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A Student's Guide to Preparing for Data Science Interviews

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Re: A Student's Guide to Preparing for Data Science Interviews

#21

Post by ellisv »

booleandilemma wrote: post #8

How many data scientist jobs are actually out there? I can understand data scientist being a position at one of the big 5 tech companies, but are they really in demand elsewhere? I've never actually met someone off the internet who calls themselves a data scientist.

> I've never actually met someone off the internet who calls themselves a data scientist.

You could probably say the same about a lot of job titles. I've never meet a sanitation worker but my trash gets picked up once a week nonetheless.

Re: A Student's Guide to Preparing for Data Science Interviews

#22

Post by tegansnyder »

booleandilemma wrote: post #8

How many data scientist jobs are actually out there? I can understand data scientist being a position at one of the big 5 tech companies, but are they really in demand elsewhere? I've never actually met someone off the internet who calls themselves a data scientist.

Manufacturing, finance, areospace, automotive industry, and medical industry are a few other places hiring data scientists.

Re: A Student's Guide to Preparing for Data Science Interviews

#23

Post by spoopy01 »

bllguo wrote: post #2

Unfortunately there is very little here that isn't just general interview advice. As a new grad that went through the hunt very recently - it was a messy process. Very few places will consider you without extensive experience, or a masters/Ph.D. Of course if you're hiring people to research machine learning algorithms that's justifiable, but plenty of the responsibilities people associate with data scientists don't r…

Unfortunately most people posting data science jobs don't really have good priors on the necessary qualifications for those positions. As someone who has gotten multiple data science positions in startups and larger enterprises, I highly recommend applying to data science jobs through platforms like AngelList, which allow you to speak to employers directly and circumvent many of the bureaucratic processes that tend to discourage people from applying to jobs they are actually well qualified for.

Re: A Student's Guide to Preparing for Data Science Interviews

#24

Post by reader5000 »

booleandilemma wrote: post #8

How many data scientist jobs are actually out there? I can understand data scientist being a position at one of the big 5 tech companies, but are they really in demand elsewhere? I've never actually met someone off the internet who calls themselves a data scientist.

Technically anybody who uses Excel is a "data scientist". Just got to get the right buzzwords.

Re: A Student's Guide to Preparing for Data Science Interviews

#26

Post by mindcrime »

bllguo wrote: post #2

Unfortunately there is very little here that isn't just general interview advice. As a new grad that went through the hunt very recently - it was a messy process. Very few places will consider you without extensive experience, or a masters/Ph.D. Of course if you're hiring people to research machine learning algorithms that's justifiable, but plenty of the responsibilities people associate with data scientists don't r…

spoopy01 nailed it. Most of the people hiring data scientists don't really know what data science is, or probably even why they need it. So they inflate the hell out of the requirements to a. CYA and b. hopefully get somebody so experienced they can come in and make up for the lack of organizational understanding of data science. IOW, they want somebody who can "teach us what we don't know".

Re: A Student's Guide to Preparing for Data Science Interviews

#27

Post by reader5000 »

moab wrote: post #12
minimaxir wrote: post #7

Earlier quoted context omitted.

On the stats side, often higher-level theory questions, such as "How does the k-means algorithm work?", "How do you select the best k for k-means?", "What is the curse of dimensionality?" which again would not be things covered at a data boot camp or data science thought pieces on Medium. On the technical side, there is often more-advanced SQL (nested JOINs + PostgreSQL window functions). On the big data side, there…

I overheard some colleagues talking about a recent interview where a candidate with "stellar industry experience", i.e. Kaggle wins and previous ML experience at a valley company, who couldn't explain Bayes rule to them, let alone rederive Naive Bayes. While books like the one below are extremely theoretical, anyone interviewing for these kind of roles should spend at least a week or two just looking through this to…

This is like not hiring a [big name coding competition] winner because he didn't know radix sort.

Re: A Student's Guide to Preparing for Data Science Interviews

#29

Post by resolaibohp »

minimaxir wrote: post #19
gipp wrote: post #14

Earlier quoted context omitted.

Having never been to a data boot-camp -- what _do_ they teach there, if those wouldn't be covered?

Galvanize, the one mentioned in the article and markets the most, is vague on their curriculum but is more scikit-learn oriented. https://www.galvanize.com/san-francisco/data-science#curricu... Metis, another known bootcamp, does explicitly mention things like k-means, which is something I didn't know: https://www.thisismetis.com/data-science-bootcamps

Having spoken to several students as well as looking at their GitHub projects from the galvanize data science program, I am fairly confident they cover those stats subjects in question.

Not to say that a data science boot camp is all you would ever need to know, but I would give them a little more credit in what they teach. That galvanize link is not really their syllabus, they probably keep it vague to force you to sign up to their email list to get the real one.

Re: A Student's Guide to Preparing for Data Science Interviews

#30

Post by softawre »

mindcrime wrote: post #26
bllguo wrote: post #2

Unfortunately there is very little here that isn't just general interview advice. As a new grad that went through the hunt very recently - it was a messy process. Very few places will consider you without extensive experience, or a masters/Ph.D. Of course if you're hiring people to research machine learning algorithms that's justifiable, but plenty of the responsibilities people associate with data scientists don't r…

spoopy01 nailed it. Most of the people hiring data scientists don't really know what data science is, or probably even why they need it. So they inflate the hell out of the requirements to a. CYA and b. hopefully get somebody so experienced they can come in and make up for the lack of organizational understanding of data science. IOW, they want somebody who can "teach us what we don't know".

Teaching me what I don't know is what I want with all of my engineering hires. I want people better than me who will tell me why my architecture isn't ideal or whatever the case is.
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