Data science interview questions with answers

Having somewhat standard "objective" questions helps even with senior candidates. You'd be surprised how many senior's would struggle with the basics, or who aren't as senior as their resume would lead you to believe.

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/

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?

> The t-test is extremely robust to departures from normality given equal sample sizes

That's over selling it. Its very sensitive to fat tails and skew. That's the reason robust testing and estimation is a thing. Wilcox's research would be a good near contemporary place to start [0][1].

[0] Wilcox, Robustness of Standard Tests https://onlinelibrary.wiley.com/doi/abs/10.1002/978111844511... (paywalled)

    Abstract: Conventional hypothesis‐testing methods such as
    student's t, the ANOVA F test, and methods based on the
    ordinary least squares regression estimator, are not
    robust to violations of assumptions. In fact, there are
    general conditions under which these methods can provide
    poor control over the probability of a type I error and
    inaccurate confidence intervals, no matter how large the
    sample sizes might be. Relatively poor power is yet another 
    concern.

[1] Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy https://books.google.co.in/books?id=uUNGzhdxk0kC

   Conventional statistical methods have a very serious
   flaw. They routinely miss differences among groups or
   associations among variables that are detected by more
   modern techniques - even under very small departures from
   normality. Hundreds of journal articles have described
   the reasons standard techniques can be unsatisfactory,
   but simple, intuitive explanations are generally
   unavailable. Improved methods have been derived, but they
   are far from obvious or intuitive based on the training
   most researchers receive. Situations arise where even
   highly nonsignificant results become significant when
   analyzed with more modern methods. Without assuming any
   prior training in statistics, Part I of this book
   describes basic statistical principles from a point of
   view that makes their shortcomings intuitive and easy to
   understand.

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.