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Showing results for tags 'real-life applications'.
After a long hibernation, I'm emerging-- with questions for the informed! I will have one free course next quarter in my area studies terminal MA program (terminating in June), and I'd like to take that chance to bolster my quantitative skills, which I currently lack. The issue is, I have many options available to me for a one-course venture into mathematical/statistical methods. My background: BC calculus from high school. Previous posts on this issue suggest two philosophies: i) cover specific topics relevant to statistical social science work, or ii) just get the fundamentals of math down (calculus, differential equations, linear algebra, etc) that will allow someone to access the necessary coursework when the time comes. The trade-off is one of timing-- if you want to use quantitative methods on the job before pursuing a PhD, are there any topics that must be covered beforehand? To help me decide, and especially to help me distinguish the different syllabi available to me, I ask the forum what topics need to be covered in order to: 1) Signal quantitative ability to ad-com's; 2) Signal quantitative competence to potential public/private sector employers; 3) Enhance my ability to conduct political science and/or policy-relevant research (two questions, I know); The more specific one can be about topics that ought to be covered in methods, the better-- it will help me and whoever else faces a similar situation in the future compare syllabi. Currently I am looking at two stats-for-social scientists courses, one with a heavy R software emphasis and the other more theoretical; one statistics department course (still more theoretical, but it's hard for me to judge); and finally, a linear algebra/multivariate calculus course that itself would not contribute to research methods but would allow me to study econometrics and intermediate microeconomics on my own following the completion of my degree.