What Makes A Data Scientist?
Advances in technology have disrupted nearly every industry and created career opportunities that were once implausible. So it should come as no surprise that nearly half of the 25 "Best Jobs in America" according to Glassdoor are tech-related.
What may be surprising, however, is that in 2016, “data scientist” came in at the top of the list.
Simply put, data scientists are big data wranglers. They explore and analyze datasets in order to understand and organize data, identify underlying patterns and trends, and develop methods which identify how to best extract and summarize information from the data that can be used to inform better decision making.
A McKinsey study predicts that by 2018 the number of data science jobs in the United States alone will exceed 490,000. However, despite demand, there will be fewer than 200,000 available data scientists to fill these positions. Globally, this demand is projected to exceed supply by more than 50 percent in the next two years.
It All Starts with Math
A career in data science begins not only with a love for mathematics, but also with a knack for applying mathematical concepts to topics from other aspects of life both academically and in general.
Traditionally, school curriculums do not emphasize many quantitative toolsets required for analyzing and manipulating large volumes of data such as statistics, matrix algebra, and hands-on exercises geared at translating these methods into numerical algorithms. While this is starting to change as more emphasis is placed on STEM education (science, technology, engineering and math), middle and high school mathematics curriculums tend to still primarily focus on preparing students for calculus.
However, other analytical toolsets such as statistics and discrete math offer critical and different ways of thinking that is key to data science.
To bring it to a consumer level, fitness trackers are a perfect example of disorganized data. When you enter information into a fitness tracker, you tend to do lazy things.
For example, after you ride a bike or go for a run, you may input the distance you traveled; however, there is so much additional information that could have also been added.
- How many minutes did you exercise?
- Did you ride a road bike, a mountain bike or a beach cruiser?
- Did you run on a treadmill or a trail?
- At what resistance or pace did you ride?
- What about your age, weight and activity level?
- All of these factors help improve the data quality and inform a more complete story about your fitness and health.
When it comes to enterprise-level initiatives, data science teams tackle the challenge of identifying and developing ways to produce measureable outputs of value from data of variable quality originating from disparate sources. Decision makers want to see summary numbers presented in an informative and consumable way. In the desire to see whole numbers, users do not always understand the importance of also looking at the statistical certainty around data measurements.
Every organization’s data might start “messy,” but it all holds valuable insights that can affect the bottom line. Data scientists can help organizations transform the data being collected in ways that will ultimately help achieve business objectives.
Opening the Door for Data Scientists
In a turbulent energy market, identifying efficiencies and realizing cost savings from data is critical for many of these businesses to stay afloat. But this is just in one sector – many other organizations have identified the need for a data science team, though few have thus far been able to fill these types of roles.
In order to effectively build a talent pipeline for data scientists, there needs to be more of a focus on teaching quantitative skills beyond calculus prep in a mathematics education. There must be increased awareness at the high school and college levels of what skill sets are in demand so programs may be tailored accordingly.
Every year the number of opportunities for this skill set grows, and the need for data scientists at a range of companies has never been greater.
Beyond math skills, prospective data scientists need to know how to think creatively and develop context and a story for the data they are analyzing. Data scientists need to be talented with numbers, but they must also excel at problem solving leveraging various types of data.
The art of taking qualitative phenomenon and quantifying it in a meaningful way is a difficult challenge largely due to the fact it is an open-ended task and not straightforward like a number crunching process. However, everything can be modeled into a mathematical story, and having the ability to look at data sets and develop strategic insights from a business mindset is what makes data scientist so valuable.
