Do you ever feel like terms like "big data," "data analytics," "data mining," "data optimization," etc. are bandied about to epic proportions these days? I mean, if you're in the field, you definitely notice these terms, but even your basic computer users and luddites are starting to notice these terms. Most people have a fairly decent idea of what these concepts mean.
One "data" term that seems to elude just about everyone, even those in the tech industry to a degree, is "data scientist."
Let's take a closer look together at data science and data scientists.
So, What Is Data Science?
Alejandro Correa Bahnsen, PhD gives a spot-on and completely relatable definition of data science in his Modern Data Science SlideShare presentation on LinkedIn. We very maturely laughed the laughter of recognition at Slide 10:
"Data science is like teenage sex:
Everybody talks about it,
nobody really knows how to do it,
everyone thinks everyone else is doing it,
so everyone claims they are doing it..."
It's sort of poetic, isn't it? But, like the subject itself; it isn't terribly helpful in providing a definition of data science.
It's all about the algorithms!
No, not Al Gore Rhythm.
Think about Facebook and its use of algorithms. Facebook has, over the years, amassed an amazing tonnage of data from you, your friends, your boss, etc. But what could the social media behemoth do with such a glut of random data in its raw state? Mark Zuckerberg and his team decided that they needed a data scientist to do some data science. And now, the data scientist has done a lot of data science and has applied an algorithm that helps users get the most out of their experience to limit who sees their posts and more.
So yes, a great deal of it has to do with algorithms, but there is much more to data science.
Data science is sometimes alternatively referred to as data-driven science and is an inter-disciplinary field dealing with scientific methods, processes, systems and even some machine learning to derive information and meaningful insights from large swaths of data. The data itself may come to the data scientist structured or unstructured and may come in a variety of forms.
Just What Does a Data Scientist...Do?
You probably have a pretty good idea about the basics of what a data scientist does by now, but as usual; there is more.
Data science falls under the Bureau of Labor Statistics (BLS) category of "Computer and Information Research Scientists."
Just keep in mind that not every computer and information research scientist is a data scientist:
The BLS goes on to clarify that data scientists are computer and information research scientists who write algorithms (there are those algorithms!) used to detect and analyze patterns in very large datasets.
But the data scientist's work doesn't stop there. He or she goes on to seek ways to sort, manage and display or share data. They seek ways to find meaningful uses for the data, in part or in total. But mostly in part and for specific purposes.
What Types of Companies Need Data Scientists?
Big data and all the information that comes with it, once appropriately parsed out via data science, has huge useful potential for just about any company that wants to learn more about its customers and their buying patterns. Data science also helps identify efficiencies and inefficiencies for specific processes, such as logistics.
Is "Data Scientist" Merely a Job Title or a Way of Life?
Seriously, we love data, data science and data scientists, but we really hope that everyone remembers that data science is a handy tool for understanding our copious stores of data and leave the data scientist hat at the office. However, choosing to become a data scientist definitely takes a certain set of core characteristics. Data Scope Analytics has shared some of the top characteristics needed for a happy and successful career as a data scientist:
- Strong mathematical and statistical skills
- Technical acuity aka wizardry
- Multimodal communication skills
- Patience and determination
This combination of traits is fairly specific to the data scientist, so who knows? Maybe it actually is a way of life in our bold new tech world.
Why Would Someone Become a Data Scientist?
If there is no other reason you might choose to become a data scientist, it is this one, courtesy of Forbes (the exclamation mark and emphasis are all ours, though):
IBM predicts demand for data scientists will soar 28% by 2020!
Seriously, that's what...way less than three years away. That is massive field growth in an insanely short time frame.
The pay for this intensive position measures up too, with a median annual wage of $111,840, as of May 2016. Basically, you work hard with all of your excellent math and analytical skills, and you earn accordingly! Not bad. And that's just the median.
When it all comes down, collecting data is incredibly easy to do. Figuring out what it means and how to use it...well, that's where the challenge comes in, which is why data scientists are so hot right now.
Yes, that's right: data scientists are hot. Maybe that's all anyone really needs to know!