Numeric Computation and Statistical Data Analysis on the Java Platform - Rilegato

Chekanov, Sergei V.

Rilegato

ISBN 10: 3319285297 ISBN 13: 9783319285290

Casa editrice: Springer-Nature New York Inc, 2016

Riassunto

Numericalcomputation, knowledge discovery and statistical data analysis integrated withpowerful 2D and 3D graphics for visualization are the key topics of this book. ThePython code examples powered by the Java platform can easily be transformed toother programming languages, such as Java, Groovy, Ruby and BeanShell. Thisbook equips the reader with acomputational platform which, unlike other statistical programs, is not limitedby a single programming language.

The authorfocuses 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-linearregression analysis and many other data-mining topics. He discusses how to findregularities in real-world data, how to classify data, and how to process datafor knowledge discoveries. The code snippets are so short that they easily fit intosingle pages.

Numeric Computation and Statistical DataAnalysis on the Java Platform is a great choice for those who want to learn how statisticaldata analysis can be done using popular programming languages, who want tointegrate data analysis algorithms in full-scale applications, and deploy suchcalculations on the web pages or computational servers regardlessof their operating system. It is an excellent reference for scientific computations to solvereal-world problems using a comprehensive stack of open-source Javalibraries included in the DataMelt (DMelt) project and will beappreciated by many data-analysis scientists, engineers and students.

Su questo libro

Informazioni sull?autore

S. Chekanov was born in Minsk (Belarus) and received his Ph.D. inexperimental physics at Radboud University Nijmegen, The Netherlands. He hasmore than twenty five years of experience in high-energy particle physicsincluding advanced programming and analysis of large data volumes collected byhigh-energy experiments operated by major international collaborations. He haswritten  a book and over a hundredprofessional articles, many of them based on analysis of experimental data fromlarge-scale international experiments, such as LEP (CERN, European Organizationfor Nuclear Research), HERA (DESY, German Electron Synchrotron) and LHC, theLarge Hadron Collider experiment at CERN. Over the past decade he has dividedhis time between data analysis, developing analysis tools and providingsoftware support for the Midwest data-analysis centre (USA) of the LHCexperiment.  He is founder of thejWork.ORG community portal for promotingscientific computing for science and education. In 2005 he created a data-analysissoftware environment, which is presently known as DMelt.

Currently, this software is the world's leading open-source program fordata analysis, statistics and scientific visualization, incorporating Javapackages from more than 100 developers around the world and with thousands ofusers. Presently, he works at the Argonne National Laboratory (Chicago, USA).

The authorfocuses 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-linearregression analysis and many other data-mining topics. He discusses how to findregularities in real-world data, how to classify data, and how to process datafor knowledge discoveries. The code snippets are so short that they easily fit intosingle pages.

Numeric Computation and Statistical DataAnalysis on the Java Platform is a great choice for those who want to learn how statisticaldata analysis can be done using popular programming languages, who want tointegrate data analysis algorithms in full-scale applications, and deploy suchcalculations on the web pages or computational servers regardlessof their operating system. It is an excellent reference for scientific computations to solvereal-world problems using a comprehensive stack of open-source Javalibraries included in the DataMelt (DMelt) project and will beappreciated by many data-analysis scientists, engineers and students.

Dettagli dell'edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 3319285297
• ISBN 13: 9783319285290
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 620
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania