Get yourself trained on Data Scientist Terminology with this Online Training Data Scientist Terminology Prep.
Online Training Data Scientist Terminology Prep
Data Science is an extensive and emerging field. Currently, many people are needed for data scraping, mining, analysis, and visualization. It is a field used in politics, finance, manufacturing, and even health care (amongst others). Data Science is even important for many advanced technological breakthroughs or creating custom computational genomic algorithms. Infact, many Hedge Funds require data scientist in their skill sets for creating Qaunt Trading algorithms. Political Parties want data scientist for geopolitical forecasting. Athletes are scouted by data scientist. Data science seems to be everywhere, and it is up to you to see how much you already know.
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Investing in yourself through Learning
As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.