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Data science interview questions with answers

242 points by fagnerbrack 6y ago

kkoncevicius 6y ago
I only read the first question in theory.md and think the answer is quite weak. > What is regression? Which models can you use to solve a regression problem? The current answer only list some names that have "regression" in it, and the description of what a regression is doesn't say anything that distinguishes it from classification. It fails to mention that regression (in ML terminology) is prediction of a continuous variable. And that almost any method can be used to do regression: knn, neural network, random forest, svm. If other answers are of similar nature you might fail the interview.
- dgellow 6y ago
  From the README: > The answers are given by the community > If you know how to answer a question — please create a PR with the answer > If there's already an answer, but you can improve it — please create a PR with improvement suggestion > If you see a mistake — please create a PR with a fix
  - kristjansson 6y ago
    I don’t like this response. It’s completely reasonable to assess the quality of informational content without assuming a duty to proofread and revise that content. Especially when the issue that might be corrected isn’t a typo or malapropism but ‘this whole thing isn’t that great.’
    - dgellow 6y ago
      Sure, I’m not saying that you have to correct it. Just that the project doesn’t assume that they have correct answers for everything and expect some participation of “the community” at large.
- atty 6y ago
  I read two answers from technical.md, and one of them was inconsistent with what was asked for... I would be very suspicious of using this repo for studying.
  - pw6hv 6y ago
    Could you elaborate on which one in particular? I've also read the first questions but haven't noticed anything like this.
    - atty 6y ago
      Sorry, I should have put it in my original reply. If you search for where it asks for an implementation of the standard deviation, you'll see that it asks for an implementation including Bessel's correction (using N-1 instead of N for the normalization, used to correct for the one degree of freedom lost by the calculation of the mean). However, when you look at the answer, provided, you'll notice that it divides by N, and does not provide 'NaN' for the condition N=1, as was asked.
- savagedata 6y ago
  One of my favorite data science factoids is how "regression" (to return to a former state) came to mean "prediction of a continuous variable". In the late 1800s, Sir Francis Galton noticed that extremely tall or short parents usually had children that were not as tall or short as themselves, i.e. the children's heights were regressing (returning) to the mean. He collected hundreds of data points, graphed them, and estimated a coefficient describing this relationship, thereby inventing "linear regression." We call them "regression" models simply because the first linear regression model was created to demonstrate the concept of regression to the mean. https://en.wikipedia.org/wiki/Regression_toward_the_mean#History https://en.wikipedia.org/wiki/Regression_toward_the_mean#His...
- notafraudster 6y ago
  The error here is ML terminology; the article answers correctly, if shallowly, the statistical definition of regression. Classical regression techniques can be (and are correctly) used on binary, ordinal, or categorical dependent variables. I know we teach people doing ML that the two forms of supervised ML are classification and regression, but that does a disservice mostly in order to make visual examples in teaching easier by making every topic a binary classification question and then saying "oh yeah, this works for regression too". Granted in an interview you'd probably want to use context in case the hiring people were trained on a specific vocab, but that maybe speaks more to the folly of using these dial-an-answer systems in place of actually learning the material.
latentdeepspace 6y ago
Do people realize that, these interview question collection do not help? I think there is 2 things to address here: - Interviewers will know "what is known" by every candidate (with the help of these pages) and harder questions will be asked - If these questions are asked at >junior levels, then RUN! the work will not satisfy you. The interview should be fun, and show the creativity of the candidate. These ones could be answered by anyone who read it a few times. I would not like to work with somebody who only know the answers to these questions and not more
- marcusabu 6y ago
  I think it's a bit far fetched to assume that recruiters tailor interview questions based on these Github repo's. For me as a junior data scientist it really useful to test my own knowledge and highlight areas which I need to study more.
  - fractionalhare 6y ago
    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 involving regression, I would expect companies to jealously guard it. If you're earnestly interested in building and testing your knowledge, I would recommend you read The Elements of Statistical Learning and Data Analysis Using Regression and Multilevel/Hierarchical Models. Also a good upper undergrad textbook in probability, like A First Course in Probability.
    - jointpdf 6y ago
      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 ML and stats is. https://projects.iq.harvard.edu/stat110/home https://projects.iq.harvard.edu/stat110/home Elements of Statistical Learning is a true textbook—a comprehensive bible that could occupy you for many thousands of hours. ISLR is the better book for a crash course: http://faculty.marshall.usc.edu/gareth-james/ISL/ http://faculty.marshall.usc.edu/gareth-james/ISL/ There are also lectures and slides from the authors: https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/ https://www.dataschool.io/15-hours-of-expert-machine-learnin...
zwaps 6y ago
