According to Mark Skinner, an analyst at Roubini Global Economics,
there’s a very good reason why men manspread on public transit — and it
actually has less to do with men being rude, and more to do with the ratios of the average male body. According to Skinner, “manspreading appears to be an adaptive strategy that men employ due to innate morphological characteristics.”
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The ethics around algorithms is a topic that lives only partly in a technical realm, of course. A data scientist doesn’t have to be an expert on the social impact of algorithms; instead, she should see herself as a facilitator of ethical conversations and a translator of the resulting ethical decisions into formal code. In other words, she wouldn’t make all the ethical choices herself, but rather raise the questions with a larger and hopefully receptive group.
Because it’s Friday: The mysterious rotating woman
Here’s another wonderful illusion. In the animated GIF below, you may see the woman rotating to the right, to the left, or bouncing from the left to the right. Personally, unless I concentrate on something other than her face, I see her bouncing from left to right and back again.
This is a great example of the self-correcting nature of the scientific process. Science is done by humans, and humans make mistakes, but scientists are also skeptics, and want to make sure conclusions are accurate.
In this case, the researchers missed a step in their data processing to align genomic datasets from different sources. This kind of mistake is not surprising as the sheer volume of available genetic data continues to grow, coming from a multitude of different sources.
The error was uncovered when a second research team was surprised to hear that 6-7% of African DNA came from Europe and requested a copy of the data used by the first team. They ran similar comparisons on the various datasets, and found no significant correlation.
The original team was quick to agree they made an error, which is exactly how science is supposed to work. If nothing else, this incident highlights the incredible complexity of bioinformatic research, and the huge need for data scientists to share their data and processes so findings can be independently replicated.
The practice of data science requires skills that fall into three general areas: business acumen, computer technology/programming and statistics/math. Depending on whom you ask, the specific set of top skills varies. Dave Holtz describes the data science skills you need to get a job as a data scientist (8 Skills You Need to Be a Data Scientist). Ferris Jumah, examining LinkedIn profiles with the title “Data Scientists,” identified 10 skills (The Data Science Skills Network). BurtchWorks offers their list of skills that are critical to success in data science (9 Must-Have Skills You Need to Become a Data Scientist). RJMetrics, using LinkedIn data, identified the top 20 data science skill (The State of Data Science). For these lists, top skills reflected the frequency with which data professionals list these skills on their social media profile or simply reflect what the author thinks is a good set of skills.
Now this is an awesome visualization. Ever since my economics of race class last semester, I’ve been very interested in the intersection of economics and relationships. I’m quite curious to the kinda patterns that exist in this data. It seems from a cursory glance that our stereotypes about socioeconomics, gender and relationships are somewhat valid.
“To statisticians, the DSI [Data Science Initiative] phenomenon can seem puzzling. Statisticians see administrators touting, as new, activities that statisticians have already been pursuing daily, for their entire careers; and which were considered standard already when those statisticians were back in graduate school.”