[burnerOh2125]

Interesting, I'm a phd-drop out in computational biology, working as a data science consultant: I use mathematics including multi-dimensional statistics, linear algebra, and calculus everyday. Being self-taught, I'm very self conscience about the math I don't know, but so far, not knowing differential equations doesn't seem to have hurt me. I actually just ran into a problem that uses Hamiltonian dynamics, so maybe I will end up learning differential equations, but it does seem like the course, as taught many places and in the Dover books I own, presents either no useful conceptual insight, like why I learned geometric algebra, or a powerful toolset, like some multi-dimensional statistics.

[codewithcheese]

Hi I am interesting your advice on which math to learn if you have a spare moment to provide it. I too am starting to teach my self the requirements of data science and am also self conscious of the math I don't know. I am very excited about what is now possible with machine learning and deep learning as I believe it will become increasingly necessary for developers to stay relevant.

Could you comment in more detail on which mathematical skill you have found useful as data scientist? Which resources did you use to teach yourself? Very appreciative of any help.

[ragulpr]

* Solid linear algebra

* Probability theory

* Statistics (bayesian if possible)

* Optimization (this is less important but extremely useful)

If you know the mechanics of multivariate calculus you'll be fine learning the above. The course that personally have had most payoff was functional analysis. Purely theoretical course that will give you no practical skills and at first glance seems unrelated to ML but it (subtly) gave me a much deeper understanding of what ML is all about.

[srean]

Think of it this way, most of the people in data-sciences are not familiar with differential or difference equations. So once you have a tool that many others don't have, certain problems (can) become accessible to you in a way it is not accessible to others. Sure, the problems solved by people who do not have differential equations in their tool chest will not need differential equations, that's a tautology.

[j7ake]

So how do you reason with abundance of molecules? mRNA and protein can be modelled from simple linear differential equation