Very interesting. I wonder in what type of interviews would these answers be considered ideal? Do people at OpenAI ask these sorts of questions, or is this more targeted toward other industries that require data scientists but are not populated by "stats" experts? For example, consider "What is regression?". The answer given is one way to go, but if the job posting would involve causal analysis or more statistical know-how, for example, it would probably be insufficient. I would want the candidate to speak about linear projections, about sampling assumptions, about probability models underlying the process and so on. On the other hand, I could imagine that it would not be a good strategy to start to lecture about minute details of regression analysis when applying for a standard data scientist position when sitting on front of "applied data scientists" or even HR folks. Anyone has any insights?
- minimaxir 6y ago
  ML research positions like those at OpenAI are a completely different level than data science interviews.
- smeeth 6y ago
  My experience is that interviews like these are for supporting role data science jobs. E.g. company x has a product (tech or not), they have some data, and they want to hire someone to make that data useful in improving their product or selling more of it. The general data science process is that when faced with a problem, you 1) select the appropriate algorithmic tool(s) for the problem and 2) apply the tool(s) to the data. One of the challenges in data science generally is that the tools can get pretty fucking complicated. The point of theory interview questions in general is to assess the first point, whether or not a candidate has the capacity to pick the right tool for a given problem. They want to hear that you understand some of the standard tools and what sorts of problems they are good for. If you "get" the common tools, you'll likely be able to reason about the application of new/different tools for weird problems as you face them, or so the logic goes. Everything I just said was more or less objective. This is my opinion: bad companies that do not know how to hire data scientists usually do what you're describing. They ask theory questions to assess whether or not the candidate already understands the specific tools they expect them to use. Good companies tend to pay more attention to whether the candidate is capable of understanding the universe of tools in general and are less worried about their specific application area. I should note that this is less relevant when hiring consultants or "plug and play" senior people. Of course for those roles you want to know that the candidate has done something similar already and is primed for success.
fractionalhare 6y ago
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 normality testing in general is usually a red herring, but this is specifically not a productive way of doing it. Use the other methods. A visual test that relies on how much the histogram approximates a bell curve is very prone to error, because a sample from a variety of other distributions can look visually normal even though it isn't. More broadly speaking, the reason I don't like this is because it's an example of the kind of formulaic, cargo-culted recipes that are often used in statistics without critical thinking. You should strive to obtain a deep understanding of your data and its distribution, and you should be deeply skeptical if the sample you happen to have looks normal. Nature abhors normality, and the central limit theorem can only promise a tendency towards normality as n approaches infinity. It says nothing about what size sample you'll practically need for your specific data to be able to treat it as normal.
- tgv 6y ago
  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.
  - srean 6y ago
    There are myriads of ways for two things to be unequal. In fact its fair to state that unless you are taking two sets of measurements of some fundamental physical constant or measuring artificially generated data, they are not going to be from the same exact distribution -- you cannot bathe in the same river twice is very true. The way to address the problem you speak of is to think hard about the kind deviations from equality that would be most damaging to the application (were it to slip in undetected). Once you know what's the most damaging deviation, you can then select a test that is very powerful in detecting that specific type of deviation. For example, you mention Kolmogorov(Smirnov) test. Those are daft blind at the tails of the distribution. So in case you need to catch deviations at the tail, then you can safely skip KS test, they are no good. On the other hand KS tests are very powerful around the median, if your application requires high resolution there, KS is the test you want.
bobdosherman 6y ago
Linear regression does not require errors to be iid, normal, and homoscedastic for it to "work". One of the ways to separate candidates is to push on what (and how) assumptions can be weakened, what the consequences are for estimation and inference, and what sort of corrections can be incorporated for maintaining consistency, improving efficiency, correcting biases, etc. An entry level candidate may not have (nor need to have) a complete understanding of asymptotic theory, but they should know what the purpose of robust standard errors are and how to use them.
- notafraudster 6y ago
  Not really sure why your comment is marked dead when it is correct. Maybe it's a new account thing? As you say, the Gauss-Markov conditions are necessary (and sufficient) to make OLS BLUE, but OLS still works fine under a variety of pathological conditions, and many of those conditions can be tested for and adjusted for using common techniques.
- bonoboTP 6y ago
