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.
Do you select a bottle of wine based more on how fancy the sleeve is than its price point? If so, then you're like me, and you may be looking to minimize the risk of wishful guesses. This article may provide a little rational weight to your purchasing decisions.
Due to my research using the Wolfram Language, I can now mention the fact that if you are spending less than $40 on a random bottle of wine, you have a less than 0.1% chance of finding a 95+-rated wine. I could also perhaps reel off some flavors and characteristics of wines from Tuscany, for example—cherry, fruit, spice and tannins. My aim is to show you how I took a passing idea of mine and brought it to fruition using the Wolfram Language.
As many teachers make the transition back into classes after the holidays, quite a few have plans to update lessons to include segments that introduce data science concepts. Why, you ask?
According to a LinkedIn report published last week, the most promising job in the US in 2019 is data scientist. And if you search for the top “hard skills” needed for 2019, data science is often in the top 10.
Data science, applied computation, predictive analytics... no matter what you call it, in a nutshell it’s gathering insight from data through analysis and knowing what questions to ask to get the right answers. As technology continues to advance, the career landscape also continues to evolve with a greater emphasis on data—so data science has quickly become an essential skill that’s popping up in all sorts of careers, including engineering, business, astronomy, athletics, marketing, economics, farming, meteorology, urban planning, sociology and nursing.
So far in this series, I’ve covered the process of extracting, cleaning and structuring data from a website. So what does one do with a structured dataset? Continuing with the Election Atlas data from the previous post, this final entry will talk about how to store your scraped data permanently and deploy results to the web for universal access and sharing.
Mark Greenberg is a retired educator and contributor to the Tech-Based Teaching blog, which explores the intersections between computational thinking, edtech and learning. He recounts his experience adapting old game code using the Wolfram Language and deployment through the Wolfram Cloud.
Chicken Scratch is an academic trivia game that I originally coded about 20 years ago. At the time I was the Academic Decathlon coach of a large urban high school, and I needed a fun way for my students to remember thousands of factoids for the Academic Decathlon competitions. The game turned out to be beneficial to our team, and so popular that other teams asked to buy it from us. I refreshed the questions each year and continued holding Chicken Scratch tournaments at the next two schools I worked in.
Spikeys Everywhere We call it “Spikey”, and in my life today, it’s everywhere: It comes from a 3D object—a polyhedron that’s called a rhombic hexecontahedron: But what is its story, and how did we come to adopt it as our symbol?
Check out these fresh picks from authors utilizing the Wolfram Language! Covering a range of topics from algebraic curves to reaction kinetics to finance policy, these books are excellent additions to the extensive list of publications showing what's possible with Wolfram technologies.
Julian Francis, a longtime user of the Wolfram Language, contacted us with a potential submission for the Wolfram Neural Net Repository. The Wolfram Neural Net Repository consists of models that researchers at Wolfram have either trained in house or converted from the original code source, curated, thoroughly tested and finally have rendered the output in a very rich computable knowledge format. Julian was our very first user to go through the process of converting and testing the nets.
We thought it would be interesting to interview him on the entire process of converting the models for the repository so that he could share his experiences and future plans to inspire others.