The Denver Post newspaper has declared that the hottest job in 2016 is data scientist, with abundant job openings and an attractive six-figure median salary. The job is described as going beyond ‘collecting and analyzing data. It’s a job for the curious, for the intuitive and for those who like to not just solve problems but figure out the problem. It’s part science, part art.” (1)
When I read these words, I laughed with realization. I’m a Fell Pony data scientist! I am perpetually curious about these ponies, I rely on my intuition in my research and writing, and I always look for ways to make constructive contributions whether or not a problem exists. I have not, however, discovered where the six-figure salary comes from. Fortunately, compensation comes in other forms.
“Data scientists not only collect and analyze data, they figure out what is important….” (2) Figuring out what is important about Fell Ponies is a fascinating journey and so far one without an end in sight. My first scientific exploration of Fell Pony data was a study of carrying capacity back in 2005. I’m currently working on another carrying capacity study. I’m also doing a private analysis of Fell Pony data for a client. I have another research project underway about influences on foal gender.
I often view my research in terms of geometry. A single piece of information is interesting, but it’s just a lonely point in the Fell Pony universe. If a second similar piece of information comes along, the topic becomes more interesting because two points define a line, and a line can be an arrow pointing to something important. If I am lucky enough to get a third related piece of information, then I conclude that I’m really onto something, because three points define a plane, an area that likely contains some truth that bears consideration.
One characteristic that discerning employers are seeking when hiring data scientists is an awareness of data context. (3) All data can be misused or misconstrued if the way that it was collected isn’t fully understood. In the pony world, show results immediately come to mind as an example. A particular pony is a champion on a particular day in a particular class in a particular grouping of ponies before a particular judge. That pony isn’t necessarily better or worse than any other pony outside that context. As a data scientist I shake my head in sorrow when I see a pony advertised as ‘supreme champion’ without the accompanying date and name of show. (I explore this topic in depth in “The Conundrum of Judging Quality” in my book about the Fell Pony breed.)
Another characteristic that employers look for is the ability to communicate findings clearly. (4) I received an email the other day from a reader of Rural Heritage magazine appreciating my way with words. “As always your articles are a joy to read, not only for their content but you put so much life in them.” I am humbled by such feedback and motivated to continue my craft.
During my schooling, I took several classes that informed my analytic and numerical skills. It wasn’t until several years later, though, that I was introduced to the difference between quantitative and qualitative research. Where quantitative research deals with numbers, qualitative research deals with stories. In the Fell Pony world, there are occasional opportunities to deal with numbers, but it is equally often the case that I am listening to stories and deriving data from them that can then hopefully progress from point to line to plane in terms of importance of the information contained therein.
Always be on the lookout for the presence of wonder – E.B. White
If data science is a job for the curious and the intuitive, I also think it’s a job for those on the lookout for the presence of wonder. In the case of ponies, the gift of a nicker, the partnership in work, the arrival of a foal – all of these and more are awe-inspiring. Being a Fell Pony data scientist is satisfying when I can put enough data points together to say something important. But a more complete satisfaction comes from seeing important data manifesting in warm, furry bodies. I never know where inspiration for the next inquiry will come from – perhaps a pony’s morning greeting or a question from a colleague – but I’ll eagerly welcome the inspiration when it arrives.
- Chuang, Tamara. “Science of Data,” The Denver Post, Sunday, January 31, 2016, Business Section K, page 1.
- Same as #1
- Chuang, page 8K.
- Same as #3.
© Jenifer Morrissey, 2016
The results of my first decade of research as a Fell Pony data scientist can be found in the book Fell Ponies: Observations on the Breed, the Breed Standard, and Breeding, available internationally on Amazon and by clicking here where royalties are higher for the author.