In this post we summarize some of our most recent and favorite answers on Quora to questions from the community about hiring junior data scientists, sharing work with the public, and collaborating.
“What do you look for when hiring an entry-level data scientist? Would a master’s in Data Science or a bootcamp be beneficial?”.
What’s the best way for data scientists to share their work?
Considering the deeply technical nature of the work, and the many ways in which an analysis can go awry, it can feel like an especially daunting task to share one’s work as a data scientist.
A recent article by Emma Walker, Data Scientist at Qriously, even called communication the “Critical skill” many data scientists are missing.
What are best practices for collaboration between data scientists?
Once a data scientist had their work noticed, and once they’ve been hired as a data scientist at an organization, the truism that “Data science is a team sport” will become a daily reality.
It’s exciting to discuss the latest new approach or algorithm, but there are many interesting questions beginning to come out surrounding the people, processes, and careers of data scientists.
The orginal article.
Would-be technology workers learn coding and programming skills at Galvanize, one of a number of new boot camps that help teach skills suited to the tech booming tech economy that has a particular need for data science experts.
A 2017 survey of the top jobs in America by employer ranking and assessment site Glassdoor – based on earning potential, job satisfaction and number of openings – ranked data scientist, DevOps engineer, data engineer, and analytics manager as the 1, 2, 3, and 5 top jobs.
The specific skills required for these jobs is a mix of programming languages and ability to communicate data findings in simple language.
Data scientist: Analyze raw data sets to extract learnings and insights.
Data engineer: Work closely with data architects and scientists to prepare data so it can be analyzed.
Plus, despite much talk of late about automation slowly killing traditional jobs, data science is to some degree robot-proof as it demands a human touch to creatively solve problems with the help of computers.
From a self-taught actress and a military medic to a baseball-crazed statistician and plasma physics scholar, the backgrounds of people who have found their way to data science jobs is broad. But wherever they started, they had to pick up the specific skills these jobs demand.
“These days, being good at data means you could get a job anywhere. Nordstrom, the post office, Walmart, they’ve all got analytics groups.”
Theresa Johnson, a data scientist with Airbnb, would seem like someone who could coast through any tech job.
Yet when Johnson decided she wanted to pivot to tech, she felt she “Still had a lot to learn.” So she buckled down with a few online classes with Coursera to see how her skills set would need to be adapted to data science.
The orginal article.
The results of our most recent Presidential election notwithstanding, West and Bergstrom maintain that humans are pretty good at detecting verbal bullshit.
Bullshit expressed as data, on the other hand, is relatively new outside scientific circles.
Multivariate graphs didn’t begin to appear in the popular press until the nineteen-eighties, and only in the past decade, as smartphones and other information-gathering devices have accelerated the accumulation of Big Data, have complex visualizations been routinely presented to the general public.
While data can be used to tell remarkably deep and memorable stories, Bergstrom told me, its apparent sophistication and precision can effectively disguise a great deal of bullshit.
Bergstrom believes that calling bullshit on data, big or otherwise, doesn’t require a statistics degree-only common sense and a few habits of mind.
To paraphrase the philosopher Harry Frankfurt, the liar knows the truth and leads others away from it; the bullshitter either doesn’t know the truth or doesn’t care about it, and is most interested in showing off his or her advantages.
These days, spurious correlations often emerge from data mining, the increasingly common practice of trawling large amounts of information for possible relationships.
Like all data-based claims, if an algorithm’s abilities sound too good to be true, they probably are.
Computer models designed to predict individual criminal behavior have shown bias against minorities, possibly because the data used to “Train” their algorithms reflect existing cultural biases.
Mind the Bullshit Asymmetry Principle, articulated by the Italian software developer Alberto Brandolini in 2013: the amount of energy needed to refute bullshit is an order of magnitude bigger than that needed to produce it.
The orginal article.