  Interesting and perhaps shows the cultural differences between ML and stats people. I took a machine learning course in my bachelor's and two more ML courses in my master's (CS). These weren't some "deep learning lite", mess-around-in-Keras courses, because DL wasn't even big back then. We covered lots of stuff, Bayesian linear regression, Gaussian processes, Gibbs sampling, Metropolis-Hastings, hierarchical Dirichlet processes, SVMs, multi-class SVM, PCA, kernel PCA, perceptrons, CMAC neural nets, Hebbian learning, AdaBoost, Fisher vectors, EM algorithms for various distributions, fuzzy logic, optimization methods like conjugate gradients etc etc. But not once were the "Gauss-Markov conditions" mentioned. Frequentist theory was only marginally addressed. I taught myself some of that stuff from the Internet, such as hypothesis testing theory, p-values, t statistic, ANOVA, etc. Also, I'd say I'm good with data structures and algorithms, complexity theory, graph theory etc. I thought these skills would be a good fit for data science jobs, but I guess it's really such a wide umbrella term, that probably you're more looking for people trained in the frequentist, statistical side of it. What application field are you in, if it's no secret?
  - 6y ago
data4lyfe 6y ago
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 studies where the answer to the question itself will be, using feature selection. For example: "Let's say you have thousands of categorical features for an anonymized dataset involving human traits, how would you figure out which predictors are the most important?" Source: https://www.interviewquery.com/ https://www.interviewquery.com/
- marcinzm 6y ago
  >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.
minimaxir 6y ago
The weirdest thing about data science interviews (when I was actively interviewing) is SQL gotcha questions. Especially with window functions. Here's a long HN thread a few months ago about an annoying situation with an interviewer asserting uncommon syntax is common: https://news.ycombinator.com/item?id=23053981 https://news.ycombinator.com/item?id=23053981 Another related SQL gotcha I saw multiple times is finding the top n records of each group in a table. Which is a know-it-or-you-don't implementation, and the interviewer can still be a jerk if they want by slamming the interviewee if they include ties, or not (RANK vs. ROW_NUMBER). It's telling that there aren't any SQL window function examples in this repo. Another fun aspect of SQL interviews is dialect-specific questions, particularly with how date/times are handled. Years ago, a company famous/infamous for primarily using mySQL explicitly noted in a take-home assignment problem definition that the database was PostgreSQL, which allowed them to ask the aforementioned window question problem, the AT TIME ZONE syntax for filter, and allow a specific definition for "beginning of week" which required me to download the database and test it manually.
- claudeganon 6y ago
  These kinds of stories seem insane to me. I currently have to do some data science-y stuff in the more generalist consulting dev role I have. I used to do a lot of SQL stuff years ago, but have forgotten most of the syntax beyond the basics. That being said, all it took is some minor googling and reading of a few blog posts to solve some middling hard problems for my client. What kind of companies make people jump through these nerd hoops? Do they actually have real work that needs doing or is this all just posturing by interviewers?
  - gerbler 6y ago
    I think an element of it is that you can have unambiguous questions with SQL, which taken to the extreme do fall into gotcha territory. I was an interviewer for a business analyst & data science team recently and we needed to hire several analysts & data-scientists who had strong SQL skills because 90% of the data manipulation/processing used SQL. I was definitely aware that this limits who we hire, but we were very short-staffed and needed people so it was easiest for us to hire people who had a good understanding of SQL. That said, we did not care about syntax subtleties, just more if could you generally answer questions using SQL. And for the data-scientist role there was a lot more than just SQL but it was a useful check.
ryndbfsrw 6y ago
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 they would approach solving a problem I’m facing at that moment rather than these cookie cutter tests which are easy to game and tell me nothing
- y42 6y ago
  >> 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.. ;)
  - Florin_Andrei 6y ago
    > Comes out they didn't take me because of my lack of SQL skills. Good. You don't want to work there. It's not a good place to be in. That signal is very clear.
marcinzm 6y ago
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.
- minimaxir 6y ago
  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.
  - marcinzm 6y ago
    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.
- conjectures 6y ago
  That's a great question. Because it lets you as a candidate screen out places run by morons.
  - marcinzm 6y ago
    I wish. I saw it asked by otherwise great teams run by very competent people. It's done in place of a leetcode algo question and those are even more divorced from day to day work. System design questions make more sense but I've noticed that you score better on them if you study the area beforehand but convincingly lie that you barely know it (and are just that clever and fast on your feet). Interviews are a shit show in general.
  - bitxbit 6y ago
    Years back someone asked me an interview question to explain sd and confidence intervals. I just gave the wrong answers and left. Especially in nascent fields such as ML and DS, it’s very important to work with people who actually know what they are doing. Otherwise you will have wasted years of your precious twenties doing absolutely nothing productive.
rhacker 6y ago
These are at best machine learning questions, not data science. ML is definitely a sub-category of data science, but if you see a job posting for data science, you're 100% not going to be doing machine learning. That would have been labeled as a machine learning job posting.
teleforce 6y ago
For Machine Learning theory related to Data Science I'd highly recommend "The Hundred-Page Machine Learning Book" by Andriy Burkov [1]. According to the author, by reading one hundred pages of the book (plus some extra bonus pages), you will be ready to build complex AI systems, pass an interview or start your own business. It is a read first book and buy later if you think it is good enough. [1] http://themlbook.com/ http://themlbook.com/

