I think the reason Scala seems surprisingly popular is that is was simple string matching so a post that said "We are looking for someone who can build scalable infrastructure" would count as one mention of "scala".
Could be wrong though.
Also see: https://news.ycombinator.com/reply?id=18725655&goto=item%3Fi...
Honestly, it is an insult to call good data preprocessing skills grunt work.
It may not be as attractive, but requires a lot of understanding of the nature of data and way in which patterns present themselves.
For what it's worth, I don't really see this as a problem in and of itself. Of course, if you want to do data science in the real world, you need to learn about data cleaning, manipulation, and warehousing in addition to the "pure" data science process that begins with tidy data. But it's good that tutorials segment these out, since not everyone needs to learn both at the same time. Anecdotally, I had plenty of real-world experience dealing with messy data by the time I started learning data science.
As an aside, my impression is that technical assessments for data science positions tend to underemphasize data cleaning and manipulation. Granted, they're still really time-consuming as it is, but there's probably some room for optimization there.
True, although ideally the data is cleaned/structured before it hits a data warehouse and the data scientist starts working with it. It's an iterative process (DS finds messy data, flags the problem upstream, repeat).
I do wish there were more tutorials with messy data; the ones I make I deliberately try to use complex datasets and highly relational ones, although as a result the tutorials are more complicated and more imposing for beginners.
> As an aside, my impression is that technical assessments for data science positions tend to underemphasize data cleaning and manipulation.
When I was interviewing for DS jobs, I got a lot of the implement-binary-search-on-a-whiteboard questions which annoyed the hell out of me. But for the takehome assignments, they required some data ETL, which I felt was more representative. (some assignments had deliberately flawed datapoints the user was expected to identify and remove while not explicitly being told to do so)
They're just calling it like it is.
(lightheartedly; I'm not like disgusted over here :-) )
Edit: trivial isn't the right word, it's obviously an important step in the process but quite different from what you are really paying a "Data Scientist/ML Engineer/etc" to do.
It's not that putting the sculptor on the task would be a waste, nearly as much as that the sculptor doesn't possess any of the necessary skills.
How many people know how to find a source of marble, run a mine, and sculpt a great work of art? Roughly nil, but it's how many organizations seem to hire.
It's literally what the phrase means.
Market research is all about getting different kinds of data and massaging it into queryable and productizable form (reports).
I would be pretty surprised to see these two so much more commonly requested than Javascript.
When can you start?
This analysis has the Austin as one of the top 10 locations, and I'm curious whether or not that is skewing the numbers. There are a steady stream of jobs actually in Austin, so either seems possible.
https://hnprofile.com/compare?search=scala,python,rust
Not the only issue I see, for instance my company posted for “machine learning engineers”, yet that isn’t in the results at all.
I think they are doing regex search in the strings, as opposed to identifying the words and doing comparisons
Because there are a multitude of possibilities.
/asp\.net|dotnet|c#|VB.net|f#|xamarin/i