Equips readers with a deillegalscriptionof the Java computational environment for data mining and knowledge discoverywhich can be used with severalillegalscripting languages, such as Python, Groovy andRuby
Provides more than 350 examplesillustrating numerical and statistical calculations using short code snippets
Discusses real-lifedata-analysis examples with visualization in 2D and 3D, including the recentHiggs discovery by CERN
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).
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 deillegalscriptive 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.
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-styleillegalscripting Using Java.- Index.- Index of Code Examples.
Numerical computation, 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 deillegalscriptive 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.
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-styleillegalscripting Using Java.- Index.- Index of Code Examples.
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).
Über den Autor
S. Chekanov was born in Minsk (Belarus) and received his Ph.D. inrnexperimental physics at Radboud University Nijmegen, The Netherlands. He hasrnmore than twenty five years of experience in high-energy particle physicsrnincluding advanced programming and analysis of large data volumes collected byrnhigh-energy experiments operated by major international collaborations. He hasrnwritten a book and over a hundredrnprofessional articles, many of them based on analysis of experimental data fromrnlarge-scale international experiments, such as LEP (CERN, European Organizationrnfor Nuclear Research), HERA (DESY, German Electron Synchrotron) and LHC, thernLarge Hadron Collider experiment at CERN. Over the past decade he has dividedrnhis time between data analysis, developing analysis tools and providingrnsoftware support for the Midwest data-analysis centre (USA) of the LHCrnexperiment. He is founder of thernjWork.ORG community portal for promotingrnscientific computing for science and education. In 2005 he created a data-analysisrnsoftware environment, which is presently known as DMelt. rnrn
Currently, this software is the world's leading open-source program forrndata analysis, statistics and scientific visualization, incorporating Javarnpackages from more than 100 developers around the world and with thousands ofrnusers. Presently, he works at the Argonne National Laboratory (Chicago, USA).
rn
Inhaltsverzeichnis
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-styleillegalscripting Using Java.- Index.- Index of Code Examples.
Klappentext
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 deillegalscriptive 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.
Equips readers with a deillegalscription
rn
of the Java computational environment for data mining and knowledge discovery
rn
which can be used with severalillegalscripting languages, such as Python, Groovy and
rn
Provides more than 350 examplesrnillustrating numerical and statistical calculations using short code snippets
rn
Discusses real-liferndata-analysis examples with visualization in 2D and 3D, including the recentrnHiggs discovery by CERN
rn
Includes supplementary material: sn.pub/extras
rn
