Thursday, February 08, 2018

Why is data science sexy? via @james_aka_yale

James Le asks why is data science "sexy" in his on-line article: 16 Useful Advice for Aspiring Data Scientists? In the end he says that "sexiness comes down to being effective".  Hmmmmmm?

Jimmy Floyd Hasselbaink.
Not a Data Scientist!
Image source:
The Dutch footballer Jimmy Floyd Hasselbaink once said, in response to Alan Shearer who claimed that scoring a goal was better than sex:

"You can never say a goal is better than sex - all the guys that say that are not having proper sex."

I guess the same could be said for Data Scientists in what the Harvard Business Review calls The Sexiest Job of the 21st Century. Let's not lose the run of ourselves!

Le goes on to collate advice from 16 data scientists who responded to his question: “What advice would you give to someone starting out in data science?”. For students aspiring to become data analysts/scientists, the list makes for interesting reading. Just a selection of some interesting quotes from Le's article:

"It’s very easy to get a Wikipedia-level understanding of, say, machine learning. For actually doing it, though, you really need to know what the right tool is for the right job, and you need to have a good understanding of all the limitations of each tool."

"put effort into understanding how the data is captured, understand exactly how each data field is defined, and understand when data is missing"

"For the person who’s trying to transition like I did, I would say, for one, it’s hard. Be aware that it’s difficult to change industries and you are going to have to work hard at it."

Joke: "a data scientist is someone who knows more stats than a computer programmer and can program better than a statistician"

"learning how to do data science is like learning to ski. You have to do it. You can only listen to so many videos and watch it happen. At the end of the day, you have to get on your damn skis and go down that hill".

Eugene's advice:
  • Ask a question first
  • Answer the question by using statistics, data mining, and visualization to make sense of the data
  • Think before you plot
  • Challenge every number
  • Above all - be passionate about data!

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