News, Views & Insights
Today we celebrate the arrival of the 36th (x.x) version of the Wolfram Language and Mathematica: Version 14.1. We’ve been doing this since 1986: continually inventing new ideas and implementing them in our larger and larger tower of technology. And it’s always very satisfying to be able to deliver our latest achievements to the world.
Today we celebrate a new waypoint on our journey of nearly four decades with the release of Version 14.0 of Wolfram Language and Mathematica. Over the two years since we released Version 13.0 we’ve been steadily delivering the fruits of our research and development in .1 releases every six months. Today we’re aggregating these—and more—into Version 14.0.
Having a really broad toolset and an open mind on how to approach data can lead to interesting insights that are missed when data is looked at only through the lens of statistics or machine learning. It’s something we at Wolfram Research call multiparadigm data science, which I use here for a small excursion through calculus, graph theory, signal processing, optimization and statistics to gain some interesting insights into the engineering of supersonic cars.
In my previous post, I demonstrated the first step of a multiparadigm data science workflow: extracting data. Now it's time to take a closer look at how the Wolfram Language can help make sense of that data by cleaning it, sorting it and structuring it for your workflow. I'll discuss key Wolfram Language functions for making imported data easier to browse, query and compute with, as well as share some strategies for automating the process of importing and structuring data. Throughout this post, I'll refer to the US Election Atlas website, which contains tables of US presidential election results for given years:
Code for America’s National Day of Civic Hacking is coming up on August 11, 2018, which presents a nice opportunity for individuals and teams of all skill levels to participate in the Safe Drinking Water Data Challenge—a program Wolfram is supporting through free access to Wolfram|One and by hosting relevant structured datasets in the Wolfram Data Repository.
According to the state of California, some 200,000 residents of the state have unsafe drinking water coming out of their taps. While the Safe Drinking Water Data Challenge focuses on California, data science solutions could have impacts and applications for providing greater access to potable water in other areas with similar problems.
The goal of this post is to show how Wolfram technologies make it easy to grab data and ask questions of it, so we’ll be taking a multiparadigm approach and allowing our analysis to be driven by those questions in an exploratory analysis, a way to quickly get familiar with the data.The Mathematics Genealogy Project (MGP) is a project dedicated to the compilation of information about all mathematicians of the world, storing this information in a database and exposing it via a web-based search interface. The MGP database contains more than 230,000 mathematicians as of July 2018, and has continued to grow roughly linearly in size since its inception in 1997.
In order to make this data more accessible and easily computable, we created an internal version of the MGP data using the Wolfram Language’s entity framework. Using this dataset within the Wolfram Language allows one to easily make computations and visualizations that provide interesting and sometimes unexpected insights into mathematicians and their works. Note that for the time being, these entities are defined only in our private dataset and so are not (yet) available for general use.It’s not really late summer unless you’re armed with an apple and a good book. There’s been a recent slew of incredible books utilizing the capabilities of the Wolfram Language that make sure your coding knowledge never stops growing and your reading list stays stocked. (And be sure to check the farmers’ market for those apples.)