242 points by fagnerbrack 6y ago

I only read the first question in theory.md and think the answer is quite weak. > What is regression? Which models can you use to solve a regression problem? The current answer only list some names that have "regression" in it, and the description of what a regression is doesn't say anything that distinguishes it from classification. It fails to mention that regression (in ML terminology) is prediction of a continuous variable. And that almost any method can be used to do regression: knn, neural network, random forest, svm. If other answers are of similar nature you might fail the interview.

dgellow 6y ago
From the README: > The answers are given by the community > If you know how to answer a question — please create a PR with the answer > If there's already an answer, but you can improve it — please create a PR with improvement suggestion > If you see a mistake — please create a PR with a fix
- kristjansson 6y ago
  I don’t like this response. It’s completely reasonable to assess the quality of informational content without assuming a duty to proofread and revise that content. Especially when the issue that might be corrected isn’t a typo or malapropism but ‘this whole thing isn’t that great.’
  - dgellow 6y ago
    Sure, I’m not saying that you have to correct it. Just that the project doesn’t assume that they have correct answers for everything and expect some participation of “the community” at large.
atty 6y ago
I read two answers from technical.md, and one of them was inconsistent with what was asked for... I would be very suspicious of using this repo for studying.
- pw6hv 6y ago
  Could you elaborate on which one in particular? I've also read the first questions but haven't noticed anything like this.
  - atty 6y ago
    Sorry, I should have put it in my original reply. If you search for where it asks for an implementation of the standard deviation, you'll see that it asks for an implementation including Bessel's correction (using N-1 instead of N for the normalization, used to correct for the one degree of freedom lost by the calculation of the mean). However, when you look at the answer, provided, you'll notice that it divides by N, and does not provide 'NaN' for the condition N=1, as was asked.
savagedata 6y ago
One of my favorite data science factoids is how "regression" (to return to a former state) came to mean "prediction of a continuous variable". In the late 1800s, Sir Francis Galton noticed that extremely tall or short parents usually had children that were not as tall or short as themselves, i.e. the children's heights were regressing (returning) to the mean. He collected hundreds of data points, graphed them, and estimated a coefficient describing this relationship, thereby inventing "linear regression." We call them "regression" models simply because the first linear regression model was created to demonstrate the concept of regression to the mean. https://en.wikipedia.org/wiki/Regression_toward_the_mean#History https://en.wikipedia.org/wiki/Regression_toward_the_mean#His...
notafraudster 6y ago
The error here is ML terminology; the article answers correctly, if shallowly, the statistical definition of regression. Classical regression techniques can be (and are correctly) used on binary, ordinal, or categorical dependent variables. I know we teach people doing ML that the two forms of supervised ML are classification and regression, but that does a disservice mostly in order to make visual examples in teaching easier by making every topic a binary classification question and then saying "oh yeah, this works for regression too". Granted in an interview you'd probably want to use context in case the hiring people were trained on a specific vocab, but that maybe speaks more to the folly of using these dial-an-answer systems in place of actually learning the material.

