Back in 2011, Brad Pitt and Jonah Hill starred in the superb and underappreciated film, Moneyball. Based on Michael Lewis’ book of the same name, Moneyball explained how the poorest team in Major League Baseball, the Oakland Athletics, used data science to create a team.
And not just any team, a team that went on a win streak of 20 games.
The method to their madness, dubbed Sabermetrics, was a unique approach to analyzing data. Sabermetrics, at its heart, was taking large data sets and deciphering meaning from them.
This is data science.
For the longest time, Sabermetrics was looked down upon by the baseball elite. After the 2005 Boston Red Sox and the 2018 Houston Astros incorporated it into their strategies, it’s now considered a legitimate method for constructing a team.
It’s not localized to the MLB though.
Industries, far and wide, are opting to delve into data analysis. Through the thoughtful interpretation of these large numbers, data scientists are helping companies run more efficiently and solve problems.
There’s more to data science than just looking at numbers.
It’s an Effective Means of Creating Positive Impact
In 2008 the housing bubble burst.
This crisis expanded out beyond those in real estate. Marketing companies to restaurants felt the pinch here. The US economy, as a whole, went into a downturn.
The Austin Technology Council, a peer group representative of technology companies in and around Austin, TX, was struggling.
In 2009 there came a moment. The board was on the fence over whether to continue their mission or to dissolve the council itself.
It was decided to put the vote on hold while the council performed an Economic Impact Study. The goal of the study was to find out if technology companies in the area were having any real effect at all. If the effect minimal, then the choice was clear.
Through surveys, research, and of course, analysis, data was collected and handed over to data scientists. The data scientists were tasked with digging through the numbers and determining which pieces were relevant and which were not.
They were crunching numbers in order to answer a question; can you measure the impact of a specific industry, like technology, in a given area.
The answer was yes.
To the tune of roughly $19,000,000,000.
That’s $19 billion.
Billion with a B.
Thanks to the hard work of these data scientists, the ATC decided to remain in business and to focus its efforts on growing the tech community in Austin.
This Economic Impact Study was part of the reason Austin has become known as the burgeoning center for technology.
If other areas, say San Antonio, was to perform similar economic impact studies, it could set off a rebranding in that area as well.
To do this, however, there is a need for data scientists who are qualified to figure out the data.
Data Science is a Reliable Field
Data science has been around for a long time.
The reason it’s growing now is that of all the ways companies can capture data. With this uptick in data acquisition, there’s more data to analyze than there are people to analyze it.
Companies will never stop gathering data. The practices and procedures may change, but there will always be data.
And where there’s data, there is a need for a data scientist.
It’s a Growing Field
Every month LinkedIn reports on the state of the US workforce.
It’s a monthly report that explains trends in hiring and retention, as well as providing the numbers to back it up.
For August 2018, the Workforce Report stated a skills gap when it came to data scientists.
In 2015, there were more data scientists available than there were jobs for them to fill. Now there’s a deficit in the United States of qualified data scientists.
A deficit of 151,717, according to LinkedIn.
Other sources state the field of data science will grow by as much as 20% by 2020.
Good news for those who are interested in collecting data.
More Industries are Getting into Data Analytics
With companies growing and striving to compete in a more digital world, data is the main tool they focus on.
This is where data scientists come in.
It doesn’t matter if the company sells shoes, baseball bats or software, there will be data to analyze. Thus, a data scientist could enter almost any industry and find a place to apply their skills.
Companies need to be able to look at all the numbers they’ve collected and make meaning out of it. Thus, the hard skills of knowing how to think critically, code, and math are a key advantage when it comes to finding a job.
Just as important as the hard skills are the soft skills.
Being able to communicate is always a deciding factor. Communicating the data in a meaningful way for those who may understand it is difficult. Those who can do it without alienating others will find their jobs secure and a certain degree of prestige.
One could argue that knowing how to analyze data is enough. But think of it this way; Steve Jobs was brilliant. He was also exceptionally hard to work with.
We don’t need another Steve Jobs.
We do need brilliant people though.
The Benefits of Data Science
Applying data science is another difficult aspect of the job. Just being able to analyze the numbers and break down what they mean is not enough.
A good data scientist applies that knowledge to solving the problem. They may even be able to spot a problem before it becomes too big of an issue.
Most of the time, however, data science is the steady application of analyzing data. Then adjusting to changes in the industry.
When its done right, and consistently maintained, amazing things can happen.
More than Just Baseball
While it’s easy to point to Sabermetrics, and Moneyball, data science has applications far beyond baseball.
And not just to the business world either.
Data scientists do the hard job of culling useful stuff from dense information.
Even here, at Bundle Your Internet, we’re analyzing data to find the best internet deals. There is someone always checking spreadsheets to make sure we’re not missing something.
It’s not easy.
If it were, then this writer would have probably gone into data science. Given my father did heavy research, which involved statistics, it wouldn’t be hard to see me going into some data-rich field.
I didn’t do well with math though.
There was always something to trip me up and I would get increasingly frustrated when it came to anything involving numbers. The issues only got worse when I got to high school. There, in the first semester of my freshman year, I flunked Algebra I.
I had to work overtime to pull out a B for the second semester and average out the year to a C.
Now I have a healthy respect for math. Better than that, I understand something about myself- I ain’t good at the maths.
When I made it to college and majored in Art, I assumed I would be free of my own nemesis. Come to find out, however, that I still had to pass one college-level math course to complete my degree.
Knowing that math was not a friend of mine, I went to the tutoring center every day to make sure I understood what I was supposed to do. And when finals came around, I studied for two days before my college algebra final.
And I passed with a B!
Now I can’t recall a single equation I learned.
I just passed it.
That’s all I care about.
So I’ll stick to my words and leave the hard math to those who are interested in such endeavors.
If math is something you enjoy, and not words, then look at data science. It’s sure to be a rewarding career.
But you do have to ask yourself, what’s better; working with words or working with numbers?