If you can read this you can be a data scientist!

 

The opening lines of Virgil’s Aeneid. Turns out it’s a stepping stone to learning de scientia data sit amet.

As a humanities guy with strong technical and quantitative interests, I’ve watched the explosion of data science* as a component of business, education, culture and career. It is a data age.

 

Found a first-person account of a Classics grad student turned data scientist; interesting take in particular that there are some commonalities that are not necessary top of mind.

“It was true that I needed to know statistics and how to write code to function effectively in these roles, but that knowledge was a given. It turned out that the differentiating points between a great data scientist and an average one were in the researcher’s ability to deal with that same uncertainty that had driven me from the humanities and into quantitative research in the first place. In other words, the scientific methodologies had all the same epistemological concerns and issues as the humanities — they just tackled those problems with different tools.

My experience has lead me to believe that graduate humanities work is in fact one of the most useful backgrounds for an industry data scientist. While there’s often a lot of focus on data scientists being experts in statistics or coding, these tools are simply a means to an end — they’re necessary but insufficient for doing great data science. If you’re a humanities graduate student and are interested in data, I’d feel confident in your ability to succeed in the field based on your less technical skills. Specifically, experience as a graduate researcher in humanities makes you an expert in:

    1. Going deep into topics and teaching yourself anything
    2. Stating research questions and supporting your answers with evidence
    3. Communicating the limitations and assumptions of your approach

    In my mind, these broad research skills are more valuable (and rare) than knowledge of the specifics of any particular quantitative methodology.

*”Data scientist is just a sexed up word for statistician.’ Nate Silver

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Big Data Comes to Design

Viegas-UserActivityonWikipedia

You may not be interested in big data and machine learning, but its interested in you. The latest field that they have gotten their grubby little suckers on is web design. To wit, here’s a blog post from Elegant Themes,  WordPress theme shop, whose excellent theme Divi I like a lot. Data is the #1 item.

 

https://www.elegantthemes.com/blog/resources/7-tech-trends-in-2016-that-web-designers-need-to-understand-and-why

7 Tech Trends in 2016 That Web Designers Need to Understand (And Why)

#1  Machine Learning

Companies nowadays must deal with enormous amounts of data on a daily basis (have you heard of ‘Big Data’?), which makes machine learning applications for analyzing this information incredibly attractive and applicable to all fields.

When it comes to design, this information could be used to determine what customers would respond better to. Ad companies have been doing this for a long time and at this point, they probably know us better than we do ourselves, thanks to information collected from all over our web travels. In the near future, though, you and your buddies might be seeing many different designs when accessing the same pages simultaneously, thanks to a team of designers who worked hard to make something unique for all potential ‘targets’.

No doubt there are lots of entrepreneurs working on plug-ins that do  user testing and personalization. If only they can do the writing too…

Data, Data Everywhere…

smith_corona
The news biz how it was…words.

…but any room to think?

 

Big data has come to the newspaper biz in a big way. London’s Guardian has a data leaderboard in their newsroom with real time metrics for how stories are “performing” but the Financial Times, being the overachievers they are, have a whole integrated data enterprise that is embedded in their news operation.

Digiday has the story. In the excerpt below, the Betts in the quote is Tom Betts, the FT’s chief data officer.

“Tech companies don’t have chief data officers.”
Betts’ appointment also marks the publisher’s evolution to decentralize its analysts. Before last year, engineers and analysts were separate from the rest of the organization. Now, data analysts are embedded in marketing and editorial.

The audience engagement team sits in the newsroom so it can work directly with journalists. It includes data analysts, SEO experts, engagement strategists, social media managers and journalists. Its objectives are to get the FT journalism out to more people and evolve the newsroom with digital readers in mind.

“An analytically mature business is where the vast majority of analysts sit within the other teams,” Betts said. Tech organizations, he added, “don’t have chief data officers.”

Adding_Machine
The news biz as it is now: numbers.

It goes down so reasonably that you are almost lulled into forgetting to ask what SEO, engagement strategy, social and media actually have to do with journalism. One of these things is not like the other. Still, the FT manages to remain pretty newspapery, certainly compared to many other papers, which seem to be lame print versions of their lame websites.

 

Now I’m off to check my metrics!