rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Algorithms for Data Science
  name:
    name part: Steele, Brian.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Chandler, John.
    role:
      role term: author.
  name:
    name part: Reddy, Swarna.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXIII, 430 p. 48 illus., 30 illus. in color. online resource.
  abstract: 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 an…
  table of contents: 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.
  note: by Brian Steele, John Chandler, Swarna Reddy.
  subject:
    topic: Computer science
  subject:
    topic: Health informatics
  subject:
    topic: Computer science
    topic: Mathematics
  subject:
    topic: Data mining
  subject:
    topic: Statistics
  subject:
    topic: Computer Science
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Statistics and Computing/Statistics Programs
  subject:
    topic: Mathematics of Computing
  subject:
    topic: Health Informatics
  classification: QA76.9.D343
  classification: 006.312
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319457970
  identifier: http://dx.doi.org/10.1007/978-3-319-45797-0
  record info:
    record creation date: 161226
    record change date: 20161226082019.0
    record identifier: 978-3-319-45797-0