ENTRIES TAGGED "math"

MathML Forges On

The standard for mathematical content in publishing work flows, technical writing, and math software

20 years into the web, math and science are still second class citizens on the web. While MathML is part of HTML 5, its adoption has seen ups and downs but if you look closely you can see there is more light than shadow and a great opportunity to revolutionize educational, scientific and technical communication.

Printer in 1568-ce

Somebody once compared the first 20 years of the web to the first 100 years of the printing press. It has become my favorite perspective when thinking about web standards, the web platform and in particular browser development. 100 years after Gutenberg the novel had yet to be invented, typesetting quality was crude at best and the main products were illegally copied pamphlets. Still, the printing press had revolutionized communication and enabled social change on a massive scale.

DE-Zeitungsrollenoffsetdruck by Steschke

In the near future, all our current web technology will look like Gutenberg’s original press sitting next to an offset digital printing machine.

With faster and faster release cycles it is sometimes hard to keep in mind what is important in the long run—enabling and revolutionizing human communication.

Since I joined the MathJax team in 2012, I have gained many new perspectives on MathML, the web standard for display of mathematical content, and its role in making scientific content a first class citizen on the web. But it is rather useless to talk about MathML’s potential without knowing about the state of MathML on the web. So let’s tackle that in this post.

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Think About Learning Bayes Using Python

An interview with Allen Downey, the author of Think Bayes

Allen Downey

Allen Downey

When Mike first discussed Allen Downey’s Think Bayes book project with me, I remember nodding a lot. As the data editor, I spend a lot of time thinking about the different people within our Strata audience and how we can provide what I refer to “bridge resources”. We need to know and understand the environments that our users are the most comfortable in and provide them with the appropriate bridges in order to learn a new technique, language, tool, or …even math.  I’ve also been very clear that almost everyone will need to improve their math skills should they decide to pursue a career in data science. So when Mike mentioned that Allen’s approach was to teach math not using math…but using Python, I immediately indicated my support for the project. Once the book was written, I contacted Allen about an interview and he graciously took some time away from the start of the semester to answer a few questions about his approach, teaching, and writing.

How did the “Think” series come about? What led you to start the series?

Allen Downey: A lot of it comes from my experience teaching at Olin College. All of our students take a basic programming class in the first semester, and I discovered that I could use their programming skills as a pedagogic wedge. What I mean is if you know how to program, you can use that skill to learn everything else.

I started with Think Stats because statistics is an area that has really suffered from the mathematical approach. At a lot of colleges, students take a mathematical statistics class that really doesn’t prepare them to work with real data. By taking a computational approach I was able to explain things more clearly (at least I think so). And more importantly, the computational approach lets students dive in and work with real data right away.

At this point there are four books in the series and I’m working on the fifth. Think Python covers Python programming–it’s the prerequisite for all the other books. But once you’ve got basic Python skills, you can read the others in any order.

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