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Big Data Tech & Work

How data scientists can improve their careers in 2018

Data scientists will see positive results from making these professional and personal resolutions, according to John Weathington.

Video: How to tell the difference between AI, machine learning, and deep learning

It's that time of year when everyone is thinking about setting resolutions. Some people may try to lose weight, while others might want to learn a new language, but data scientists have a unique personality that I think I understand.

Don't get me wrong--there's nothing wrong with losing weight or learning a new language; however, after working with data scientists for decades and being one myself, I've noticed a few things that might set us apart from others. Let's face it: How many people actually enjoy staring at code for hours because they're convinced they can get a query to run faster?

In 2018, I encourage you to consider making these professional and personal resolutions.

SEE: IT jobs 2018: Hiring priorities, growth areas, and strategies to fill open roles (Tech Pro Research)

A professional resolution: Learn more about data science

Data science is a multidisciplinary practice that involves computer programming, advanced mathematics, artificial intelligence, data visualization, database administration, data warehousing, and business intelligence. If you're an expert in only one or two of these subjects, it's a good time to expand your knowledge base.

Many data scientists (including myself) emerged from the world of business intelligence and data warehousing; in the 1990s, we were doing what many data scientists are, at least in part, doing today. As skilled and knowledgeable as we were about data warehousing, I doubt many people doing that work knew anything about artificial intelligence.

If this sounds similar, take time in 2018 to master machine learning, neural networks, genetic algorithms, expert systems, and all the wonderful techniques that will eventually teach computers how to take over the world (don't worry, I don't think that will happen for a few centuries).

SEE: The great data science hope: Machine learning can cure your terrible data hygiene (ZDNet)

Conversely, a number of data scientists entered the profession from the artificial intelligence and/or advanced mathematics world--it seemed to be a logical progression. These professionals felt they had the hard part figured out, and now it was only a matter of learning about databases. The reality is becoming a data professional is not as easy as it looks. So, when faced with the frustrations of long-running queries and outer joins gone wild, most data scientists revert back to their comfort zone of Bayesian data analysis and stochastic calculus. If this is you, take the leap into the data world and make a commitment to learn about data munging (it's a technical term)--the good, the bad, and the ugly. It's frustrating at times, but it's very rewarding when mastered.

A personal resolution: Get it done now!

For years, I categorically dismissed the notion of rushing through an activity without thinking it through, and anyone who pestered me into moving faster than I felt was right was met with gnashing of teeth and a furrowed brow.

There's great value in thinking things through. We've all heard the horror stories of buggy code that got into production because someone carelessly missed a comma or a semicolon. When we build code, we take our time; we review our code carefully, and then we ask our peers to review our code. We build and run unit tests, system tests, functional tests, regression tests, and user acceptance tests before any end user touches our system.

But not all things work like data science. There is a cost for thinking things through--time. Sometimes the trade-off makes sense, though I bet that you tend to overthink too many things.

SEE: How to build a successful data scientist career (free PDF) (TechRepublic)

In 2018, practice acting without thinking when the risk is low--you'll find this is the case with most activities. When you get an email, read it and respond to it without taking too long to formulate a reply. By taking quick action, you'll have more time to think about data science.


As a data scientist, I'm sure you have a passionate interest in learning; that said, data scientists play it safe sometimes and stick to what we know because it's easy. For 2018, resolve to make a change.

On the professional side, expand your knowledge about data science. If you're an expert in databases, then learn more about artificial intelligence; if you're a wiz at mathematics, take the plunge with data warehousing. And most of us can benefit from a class or two in data visualization.

On the personal side, make a commitment to get things done quickly. It may be hard at first due to your natural tendency to think everything through, but make a point to act quickly whenever you can. You'll explore a different side of life--one where things move fast and the rewards are bountiful.

Resolutions have the notorious reputation for taking on a stateless characteristic: By February, there's no memory of what was committed to in January. Don't let this happen to you--resolve that, before 2019, you will be a fast-acting, well-rounded data scientist. This time next year, you'll be glad you set this goal.

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