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I am a Pure Maths PhD student specialising in functional analysis.

I would like to work as a data scientist after my PhD graduation, particularly in the field of machine learning, deep learning and artificial intelligence.

I have some backgrounds on machine learning such as linear regression, logistic regression, K-mean clustering, SVM. For deep learning, I know neural networks and CNN.

To build up more on my theoretical background in these fields, I started reading Element of Statistical Learning (ESL) where I think it is known as the bible in statistical learning. I find its contents manageable.

In terms of programming, I have been using Python for the past 2 years and tutored an undergraduate Python course last year. So I think my Python skills, data Structures and Algorithms are average (at least, not beginner). I have implemented some projects involving stochastic differential equation models using Python.

My question is: what is next after I finish reading ESL?

I found a post in CV asking for reference BEFORE reading ESL, but not AFTER ESL.

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    $\begingroup$ You've told us what you know, but not what you want to learn about. What information do you need? What criteria do we use to differentiate between several alternative advanced texts? (Also, there are some texts listed here: stats.stackexchange.com/questions/460411/…) $\endgroup$
    – Sycorax ♦
    May 25, 2020 at 16:15
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    $\begingroup$ I’m going to mention that most of professional data science is not modeling but cleaning data. If you don’t have experience with such tasks, learn Pandas and some of the other data manipulation libraries that have built-in functions to accomplish a lot of what you might be tempted to solve using time-consuming methods like loops. So I guess my recommendation is Python for Data Analysis by Wes McKinney. You also might find yourself more interested in journal and conference papers than books. That’s where the cutting-edge material will be. $\endgroup$
    – Dave
    May 25, 2020 at 16:19
  • $\begingroup$ @Dave can you suggest a few journals and conference papers that a data scientist must know? $\endgroup$
    – Idonknow
    May 25, 2020 at 23:01
  • $\begingroup$ I would like to learn theoretical machine learning and artificial intelligence. $\endgroup$
    – Idonknow
    May 26, 2020 at 7:14
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    $\begingroup$ I would say that, if you want to be a data scientist, you need to get much more into practical stuff. Cleaning data. Figuring out what the problem is. Working with clients. $\endgroup$
    – Peter Flom
    May 26, 2020 at 11:59

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Well the answer to the question is probably a matter of preference and depends on whether you want to specialize in some specific field (e.g. reinforcement learning) or you're aiming at having a more in-depth (but not limited to one subfield) view on machine learning.

If it's the latter I would recommend you to look at the following two titles (both available online):

  1. Machine Learning: A Probabilistic Perspective, Kevin P. Murphy

  2. Pattern Recognition and Machine Learning, Christopher M. Bishop

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