapReduce -- Data Visualization -- Linear Regression Methods -- Healthcare Analytics -- Cluster Analysis -- k-Nearest Neighbor Prediction Functions -- The Multinomial Naive Bayes Prediction Function -- Forecasting -- Real-time Analytics.

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.

ISBN: 9783319457970$q(electronic bk.)

Standard No.: 10.1007/978-3-319-45797-0doiSubjects--Topical Terms:

764252
Quantitative research
--Mathematics.

LC Class. No.: Q180.55.Q36

Dewey Class. No.: 001.420151

Algorithms for data science
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245 1 0 $a Algorithms for data science $h [electronic resource] / $c by Brian Steele, John Chandler, Swarna Reddy.
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300 $a xxiii, 430 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Introduction -- Data Mapping and Data Dictionaries -- Scalable Algorithms and Associative Statistics -- Hadoop and MapReduce -- Data Visualization -- Linear Regression Methods -- Healthcare Analytics -- Cluster Analysis -- k-Nearest Neighbor Prediction Functions -- The Multinomial Naive Bayes Prediction Function -- Forecasting -- Real-time Analytics.
520 $a 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.
650 0 $a Quantitative research $x Mathematics. $3 764252
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Statistics and Computing/Statistics Programs. $3 275710
650 2 4 $a Mathematics of Computing. $3 273710
650 2 4 $a Health Informatics. $3 274212
700 1 $a Chandler, John. $3 764250
700 1 $a Reddy, Swarna. $3 764251
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-45797-0
950 $a Computer Science (Springer-11645)