Monthly Archives: May 2016

Visualization libraries in Jupyter, Python, & R

I’ve become a near-rabid fan of the Jupyter data analysis environment (hello Scott!), and I am deeply impressed by the work that Continuum (and some of my former colleagues at Google) have put into supporting it.  (I share some of these … Continue reading

Posted in data science, statistics, tech, work | Leave a comment

There’s a fairly tidy — but imperfect — correspondence between the three wh’s and the relational skillsets I proposed yesterday. how corresponds well to the tooling skillset what roughly corresponds to the data stewardship skillset … leaving why to correspond to the collaboration skillset, … Continue reading

Posted on by Jeremy | 1 Comment

Relational data science skills

Here’s what I see as ideal “data science” leadership. This post is a nod to the classic Conway Venn Diagram, but more focused on relational skills rather than the specific individual output (much as Tunkelang suggests here). Tooling skills Here, it’s most helpful to … Continue reading

Posted in data science, Patterns, programming, statistics, work | 2 Comments

Rolling the dice at the Just World Casino

tl;dr: The tech frame of “lean startup”, venture capital funding, “exit strategies”, and relentless “valuation” talk is fundamentally anti-human for nearly all of us. [ETA (immediately after publication):] Startup idea: They are treated like bees; they are robbed of the … Continue reading

Posted in politics, programming, tech | 4 Comments

Three wh-‘s of data science

“Big data” bandwagoneers may remember the three Vs of big data: volume, variety, and velocity (sometimes joined by veracity or variability[0]).  These concerns are real, though (if you’re not Google, Amazon or the NSA), your data is probably not as big as you think … Continue reading

Posted in Patterns, programming, statistics, work | 2 Comments