Data science interview questions with answers • Hacker News Board

Re: Data science interview questions with answers

#31

Post by marcinzm » Sun, Sep 13, 2020, 6:20 PM UTC

data4lyfe wrote: post #24

I have to agree that these interview questions function more as a cheatsheet review than actually anything practical that would be seen in an interview. Data science interviews don't function as a biology test where you're just rattling off memorizations to how neural networks or linear models work. Ultimately these types of questions like "What is feature selection" are more likely to be encapsulated into case studi…

>I have to agree that these interview questions function more as a cheatsheet review than actually anything practical that would be seen in an interview. Data science interviews don't function as a biology test where you're just rattling off memorizations to how neural networks or linear models work.

Me and my co-workers have been asked exactly that by top companies although it was more for machine learning engineer/applied scientist positions. Many textbook questions asked one after the other. It's not the only interview type they did but it definitely mattered and was often the first filter. So if you didn't answer well enough then you were out.

Re: Data science interview questions with answers

#32

Post by y42 » Sun, Sep 13, 2020, 6:21 PM UTC

ryndbfsrw wrote: post #27

I’ve worked in the field for 7 years now so not that long but long enough to build some heuristics. The best data scientists are just people who try to understand the ins and outs of business processes and look at problems with healthy suspicion and curiosity. The ability to explain the nuances of manifolds in SVMs is not something that comes into it outside these contrived interviews. I prefer to ask candidates how…

>> I prefer to ask candidates how they would approach solving a problem

Word. Totally off topic, but: I work in the field of information technology since more than 20 years now, more or less. Not always the same focus, not always full time, but always IT related. I consider myself a good problem solver because of my self learning and analytical skills.

I recently applied for a job as a BI developer. The interview consisted of 10 questions about SQL. I more or less answered them, just 1 or 2 wrong. Not wrong as in "not correct" but rather "Not what we exactly expected" or "you did not see the little traps".

Comes out they didn't take me because of my lack of SQL skills. I do not understand how this kind of recruiting process will help anyone getting skilled people and how this is still common practice. It's frustrating for people like me, who do not have the complete SQL syntax in mind, but are flexible in choosing their problem solving approaches. A couple of years ago I started in a big data company, never heard of MongoDB before, little skills in Bash. If they would just asked me questions about that, hiring me would be a total no-go. They did hire me. I improved process, like measurable, and mastered MongoDB. Nothing, that one could expect from a questionaire.

A second interview, same outcome. They not even asked me detailled questions, just wanted to know what my SQL skills are. I answered: Immediate, but I'm good in learning. The did not take me, too.

Although, I understand that it's hard to evaluate this kind of skill, I'm really frustrated, when I face those "hiring techniques". Or maybe I'm just not good in SQL, and they anticipated it.. ;)

Re: Data science interview questions with answers

#33

Post by marcinzm » Sun, Sep 13, 2020, 6:27 PM UTC

minimaxir wrote: post #30

marcinzm wrote: post #29

I've seen multiple companies ask candidates to write working code for machine learning end-to-end from scratch. As in, write a stochastic gradient descent logistic regression model with training, inference, etc. without any libraries beyond pandas/numpy If you're lucky they'll provide you the equations or let you google them. So something to memorize including the various numpy/pandas gotchas.

The followup question would then be "do you currently write end-to-end ML code in raw Python in production?" The answer will most likely be no.

That response applies to 80% of modern interview questions at tech companies. So if you want a job there you smile, keep quiet and answer the problem. Since the job at large companies usually involves putting up with BS it's probably not a bad filter either for candidates.

Re: Data science interview questions with answers

#34

Post by disgruntledphd2 » Sun, Sep 13, 2020, 6:43 PM UTC

jointpdf wrote: post #21

fractionalhare wrote: post #12

Earlier quoted context omitted.

Lots of tech and finance companies (particularly those with standardized interview processes) will blacklist questions if they're found online. Those companies will constantly check GitHub, GeeksForGeeks and Leetcode to see if their questions are listed there with solutions. This probably won't be the case for a question as basic as, "what is regression?" But for any intermediate to advanced interview question involv…

A couple recommendations piggybacking off of yours: A First Course in Probability has a lot of problems (with solutions) and worked examples, but it’s light on intuition and pedagogy. It’s not an easy book to learn from, on its own. I highly recommend listening to Joe Blitzstein’s STAT 110 lectures and reviewing the wealth of problems/notes. The greater mastery of probability theory that you have, the easier studying…

Also, Regression and Other Stories is the new edition of the Regression with Multilevel models book, and it's much, much better (especially for n00bs).

Re: Data science interview questions with answers

#35

Post by aok1425 » Sun, Sep 13, 2020, 6:58 PM UTC

y42 wrote: post #32

ryndbfsrw wrote: post #27

I’ve worked in the field for 7 years now so not that long but long enough to build some heuristics. The best data scientists are just people who try to understand the ins and outs of business processes and look at problems with healthy suspicion and curiosity. The ability to explain the nuances of manifolds in SVMs is not something that comes into it outside these contrived interviews. I prefer to ask candidates how…

>> I prefer to ask candidates how they would approach solving a problem Word. Totally off topic, but: I work in the field of information technology since more than 20 years now, more or less. Not always the same focus, not always full time, but always IT related. I consider myself a good problem solver because of my self learning and analytical skills. I recently applied for a job as a BI developer. The interview con…

I can try to help you pass the SQL round. I’m going through DS/Analytics Eng interviews myself. I’ve passed all of my technical rounds, but failed the final rounds so far. Email me at aok1425 at gmail.

Re: Data science interview questions with answers

#36

Post by arpyzo » Sun, Sep 13, 2020, 7:07 PM UTC

y42 wrote: post #32

ryndbfsrw wrote: post #27

I’ve worked in the field for 7 years now so not that long but long enough to build some heuristics. The best data scientists are just people who try to understand the ins and outs of business processes and look at problems with healthy suspicion and curiosity. The ability to explain the nuances of manifolds in SVMs is not something that comes into it outside these contrived interviews. I prefer to ask candidates how…

>> I prefer to ask candidates how they would approach solving a problem Word. Totally off topic, but: I work in the field of information technology since more than 20 years now, more or less. Not always the same focus, not always full time, but always IT related. I consider myself a good problem solver because of my self learning and analytical skills. I recently applied for a job as a BI developer. The interview con…

I think this must happen in all computer engineering fields. It's happened to me numerous times when applying for DevOps positions.

Interviewers don't seem to realize that possessing knowledge and fluency are a trade-off. If I'm amazing at SQL, I'll have a gaps elsewhere and vice-versa. There's just too much to learn, and stay on top of.

My takeaway when I fail an interview due to nonsense like this is that these aren't places I would've been happy working at anyway so they did me a favor by not hiring me.

Re: Data science interview questions with answers

#37

Post by Jugurtha » Sun, Sep 13, 2020, 7:17 PM UTC

ryndbfsrw wrote: post #27

I’ve worked in the field for 7 years now so not that long but long enough to build some heuristics. The best data scientists are just people who try to understand the ins and outs of business processes and look at problems with healthy suspicion and curiosity. The ability to explain the nuances of manifolds in SVMs is not something that comes into it outside these contrived interviews. I prefer to ask candidates how…

Diving deep into a domain and interfacing with a subject matter expert goes a long, long, way. We've been building custom data products for enterprise for about seven years, and a lot of the work is listening and grokking large amounts of content about whatever domain we were helping with in general, and the specifics of our clients. Retail, banking, telcos, energy, communication.

Having a background in acoustics, reservoir characterization, telecom networks, opens up clients because you 'get it' or at least you work hard to get it, which improves buy-in of the experts to sit down with you and answer your questions. You did your homework.

If you don't and just storm in talking about something something neural nets, they'll see it as a waste of time, won't bother explaining nuances, will delay sending data you desperately need. You won't have their cooperation even if you have executive support. There's no data in CSV form or an API to hit in most real world projects, so you need their help getting data, and their expertise to understand it.

Another major point is specifying the metrics. The real world metrics, not AUC or F1 scores. You need collaboration to get there, too.

There's so much to be done before there's data to work with, let alone good data. And there's so much after the model building step.

It can drive people to quit. One reason is that when you storm in and consider that people are morons, you get frustrated rapidly.

Re: Data science interview questions with answers

#38

Post by tgv » Sun, Sep 13, 2020, 7:20 PM UTC

fractionalhare wrote: post #13

The quality and depth of answers here is pretty inconsistent. But this in particular is a pet peeve of mine: > Plot a histogram out of the sampled data. If you can fit the bell-shaped "normal" curve to the histogram, then the hypothesis that the underlying random variable follows the normal distribution can not be rejected. This is commonly taught in undergrad stats, but you shouldn't do this. I'm of the opinion that…

There's a test for normality. Well, I knew one, Kolmogorov, but Wikipedia already lists 8. What data scientist doesn't know of such a test?

And how would you fit the data? There's not one, unique way to fit. And as you say, a small deviation can mean a lot: if you use L2 distance, the errors at the outer regions of a normal distribution are probably dwarfed by any deviation that occurs more towards the center.

Re: Data science interview questions with answers

#39

Post by conjectures » Sun, Sep 13, 2020, 7:34 PM UTC

marcinzm wrote: post #29

I've seen multiple companies ask candidates to write working code for machine learning end-to-end from scratch. As in, write a stochastic gradient descent logistic regression model with training, inference, etc. without any libraries beyond pandas/numpy If you're lucky they'll provide you the equations or let you google them. So something to memorize including the various numpy/pandas gotchas.

That's a great question. Because it lets you as a candidate screen out places run by morons.

Re: Data science interview questions with answers

#40

Post by starpilot » Sun, Sep 13, 2020, 7:44 PM UTC

ryndbfsrw wrote: post #27

I’ve worked in the field for 7 years now so not that long but long enough to build some heuristics. The best data scientists are just people who try to understand the ins and outs of business processes and look at problems with healthy suspicion and curiosity. The ability to explain the nuances of manifolds in SVMs is not something that comes into it outside these contrived interviews. I prefer to ask candidates how…

This is why I'm switching from DS to SWE. The communication hurdles with the business people are so hard for me. I've talked with other nerds my whole life and struggle to connect with and dissect the other side. That and the pay is better.