I'm highlighting each day someone in the enterprise tech community that I think you might be interested in knowing. To keep up with all of my picks, subscribe to my twice-monthly newsletter using the form on the right side of this page.
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Today's ITUnity Champ:
In thinking about who constitutes “the community” for us at Petri.com, we wrestled briefly with whether or not to include in that group those working for the top-tier tech companies. And we’ve made an executive decision: Vendor execs are people, too.
Urbonas, who was head of analytics at Western Union before joining Amazon in 2016, is very focused on the data science field. That’s an area that will become increasingly important to IT pros and other enterprise tech specialists in the coming years. (Microsoft launched a year ago an online certification program for data scientists.)
Urbonas is writing about the skills and approach that will help make those interested in data science successful practitioners. Check out one of his most recent posts for some good advice:
How to think like a data scientist to become one
We have all read the punchlines – data scientist is the sexiest job, there’s not enough of them and the salaries are very high. The role has been sold so well that the number of data science courses and college programs are growing like crazy. After my previous blog post I have received questions from people asking how to become a data scientist – which courses are the best, what steps to take, what is the fastest way to land a data science job?
I tried to really think it through and I reflected on my personal experience – how did I get here? How did I become a data scientist? Am I a data scientist? My experience has been very mixed – I have started out as a securities analyst in an investment house using mainly Excel then slowly shifted towards business intelligence in the banking industry and then in consulting, eventually doing the actual so-called “data science” – building predictive models, working with Big Data, crunching tons of numbers and writing code to do data analysis and machine learning – though in the earlier days it was called “data mining.”
Read the rest of his post here.