Not to detract from the article, but: Wow the meaning of the term "data scientist" has changed since the days of "sexiest job". From the article description:
- Rachel has a master’s degree in cell biology and now works in a research hospital doing cell assays.
- She learned a bit of R in an undergrad biostatistics course and has been through the Carpentries lesson on the Unix shell.
- Rachel is thinking about becoming a data scientist and would like to understand how data is stored and managed.
Data Scientists, back in the day, were largely people with both a fairly strong quantitative background and a strong software engineering background. The kind of people who could build a demo LSTM in an afternoon. Usually there was a bit of a trade-off between the quant/software aspects (really mathly people might be worse coders, really strong coders might need to freshen up on a few areas of mathematics), but generally they were fairly strong in each area.
In many orgs it's been reduced to "over paid data analysts" but I wouldn't even hire "Rachel" for a role like that.
- Rachel has a master’s degree in cell biology and now works in a research hospital doing cell assays.
- She learned a bit of R in an undergrad biostatistics course and has been through the Carpentries lesson on the Unix shell.
- Rachel is thinking about becoming a data scientist and would like to understand how data is stored and managed.
Data Scientists, back in the day, were largely people with both a fairly strong quantitative background and a strong software engineering background. The kind of people who could build a demo LSTM in an afternoon. Usually there was a bit of a trade-off between the quant/software aspects (really mathly people might be worse coders, really strong coders might need to freshen up on a few areas of mathematics), but generally they were fairly strong in each area.
In many orgs it's been reduced to "over paid data analysts" but I wouldn't even hire "Rachel" for a role like that.