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Algorithms for data science [electronic resource]

Algorithms for data science [electronic resource]

자료유형
E-Book(소장)
개인저자
Steele, Brian. Chandler, John. Reddy, Swarna.
서명 / 저자사항
Algorithms for data science [electronic resource] / Brian Steele, John Chandler, Swarna Reddy.
발행사항
Cham :   Springer International Publishing :   Imprint: Springer,   2016.  
형태사항
1 online resource (xxiii, 430 p.) : ill. (chiefly col.).
ISBN
9783319457970
요약
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.
일반주기
Title from e-Book title page.  
내용주기
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.
서지주기
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Quantitative research --Mathematics.
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URL
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001 000046030216
005 20200608130417
006 m d
007 cr
008 200601s2016 sz a ob 001 0 eng d
020 ▼a 9783319457970
040 ▼a 211009 ▼c 211009 ▼d 211009
082 0 4 ▼a 001.4/20151 ▼2 23
084 ▼a 001.420151 ▼2 DDCK
090 ▼a 001.420151
100 1 ▼a Steele, Brian.
245 1 0 ▼a Algorithms for data science ▼h [electronic resource] / ▼c Brian Steele, John Chandler, Swarna Reddy.
260 ▼a Cham : ▼b Springer International Publishing : ▼b Imprint: Springer, ▼c 2016.
300 ▼a 1 online resource (xxiii, 430 p.) : ▼b ill. (chiefly col.).
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references and index.
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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Quantitative research ▼x Mathematics.
700 1 ▼a Chandler, John.
700 1 ▼a Reddy, Swarna.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-319-45797-0
945 ▼a KLPA
991 ▼a E-Book(소장)

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/e-Book 컬렉션/ 청구기호 CR 001.420151 등록번호 E14023160 도서상태 대출불가(열람가능) 반납예정일 예약 서비스 M

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