# Game Theory/Optimization: what strategy optimizes my employability? How to pragmatically assess my utility funciton and hence my payoff? [closed]

I would like to receive some very pragmatic answers, and if not, informed opinions.

So I have to choose three elements out of list A and 3 elements out of list B and I want to pick them so that I can maximize my employability.

As employability I want to consider the following sectors: Engineering Companies, Statistics/Data Analysis/Machine Learning companies, Mathematical Physics Research at University/Private companies, Analyst/Software developer.

Here are the lists:

LIST A: (choose 3)

• Numerical Methods (learning Python in-depth and also the underlying mathematical theory)
• Survival Models
• Advanced Theory of PDEs and Applications
• Statistical Inference
• Graph Theory
• Optimization
• Design & Analysis of Experiments (learning SAS in depth)

LIST B: (Choose 3)

• Relativity, Cosmology and Black Holes.
• Statistical Methods 2 (Nonlinear regression)
• Mathematical Biology
• Hilbert Spaces (functional analysis on Hilbert Spaces)
• Integral Transform Methods
• Stochastic Processes

So my question is how do I choose, PRAGMATICALLY, 3 items from list A and 3 items from list B to optimize my employability as considered in the four job sectors? For example here are my thoughts:

I gave each item a grade from 1 to 10 on how it would be useful in each sector, so that for example " Numerical Methods (10,8,7,10)" means that numerical methods would be 10/10 useful in engineering companies jobs, 8/10 useful in statistics/data analysis/machine learning companies, 7/10 in research and finally 10/10 in Analyst/software developer.

Of course the numbers need to be motivated, however they are clearly not that arbitrary, as I am sure many will say down in the comments.

So here are the grades I've given to the items and how I optimized for each sector:

LIST A: Numerical Methods(10,8,7,10), Survival Models(5,10,6,6), Advanced PDEs (9,4,9,2), Advanced fluid dynamics (9,3,10,3), Statistical Inference(7,10,6,6), Graph Theory(8,8,6,9), Design&Analysis of Experiments(9,9,9,9)

LIST B: Rel&Cosmology(8,2,10,2), Nonlinear Regression (5,10,6,6), Mathematical Biology(7,2,10,2), Hilbert Spaces(2,4,8,2), Integral Transfom (10, 2,10,2), Stochastic Processes (7,10,8,7).

Therefore I obtained the following:

• Engineering: LIST A: Num Meth, Adv PDE, Analysis & Design ( or choose Fluid Dynamics). LIST B: integral transf, rel and cosmology, stochastic processes (or math biology).
• Stats/Machine Learning: LIST A: Survival, Stat Inference, Analysis and Design. LIST B: Non linear regression, stochastic processes, Hilbert Spaces.
• Research: LIST A: Adv PDEs, Adv fluid dynamics, design and analysis of experiments. LIST B: Rel and cosmology, math biology, integral transform.
• Analyst/Soft Dev: LIST A: Num Meth, Graph Theory, Design and Analysis. LIST B: nonlinear regression, stochastic processes, and any other.

Given my grades, how exactly do you think I could optimize my employability? Furthermore, would you be able to propose a different grading? In general, if I had no preference between the four job sectors, and no preference in the items themselves, but my only payoff would consist in optimizing my employability, what combination of items would be the best?

## closed as off-topic by Rahul, Claude Leibovici, астон вілла олоф мэллбэрг, user91500, JunivenFeb 26 '17 at 0:04

This question appears to be off-topic. The users who voted to close gave this specific reason:

• "Seeking personal advice. Questions about choosing a course, academic program, career path, etc. are off-topic. Such questions should be directed to those employed by the institution in question, or other qualified individuals who know your specific circumstances." – Rahul, Claude Leibovici, астон вілла олоф мэллбэрг, user91500, Juniven
If this question can be reworded to fit the rules in the help center, please edit the question.

• My two cents: For an engineering or data science career, I'd vote for numerical methods, statistical inference, and optimization from list A, and Statistical methods 2, stochastic processes, and I'm not sure about the third from list B. Ideally list B would contain a linear algebra class, because linear algebra is super important for everything. For mathematical physics research, the answer would be quite different. – littleO Feb 25 '17 at 2:33
• @littleO first of all thank you for your comment, didn't expect such a quick answer! I like your list A choice! Regarding linear algebra, I've already had two courses in linear algebra in my first year so I should have some experience in it. (these are courses for the third year) Would you be able to just give me a guess of how you would choose them for mathematical physics research? Furthermore, as I saw you chose many "stats" modules, is statistics very important in a engineering career? – Euler_Salter Feb 25 '17 at 2:37
• Statistics is super important for data science, and probably very important for a lot engineering careers. I don't know too much about mathematical physics but I'll take a stab at it anyway. For mathematical physics I would choose Advanced PDEs, Advanced Fluid Dynamics, and I'm not sure what else from list A, and Relativity/Cosmology/Black holes, Hilbert Spaces, and maybe Integral Transform Methods from list B. – littleO Feb 25 '17 at 2:46
• For mathematical physics research, I'd say the Hilbert spaces class should definitely be on your list, given the role Hilbert spaces and functional analysis play in quantum mechanics. – littleO Feb 25 '17 at 3:09
• I don't really know about the Design and Analysis of Experiments class, but my guess (based on very little information) is that it's a low priority. Maybe for some types of engineering jobs it's more important than I realize. I would guess it's not very useful for mathematical physics research, because I think mathematical physics is very theoretical. I could be wrong. – littleO Feb 25 '17 at 8:13