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Scientific Data Analysis Using Jython Scripting and Java

Medium: Buch
ISBN: 978-1-84996-286-5
Verlag: Springer
Erscheinungstermin: 20.08.2010
Lieferfrist: bis zu 10 Tage

Scientific Data Analysis using Jython Scripting and Java presents practical approaches for data analysis using Java scripting based on Jython, a Java implementation of the Python language. The chapters essentially cover all aspects of data analysis, from arrays and histograms to clustering analysis, curve fitting, metadata and neural networks. A comprehensive coverage of data visualisation tools implemented in Java is also included.

Written by the primary developer of the jHepWork data-analysis framework, the book provides a reliable and complete reference source laying the foundation for data-analysis applications using Java scripting. More than 250 code snippets (of around 10-20 lines each) written in Jython and Java, plus several real-life examples help the reader develop a genuine feeling for data analysis techniques and their programming implementation.

This is the first data-analysis and data-mining book which is completely based on the Jython language, and opens doors to scripting using a fully multi-platform and multi-threaded approach. Graduate students and researchers will benefit from the information presented in this book.


Produkteigenschaften


  • Artikelnummer: 9781849962865
  • Medium: Buch
  • ISBN: 978-1-84996-286-5
  • Verlag: Springer
  • Erscheinungstermin: 20.08.2010
  • Sprache(n): Englisch
  • Auflage: 2010. Auflage 2010
  • Serie: Advanced Information and Knowledge Processing
  • Produktform: Gebunden
  • Gewicht: 1820 g
  • Seiten: 440
  • Format (B x H x T): 163 x 240 x 33 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

Jython, Java and jHepWork.- to Jython.- Mathematical Functions.- One-dimensional Data.- Two-dimensional Data.- Multi-dimensional Data.- Arrays, Matrices and Linear Algebra.- Histograms.- Random Numbers and Statistical Samples.- Graphical Canvases.- Input and Output.- Miscellaneous Analysis Issues Using jHepWork.- Data Clustering.- Linear Regression and Curve Fitting.- Neural Networks.- Steps in Data Analysis.- Real-life Examples.