Numeric Computation and Statistical Data Analysis on the Java Platform, Softcover reprint of the original 1st ed. 2016 Advanced Information and Knowledge Processing Series

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 regardlessof 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.

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.

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).

Equips readers with a description of the Java computational environment for data mining and knowledge discovery which can be used with several scripting languages, such as Python, Groovy and Provides more than 350 examples illustrating numerical and statistical calculations using short code snippets Discusses real-life data-analysis examples with visualization in 2D and 3D, including the recent Higgs discovery by CERN Includes supplementary material: sn.pub/extras