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This is my first question here, so please pardon my gaffes.

I am currently working as a Data-Scientist, a position which I worked up from Junior Analyst position.My bachelors is in Computer Science and masters in Information Systems where I learned most of my Stats/Analytics knowledge.

I am planning to appear for a few data-science interviews in distant future and I am aiming to have in-depth Statistical(at par with Statistics grads) and Machine-Learning knowledge.

Currently I do not have any deadline on hands,hence I am open to starting with Calculus/Linear Algebra refreshers as well.

Can you guys suggest the best course of books/videos to prepare myself?

P.S.: I am open to suggestions on programming as well.

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closed as too broad by Xi'an, Michael Chernick, Sycorax, Siong Thye Goh, mdewey Apr 14 at 15:42

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

  • $\begingroup$ Can never go wrong with a little more linear algebra. I recommend Sheldon Axler's Linear Algebra Done Right. $\endgroup$ – Mr. Wayne Apr 13 at 18:59
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Partial advice, in no particular order, to succeed in your DS interview:

  • Study through a book like Hastie et al.'s Elements of Statistical Learning or Barbers's Bayesian Reasoning and Machine Learning; forget videos for a long-term plan unless you decide to follow a long series of lectures. Do not get sucked into Deep Learning to begin with; it is cool and hip but realistically only a small proportion of ML/Stats jobs actually require Deep Learning; core competency on how and why to use regularisation and dimensionality reduction, will get one much, much further. You want to come across as being coherent and learned.

  • Answer questions in CV.SE, i.e. here. You want the experience of strangers judging you on your merits as a ML/Stats practitioner? Look no further, just click here. This is free practice; but it requires time.

  • Go to Glassdoor and write down all the Google, Microsoft, LinkedIn, Facebook, Amazon, etc. questions on their Data Science interviews mentioned. Also check general analyst questions from companies that might interest you. This should comfortably give you about 250-300 questions to go with. Some questions will be trivial, some will be hard and some will be out of scope. Learn the trivial ones (almost) by heart and learn how to answer the hard ones. Take a moment and think about out of scope ones so you avoid being totally flat-footed if they come up.

  • Do not give up and keep your cool. Personal anecdote: I have seen candidates with PhD in Statistics crumble under simple questions that they got wrong; myself included. Interviewing is a game, and a single game does not define you. Do not treat any interview as a write-off even it goes bad and do not panic if you get stuff wrong; all interviews are a learning experience and you will become better.

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  • $\begingroup$ This is pretty good advice ! (+1) $\endgroup$ – Robert Long Apr 13 at 19:48
  • $\begingroup$ Thanks user11852. I can see your experience reflecting through your response. Let's say you wanted to suggest 4-5 books of statistics with increasing level of difficulty which would you suggest?. Right now your reply, answers my question very much. This is just a complementary question. $\endgroup$ – ealbsho93 Apr 13 at 20:19
  • $\begingroup$ I am glad I could help; if you find an answer helpful you could consider upvoting it. For your side question: This is a bit of a matter of preference really. I think focusing on one book and finishing it, will provide one good context about what needs to be the second book. I like L. Wasserman's books All of Statistics and All of Non-parametric Statistics a lot but they are somewhat out of scope for a new-comer. $\endgroup$ – usεr11852 Apr 13 at 20:26

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