Submitted by lolik on Sat, 03/25/2017 - 08:18
Have you ever thought why the data scientist profession is so hot right now? You are proboably familiar with the answer. Data science is a hot commodity because of large data volumes and new computerized techniques. However, have you thought why this data value is so large now, compared to, say 2000? And this is a good question to answer. We will attempt to do this in this blog.
Submitted by Anonymous on Sat, 01/23/2016 - 14:21
According to TIOBE Index for January 2016, the Java popularity index again has reached 21%, leaving behind C++ (6%), while Python index is only 3.8%. These numbers can be different for data analysts positions, of course, where Python is likely to be more popular than Java.
Submitted by jworkorg on Sat, 11/14/2015 - 20:52
JTerm is an attempt to mimic Linux/Unix commands using 100% Java. Thus it works on Windows. This project is its infancy, but it is already very useful. For some, it may also look also more attractive than using Cygwin on Windows. First thing to notice - it is only 3M in size, a tiny program compared to the default Cygwin installation. Secondly, it has nice look and feel thanks to Nimbus L&F. In combination with JPort portable Java desktop, it may lead to interesting alternative to Cygwin.
Submitted by jworkorg on Sat, 01/17/2015 - 16:34
R-package - a software for statistical computing written in C. Script oriented. Pros: widely used, simple, extensive documentation. Cons: simpler graphics compared to competitors, no multi-threading, scripting is less powerful compare to other programming languages.
Submitted by jworkorg on Tue, 04/08/2014 - 20:17
Java can be rather fast! According to the article posted in ArXiv (http://arxiv.org/abs/1311.1229), Java is faster than C++ in reading data (to be exact, data in the ProMC format, which is based on Google's Protocol Buffers). See Table I of this article.
Submitted by jworkorg on Thu, 01/02/2014 - 10:55
We often hear the term "big data" (see "Big data" wikipedia link). Taking the path of finding cool words for description of something quite trivial (before we had "not quite big data", and in 10 years from now we will have "monstrously big data"?), how about a new term "small data"? The definition of "small data" is less ambiguous than for "big data": Data that has small enough size for human comprehension.
Submitted by jworkorg on Sat, 09/28/2013 - 20:17
jPort is finally out (and it is already release 1.4!). What is special about jPort project? jPort is a portable application launcher for Java-enabled platforms (Windows, Linux, Mac). It can launch dozens of free Java-powered programs for office, science, education, code development, entertainment and graphics. If you need a tool to organize all your applications in one place, a single menu (for example, on menuless Windows8) - this is the way to go.
Submitted by jworkorg on Sat, 04/27/2013 - 12:46
SCaVis is a successor of jHepWork. Why this change in its name? jHepWork has its origin in high-energy physics (remember, Higgs?). The "Hep" part of jHepWork abbreviates "High-Energy Physics". jHepWork is used in several HEP areas, but it is not very popular in this field since high-energy physics is almost completely based on the ROOT C++ package developed at CERN. The reason is that experimental data are written and stored in the ROOT format.
Submitted by jworkorg on Sat, 09/24/2011 - 12:42
As the name suggests, the award is now opened up to a wider range of Open Source projects. This year’s Award is our most exciting and we’re expecting up to 4 million visits during the Award period. Feel free to check out information about the Awards here. The winner will win a $2,500 prize fund, while the first runner-up will receive $1,000 and the second runner-up $500.
Submitted by jworkorg on Thu, 08/25/2011 - 21:55
There was recent discussion about Java7 performance for numerical calculations. Few people saw some 20-40% improvement for java7, compare to Java6. This simple script which can be executed in jhepwork shows no such improvement. Run this code above (here I'm using 2 cores) and see it yourself: