The thing is : Data Science requires ... "scientific" rigor and thought process. A lot of people who hire often forget that science is integral to data science: it's right there in the name.
A Student's Guide to Preparing for Data Science Interviews
41–50 of 61 posts
Re: A Student's Guide to Preparing for Data Science Interviews
#42Earlier quoted context omitted.
This is like not hiring a [big name coding competition] winner because he didn't know radix sort.
I think it's more like not hiring a big name coding competition winner because they never bothered to learn how to use version control, or any coding best practice, or any language other than C. Trying to do data science with zero knowledge of the fundamentals of probability is dangerous . Bayes rule isn't some kind of deep magic, it's covered within the first few lectures of an undergraduate probability course and i…
>I think it's more like not hiring a big name coding competition winner because they never bothered to learn how to use version control, or any coding best practice, or any language other than C.
Depends on what you're hiring for, but I'll take "competition winner with no version control" over "average programmer with expert VC capabilities".
>Bayes rule isn't some kind of deep magic
Yes, it's largely conceptually obsolete.
The people jamming out weekly SOTA machine learning models on arxiv aren't sitting around meditating on conditional probabilities. They're making little tweaks to giant models that are basically impossible for a human to comprehend.
Re: A Student's Guide to Preparing for Data Science Interviews
#43Earlier quoted context omitted.
I have one notable observation: On Hacker News, every time an interview thread pops up, there is a discussion decrying the use of technical screenings before an onsite, and often suggest practical work experience instead using a homework assignment (which this article does not discuss). Most of the companies I've talked with for data analyst/science roles have given me both a homework assignment and a technical scree…
Did you find another job yet? If not, what type of opportunity are you looking for? Data science is also a wide, somewhat poorly defined domain.
Still looking. Mostly for any role with a data analyst/data scientist title (i.e. I am not applying for the machine learning/NLP roles which require a PhD and the authorship of several papers since there is no point).
Re: A Student's Guide to Preparing for Data Science Interviews
#44Earlier quoted context omitted.
I think it's more like not hiring a big name coding competition winner because they never bothered to learn how to use version control, or any coding best practice, or any language other than C. Trying to do data science with zero knowledge of the fundamentals of probability is dangerous . Bayes rule isn't some kind of deep magic, it's covered within the first few lectures of an undergraduate probability course and i…
>I think it's more like not hiring a big name coding competition winner because they never bothered to learn how to use version control, or any coding best practice, or any language other than C. Depends on what you're hiring for, but I'll take "competition winner with no version control" over "average programmer with expert VC capabilities". >Bayes rule isn't some kind of deep magic Yes, it's largely conceptually ob…
> Yes, it's largely conceptually obsolete.
Wow
Re: A Student's Guide to Preparing for Data Science Interviews
#45The thing is : Data Science requires ... "scientific" rigor and thought process. A lot of people who hire often forget that science is integral to data science: it's right there in the name.
I think this was posted earlier. But some companies really just want a statistician.
Very few companies are actually using their data scientists as scientists. From my experience.Except for when I worked at a large hospital. We had a research board, and had to be certified to study Humans CITI. But beyond that..
Re: A Student's Guide to Preparing for Data Science Interviews
#46Earlier quoted context omitted.
What kinds of technical questions do they ask in a data science interview?
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'm not sure which bootcamps you're referring to, but I went to one that went into the topics from your first sentence in depth. The topics from your second sentence were not as thoroughly explored, though.
Re: A Student's Guide to Preparing for Data Science Interviews
#47Earlier quoted context omitted.
I think it's more like not hiring a big name coding competition winner because they never bothered to learn how to use version control, or any coding best practice, or any language other than C. Trying to do data science with zero knowledge of the fundamentals of probability is dangerous . Bayes rule isn't some kind of deep magic, it's covered within the first few lectures of an undergraduate probability course and i…
>I think it's more like not hiring a big name coding competition winner because they never bothered to learn how to use version control, or any coding best practice, or any language other than C. Depends on what you're hiring for, but I'll take "competition winner with no version control" over "average programmer with expert VC capabilities". >Bayes rule isn't some kind of deep magic Yes, it's largely conceptually ob…
I'm not sure I agree with that. I don't know any ML researchers that don't know about probability, but maybe they exist somewhere. Machine learning research isn't a good model for "data science" writ large.
Maybe there are some jobs and some problem spaces where you can just tweak big black box models and you don't ever need to think about what their output means. But if you're the kind of data scientist who helps make decisions with data -- you better believe statistics and probability is conceptually relevant. As soon as models meet the real world, you've got to understand probability in order to know what to expect.
Re: A Student's Guide to Preparing for Data Science Interviews
#48Earlier quoted context omitted.
I think it's more like not hiring a big name coding competition winner because they never bothered to learn how to use version control, or any coding best practice, or any language other than C. Trying to do data science with zero knowledge of the fundamentals of probability is dangerous . Bayes rule isn't some kind of deep magic, it's covered within the first few lectures of an undergraduate probability course and i…
>I think it's more like not hiring a big name coding competition winner because they never bothered to learn how to use version control, or any coding best practice, or any language other than C. Depends on what you're hiring for, but I'll take "competition winner with no version control" over "average programmer with expert VC capabilities". >Bayes rule isn't some kind of deep magic Yes, it's largely conceptually ob…
> Yes, it's largely conceptually obsolete.
I'm sorry, what? How did you arrive at a point where you believe this is true? This is like calling compilers "obsolete."
Is it because you believe deep learning has "taken over" or something?
Re: A Student's Guide to Preparing for Data Science Interviews
#49Earlier quoted context omitted.
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.
Yes, but I'd argue that's on a different level from "teach us why we need an 'architecture'" or "teach us how to use this 'data science' stuff". Some people are trying to be buzzword compliant when they don't actually understand the buzzwords. Ya know? Or maybe there's a better explanation for asking for a candidate with a Ph.D. in Statistics to create a linear regression model in Excel. Because truth is, for many co…
a Ph.D. in Statistics to create a linear regression model in Excel
Linear regression, logistic regression and k-means clustering, if you can get a project into actual real-money production on one of those, you are already well ahead of 90% of data scientists. And these techniques are decades old!
Re: A Student's Guide to Preparing for Data Science Interviews
#50The thing is : Data Science requires ... "scientific" rigor and thought process. A lot of people who hire often forget that science is integral to data science: it's right there in the name.
I think this was posted earlier. But some companies really just want a statistician. Very few companies are actually using their data scientists as scientists. From my experience.Except for when I worked at a large hospital. We had a research board, and had to be certified to study Humans CITI. But beyond that..
But some companies really just want a statistician
In what way is what statisticians do not "scientific"? Setting up and rejecting (or not) the null hypothesis is the very definition of the scientific process...