Recently I attended the Big Data Analytics Symposium held at the University of Central Florida (UCF).  Hundreds of students attended, most enrolled in programs related to the fields of statistics, data science, and information technology.

I had the opportunity to chat with many of them briefly as they stopped by our table, wanting to learn about our company and career opportunities. Each encounter lasted only a few minutes. If I had the chance for a deeper conversation, this is the advice I would give.

Big Data is BIG

The world of data science is wide open. There is endless variety in the questions that can be answered. With the advent of big data, combined with machine learning techniques, it feels like there is no question that can’t be answered and no problem that can’t be solved.

At the symposium, Employment Technologies was one of eight Employer Sponsors. While each sponsor is involved in data analytics, we all use analytics in different ways to attack different issues.

At Employment Technologies, we use data analytics to design assessments that help companies predict which job applicants will be the most productive, the most engaged, and the least likely to leave their jobs. At Addition Financial, they wanted to know which customers were most likely to close their accounts so they could take action to save the account before the customers left.  And the Walt Disney Company? They are so active in the field of data analytics they have an entire conference of their own dedicated to data and analytics!

The point is, data science is ubiquitous and the employment settings varied.

TIP #1
With so much opportunity, it’s important to think about what kinds of problems you want to solve and what type of company you want to work for.


Failure IS an Option

Winston Churchill said, “Success is not final, failure is not fatal:  it is the courage to continue that counts.” Being in the fast-paced world of data analytics can be challenging and endlessly interesting. But no one said it would be easy.

Data can be temperamental. One day your code will work and the next day, inexplicably, it won’t run. Maybe you get some results, but what do they mean?  Or worse yet, you don’t get any results despite your best efforts.

One recent graduate from UCF’s data science program put it this way, “Failing can be very difficult if you are a perfectionist. But it is almost inevitable that, despite your best efforts, something somewhere will go wrong.”

The key, as this student learned, is to use the experience as a growth opportunity. It may sound like a platitude, but it’s completely true.

TIP #2
You can learn more from your failures than your successes.


This is probably why job interviewers sometimes ask, “What have you failed at lately?” And then they listen carefully to find out how you handled the challenge, what you learned, whether you put it behind you and, as Winston Churchill said, had the courage to continue.

Don’t Forget Your Soft Skills

When we talk about hard skills, we are talking about technical skills. In this case, we mean the ones you need to succeed in the field of predictive analytics. Of course you must have a solid understanding of data analytics procedures. You need to understand research methods and how to attack a problem. Data wrangling techniques are crucial. If you can write code and program in a variety of languages, that’s a plus.

Hard skills are the “price of admission” to landing that dream job.

Yet all these techniques will change over time. The field of predictive analytics inevitably evolves. One UCF professor told us that the curriculum they are teaching now won’t be the same as what is taught in 10 years. Think about that. In 10 years, students coming out of data analytics programs will know the latest and greatest tools and techniques – which could be quite different from those being taught today.

So how do you stay relevant and set yourself apart?  The secret is soft skills.

Employers, including Employment Technologies, want people who deliver more than technical expertise. For example, it’s crucial for a data scientist to be a good collaborator. Data-driven insights don’t happen in a vacuum. True breakthroughs usually occur when teams of people attack a problem from all different angles. It also helps to be naturally inquisitive and to have a passion and drive for finding the answers.

Being a quick learner doesn’t hurt either. Remember those future graduates who will know the latest and greatest? You’ll have to know it too in order to keep up. And having well-developed soft skills will give you a clear edge.

TIP #3
Hard skills get you in the door. Soft skills help keep you there.


To learn more about Employment Technologies’ approach to predictive analytics, click here or contact us for more information.