latentdeepspace 6y ago

Do people realize that, these interview question collection do not help? I think there is 2 things to address here: - Interviewers will know "what is known" by every candidate (with the help of these pages) and harder questions will be asked - If these questions are asked at >junior levels, then RUN! the work will not satisfy you. The interview should be fun, and show the creativity of the candidate. These ones could be answered by anyone who read it a few times. I would not like to work with somebody who only know the answers to these questions and not more

marcusabu 6y ago
I think it's a bit far fetched to assume that recruiters tailor interview questions based on these Github repo's. For me as a junior data scientist it really useful to test my own knowledge and highlight areas which I need to study more.
- fractionalhare 6y ago
  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 involving regression, I would expect companies to jealously guard it. If you're earnestly interested in building and testing your knowledge, I would recommend you read The Elements of Statistical Learning and Data Analysis Using Regression and Multilevel/Hierarchical Models. Also a good upper undergrad textbook in probability, like A First Course in Probability.
  - jointpdf 6y ago
    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 ML and stats is. https://projects.iq.harvard.edu/stat110/home https://projects.iq.harvard.edu/stat110/home Elements of Statistical Learning is a true textbook—a comprehensive bible that could occupy you for many thousands of hours. ISLR is the better book for a crash course: http://faculty.marshall.usc.edu/gareth-james/ISL/ http://faculty.marshall.usc.edu/gareth-james/ISL/ There are also lectures and slides from the authors: https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/ https://www.dataschool.io/15-hours-of-expert-machine-learnin...

zwaps 6y ago

Very interesting. I wonder in what type of interviews would these answers be considered ideal? Do people at OpenAI ask these sorts of questions, or is this more targeted toward other industries that require data scientists but are not populated by "stats" experts? For example, consider "What is regression?". The answer given is one way to go, but if the job posting would involve causal analysis or more statistical know-how, for example, it would probably be insufficient. I would want the candidate to speak about linear projections, about sampling assumptions, about probability models underlying the process and so on. On the other hand, I could imagine that it would not be a good strategy to start to lecture about minute details of regression analysis when applying for a standard data scientist position when sitting on front of "applied data scientists" or even HR folks. Anyone has any insights?

minimaxir 6y ago
ML research positions like those at OpenAI are a completely different level than data science interviews.
smeeth 6y ago
My experience is that interviews like these are for supporting role data science jobs. E.g. company x has a product (tech or not), they have some data, and they want to hire someone to make that data useful in improving their product or selling more of it. The general data science process is that when faced with a problem, you 1) select the appropriate algorithmic tool(s) for the problem and 2) apply the tool(s) to the data. One of the challenges in data science generally is that the tools can get pretty fucking complicated. The point of theory interview questions in general is to assess the first point, whether or not a candidate has the capacity to pick the right tool for a given problem. They want to hear that you understand some of the standard tools and what sorts of problems they are good for. If you "get" the common tools, you'll likely be able to reason about the application of new/different tools for weird problems as you face them, or so the logic goes. Everything I just said was more or less objective. This is my opinion: bad companies that do not know how to hire data scientists usually do what you're describing. They ask theory questions to assess whether or not the candidate already understands the specific tools they expect them to use. Good companies tend to pay more attention to whether the candidate is capable of understanding the universe of tools in general and are less worried about their specific application area. I should note that this is less relevant when hiring consultants or "plug and play" senior people. Of course for those roles you want to know that the candidate has done something similar already and is primed for success.

