Data Scientist Engineering Thinking

The problem

Many data scientists were engineers and many engineers were scientists. For much of the time since, say, mid-1500s. there were little difference between scientists and engineers. Galileo Galilei designed and make his own telescope, observed the sky, sketch out formula, reached his own conclusion.

But, nowadays, Scientists and Engineers are like pharmacist and anesthetist. The only common shared between them is probably they all use computers to conduct their work(one may use PC and another use Mac).

As Data Scientist, the routine work is looking for structured data source, design the right model, adapted an appropriate statistic/ML solution, and result is land on an article for publishing or PPT for presentation. However, in real life, DS need also think about “Repeatable” analysis, so that the whole team don’t have to do the “prediction work” again and again. DS team need to leverage the power of CPU automation, DS team need to spread the message to more broader audience, say, “put on mask”.

Data Scientists could benefit a bit from engineering thinking.

Major thinking difference

Daily work: Scientists need to “publishing article”, its work is more of searching for law explaining and predicting phenomena, whereas engineers design and build applications serve useful purpose, sometimes, for a long run.