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data-science

Planning to spend the weekend reading/watching H2O stuff:

I intend to use the latest major version of H2O (3.x) which has been recently released.

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I googled and put together a lightly curated set of links that I want to read to collate a strategy on how to prepare for a data science interview.

Since I will be answering questions on programming using Python, here are some links on Python programming interview questions:

I must admit that I don’t really like the look of some of these links as they seem too focused on syntax or clever and compact ways of achieving trivial things, and not on the language big picture, architecture of applications or actually solving difficult problems. In addition, these don’t cover some of the core packages that a data scientist should know, such as NumPy, SciPy or scikit-learn, and to a lesser extent pandas and statsmodels.

For reference, here is my implementation of FizzBuzz.

for x in range(101):
    if x % 15 == 0:
        print("FizzBuzz")
    elif x % 3 == 0:
        print("Fizz")
    elif x % 5 == 0:
        print("Buzz")
    else:
        print(x)

This is the vanilla implementation that can be found all over the internet, and the trick really is to handle division modulo 15 first rather than last, as it reads in the question. Everything else is standard syntax. At some point, I have implemented it using generators, but I can’t find that solution, and I don’t want to get distracted from pulling together links for my interview prep.

Over the next couple of days I intend to work through these links, and I will see if I can refine down to a core strategy that I can use to preapre and review before interview. I also intend to blog about some of the peripheral aspects of job search such as writing a good resume and cover letter, interview etiquette and searching for relevant jobs.