Numerical
computation, knowledge discovery and statistical data analysis integrated with
powerful 2D and 3D graphics for visualization are the key topics of this book. The
Python code examples powered by the Java platform can easily be transformed to
other programming languages, such as Java, Groovy, Ruby and BeanShell. This
book equips the reader with a
computational platform which, unlike other statistical programs, is not limited
by a single programming language.
The author
focuses on practical programming aspects and covers a broad range of topics,
from basic introduction to the Python language on the Java platform (Jython),
to descriptive statistics, symbolic calculations, neural networks, non-linear
regression analysis and many other data-mining topics. He discusses how to find
regularities in real-world data, how to classify data, and how to process data
for knowledge discoveries. The code snippets are so short that they easily fit into
single pages.
Numeric Computation and Statistical Data
Analysis on the Java Platform
is a great choice for those who want to learn how statistical
data analysis can be done using popular programming languages, who want to
integrate data analysis algorithms in full-scale applications, and deploy such
calculations on the web pages or
computational servers regardless
of
their operating system.
It is an excellent
reference for scientific computations to
solve
real-world problems using a
comprehensive stack of open-source Java
libraries included in the DataMelt (DMelt) project and
will be
appreciated by many data-analysis scientists, engineers and students
.
Series
Edition
Language
Place of publication
Publishing group
Springer International Publishing
Target group
Primary & secondary/elementary & high school
Graduate
Illustrations
92 s/w Abbildungen
XXVI, 620 p. 92 illus.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 41 mm
Weight
ISBN-13
978-3-319-28529-0 (9783319285290)
DOI
10.1007/978-3-319-28531-3
Schweitzer Classification
S. Chekanov was born in Minsk (Belarus) and received his Ph.D. in
experimental physics at Radboud University Nijmegen, The Netherlands. He has
more than twenty five years of experience in high-energy particle physics
including advanced programming and analysis of large data volumes collected by
high-energy experiments operated by major international collaborations. He has
written a book and over a hundred
professional articles, many of them based on analysis of experimental data from
large-scale international experiments, such as LEP (CERN, European Organization
for Nuclear Research), HERA (DESY, German Electron Synchrotron) and LHC, the
Large Hadron Collider experiment at CERN. Over the past decade he has divided
his time between data analysis, developing analysis tools and providing
software support for the Midwest data-analysis centre (USA) of the LHC
experiment. He is founder of the
jWork.ORG community portal for promoting
scientific computing for science and education.In 2005 he created a data-analysis
software environment, which is presently known as DMelt.
Currently, this software is the world's leading open-source program for
data analysis, statistics and scientific visualization, incorporating Java
packages from more than 100 developers around the world and with thousands of
users. Presently, he works at the Argonne National Laboratory (Chicago, USA).
Java Computational Platform.- Introduction to Jython.- Mathematical Functions.- Data Arrays.- Linear Algebra and Equations.- Symbolic Computations.- Histograms.- Scientific Visualization.- File Input and Output.- Probability and Statistics.- Linear Regression and Curve Fitting.- Data Analysis and Data Mining.- Neural Networks.- Finding Regularities and Data Classification.- Miscellaneous Topics.- Using Other Languages on the Java Platform.- Octave-style Scripting Using Java.- Index.- Index of Code Examples.
