How data scientists can improve their careers in 2018
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.
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).
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.