fractionalhare 6y ago

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 normality testing in general is usually a red herring, but this is specifically not a productive way of doing it. Use the other methods. A visual test that relies on how much the histogram approximates a bell curve is very prone to error, because a sample from a variety of other distributions can look visually normal even though it isn't. More broadly speaking, the reason I don't like this is because it's an example of the kind of formulaic, cargo-culted recipes that are often used in statistics without critical thinking. You should strive to obtain a deep understanding of your data and its distribution, and you should be deeply skeptical if the sample you happen to have looks normal. Nature abhors normality, and the central limit theorem can only promise a tendency towards normality as n approaches infinity. It says nothing about what size sample you'll practically need for your specific data to be able to treat it as normal.

tgv 6y ago
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.
- srean 6y ago
  There are myriads of ways for two things to be unequal. In fact its fair to state that unless you are taking two sets of measurements of some fundamental physical constant or measuring artificially generated data, they are not going to be from the same exact distribution -- you cannot bathe in the same river twice is very true. The way to address the problem you speak of is to think hard about the kind deviations from equality that would be most damaging to the application (were it to slip in undetected). Once you know what's the most damaging deviation, you can then select a test that is very powerful in detecting that specific type of deviation. For example, you mention Kolmogorov(Smirnov) test. Those are daft blind at the tails of the distribution. So in case you need to catch deviations at the tail, then you can safely skip KS test, they are no good. On the other hand KS tests are very powerful around the median, if your application requires high resolution there, KS is the test you want.

bobdosherman 6y ago

Linear regression does not require errors to be iid, normal, and homoscedastic for it to "work". One of the ways to separate candidates is to push on what (and how) assumptions can be weakened, what the consequences are for estimation and inference, and what sort of corrections can be incorporated for maintaining consistency, improving efficiency, correcting biases, etc. An entry level candidate may not have (nor need to have) a complete understanding of asymptotic theory, but they should know what the purpose of robust standard errors are and how to use them.

notafraudster 6y ago
Not really sure why your comment is marked dead when it is correct. Maybe it's a new account thing? As you say, the Gauss-Markov conditions are necessary (and sufficient) to make OLS BLUE, but OLS still works fine under a variety of pathological conditions, and many of those conditions can be tested for and adjusted for using common techniques.
bonoboTP 6y ago
Interesting and perhaps shows the cultural differences between ML and stats people. I took a machine learning course in my bachelor's and two more ML courses in my master's (CS). These weren't some "deep learning lite", mess-around-in-Keras courses, because DL wasn't even big back then. We covered lots of stuff, Bayesian linear regression, Gaussian processes, Gibbs sampling, Metropolis-Hastings, hierarchical Dirichlet processes, SVMs, multi-class SVM, PCA, kernel PCA, perceptrons, CMAC neural nets, Hebbian learning, AdaBoost, Fisher vectors, EM algorithms for various distributions, fuzzy logic, optimization methods like conjugate gradients etc etc. But not once were the "Gauss-Markov conditions" mentioned. Frequentist theory was only marginally addressed. I taught myself some of that stuff from the Internet, such as hypothesis testing theory, p-values, t statistic, ANOVA, etc. Also, I'd say I'm good with data structures and algorithms, complexity theory, graph theory etc. I thought these skills would be a good fit for data science jobs, but I guess it's really such a wide umbrella term, that probably you're more looking for people trained in the frequentist, statistical side of it. What application field are you in, if it's no secret?
- 6y ago

data4lyfe 6y ago

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 studies where the answer to the question itself will be, using feature selection. For example: "Let's say you have thousands of categorical features for an anonymized dataset involving human traits, how would you figure out which predictors are the most important?" Source: https://www.interviewquery.com/ https://www.interviewquery.com/

marcinzm 6y ago
>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.

