Machine Learning is (or should be) a core component of any marketing program now, especially in digital marketing campaigns. The following insightful quote by Dan Olley (EVP of Product Development and CTO at Elsevier) sums up the urgency and criticality of the situation: “If CIOs invested in machine learning three years ago, they would have wasted their money. But if they wait another three years, they will never catch up.” This statement also applies to CMOs.

According to a report by the Semiconductor Industry Association, we will run out of electricity to power computers by 2040 if we don’t radically improve computing efficiency. This is one of the many reasons why scientists are striving to use quantum computing to advance widely applicable algorithms, including deep learning.

There are more than 12,000 packages for R available on CRAN, and many others available on Github and elsewhere. But how can you be sure that a given R package follows best development practices for high-quality, secure software?

I just finished the implementation of another approach to finding repetitive calendar behaviour, and was quite surprised that the only short period for stocks, has just began. What are the odds of this? 🙂

Javier Benitez points us to this article from 2014 by Susan Howitt and Anna Wilson, which has subtitle, “The way textbooks and scientific research articles are being used to teach undergraduate students could convey a misleading image of scientific research,” and begins:

Zach Shahn writes:

Looking for something original to decorate your wall? Art With Code, created by Harvard University bioinformatician Jean Fan, provides a collection of R scripts to generate artistic images in the style of famous artworks, for example this randomly-generated piece in the style of Mondrian:

It’s here, and he even calls it a “bag of tricks”!

I’ve just had the privilege of giving my first keynote at EuroPython (and my second keynote this year), I’ve just spoken on “Citizen Science”. I gave a talk aimed at engineers showing examples of projects around healthcare and humanitarian topics using Python that make the world a better place. The main point was “gather your data, draw graphs, start to ask questions” – this is something that anyone can do:

Most cities in the US have a grid-based street structure. But it’s rarely a perfect grid: sometimes the vagaries of history, geography, or convenience lead to deviations from right angles. And sometimes, rival urban planners simply disagree: