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What math topics to cover before msc in data science watch

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    I am currently completing a bsc in finance, investment and risk, and have been accepted to pursue an msc in data science this October. I have covered finance related stats and am confident with it, but i am concerned that my core math/stats may be lacking (haven't looked at it since A levels over five years ago). I want to spend the time from now until October on brushing up, and so opinions on a study plan/books would be greatly appreciated. The areas i want to study are:

    Linear algebra
    Calculus-PDE's in particular
    Probability theory
    Real analyses
    Stochastic calculus (purely for my own interest if time permits)


    Obviously i am not looking to master all of these topics, but i want to get back to the nitty gritty of the fundamentals, because i am concerned that other students on the msc may have come from a pure mathematics background. I am fortunate enough to be able to dedicate a great deal of time to this, and i work better from books as apposed to moocs etc.

    Are the Schaum outline books a good place to start? I have purchased a few and they seem to be chock full of exercises. I am also starting to read Analyses 1 by Terence Tao.

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    (Original post by Parsley22)
    I am currently completing a bsc in finance, investment and risk, and have been accepted to pursue an msc in data science this October. I have covered finance related stats and am confident with it, but i am concerned that my core math/stats may be lacking (haven't looked at it since A levels over five years ago). I want to spend the time from now until October on brushing up, and so opinions on a study plan/books would be greatly appreciated. The areas i want to study are:

    Linear algebra
    Calculus-PDE's in particular
    Probability theory
    Real analyses
    Stochastic calculus (purely for my own interest if time permits)
    I'd say you have a pretty good selection of topics there. Of the ones that you list, I'd say that you're least likely to come across PDE's at MSc level for Data Science (they may arise at research level when you're looking at means of optimizing objective functions) and I think it relatively unlikely that stochastic calculus would arise. My suggestion is to focus on probability theory (which will lead you on to the parts of real analysis that you might just come across at masters level - i.e. measure theory), linear algebra and multidimensional calculus - make sure that you're comfortable with differentiating matrix equations, for example.

    Are the Schaum outline books a good place to start? I have purchased a few and they seem to be chock full of exercises. I am also starting to read Analyses 1 by Terence Tao.
    Schaum stuff can be good if you like learning via problems.I'd go by Amazon reviews to see which individual volumes are best. For probability, Grimmett & Stirzaker is pretty much a standard tome (with Grimmett's "Probability: an Introduction" as a suitable alternative if G & S is too much). For good textbooks on Data Science itself (that will lead you to investigating the maths that you need), both Kuhn & Johnson's "Applied Predictive Modeling" and James, Witten Hastie & Tibshirani "An Introduction to Statistical Learning" are excellent. Hastie, Tibshirani & Friedman "Elements of Statistical Learning" is an excellent handbook.
 
 
 
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