minimaxir 6y ago

The weirdest thing about data science interviews (when I was actively interviewing) is SQL gotcha questions. Especially with window functions. Here's a long HN thread a few months ago about an annoying situation with an interviewer asserting uncommon syntax is common: https://news.ycombinator.com/item?id=23053981 https://news.ycombinator.com/item?id=23053981 Another related SQL gotcha I saw multiple times is finding the top n records of each group in a table. Which is a know-it-or-you-don't implementation, and the interviewer can still be a jerk if they want by slamming the interviewee if they include ties, or not (RANK vs. ROW_NUMBER). It's telling that there aren't any SQL window function examples in this repo. Another fun aspect of SQL interviews is dialect-specific questions, particularly with how date/times are handled. Years ago, a company famous/infamous for primarily using mySQL explicitly noted in a take-home assignment problem definition that the database was PostgreSQL, which allowed them to ask the aforementioned window question problem, the AT TIME ZONE syntax for filter, and allow a specific definition for "beginning of week" which required me to download the database and test it manually.

claudeganon 6y ago
These kinds of stories seem insane to me. I currently have to do some data science-y stuff in the more generalist consulting dev role I have. I used to do a lot of SQL stuff years ago, but have forgotten most of the syntax beyond the basics. That being said, all it took is some minor googling and reading of a few blog posts to solve some middling hard problems for my client. What kind of companies make people jump through these nerd hoops? Do they actually have real work that needs doing or is this all just posturing by interviewers?
- gerbler 6y ago
  I think an element of it is that you can have unambiguous questions with SQL, which taken to the extreme do fall into gotcha territory. I was an interviewer for a business analyst & data science team recently and we needed to hire several analysts & data-scientists who had strong SQL skills because 90% of the data manipulation/processing used SQL. I was definitely aware that this limits who we hire, but we were very short-staffed and needed people so it was easiest for us to hire people who had a good understanding of SQL. That said, we did not care about syntax subtleties, just more if could you generally answer questions using SQL. And for the data-scientist role there was a lot more than just SQL but it was a useful check.

ryndbfsrw 6y ago

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 they would approach solving a problem I’m facing at that moment rather than these cookie cutter tests which are easy to game and tell me nothing

y42 6y ago
>> 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.. ;)
- Florin_Andrei 6y ago
  > Comes out they didn't take me because of my lack of SQL skills. Good. You don't want to work there. It's not a good place to be in. That signal is very clear.

marcinzm 6y ago

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.

minimaxir 6y ago
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.
- marcinzm 6y ago
  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.
conjectures 6y ago
That's a great question. Because it lets you as a candidate screen out places run by morons.
- marcinzm 6y ago
  I wish. I saw it asked by otherwise great teams run by very competent people. It's done in place of a leetcode algo question and those are even more divorced from day to day work. System design questions make more sense but I've noticed that you score better on them if you study the area beforehand but convincingly lie that you barely know it (and are just that clever and fast on your feet). Interviews are a shit show in general.
- bitxbit 6y ago
  Years back someone asked me an interview question to explain sd and confidence intervals. I just gave the wrong answers and left. Especially in nascent fields such as ML and DS, it’s very important to work with people who actually know what they are doing. Otherwise you will have wasted years of your precious twenties doing absolutely nothing productive.

rhacker 6y ago

These are at best machine learning questions, not data science. ML is definitely a sub-category of data science, but if you see a job posting for data science, you're 100% not going to be doing machine learning. That would have been labeled as a machine learning job posting.

teleforce 6y ago

For Machine Learning theory related to Data Science I'd highly recommend "The Hundred-Page Machine Learning Book" by Andriy Burkov [1]. According to the author, by reading one hundred pages of the book (plus some extra bonus pages), you will be ready to build complex AI systems, pass an interview or start your own business. It is a read first book and buy later if you think it is good enough. [1] http://themlbook.com/ http://themlbook.com/