4th year applied math/econ double major at UC Berkeley. I thought i'd post course descriptions since course titles don't really mean much.
Introduction to Partial Differential Equations -- Mathematics (MATH) 126 [4 units]
Description: Waves and diffusion, initial value problems for hyperbolic and parabolic equations, boundary value problems for elliptic equations, Green's functions, maximum principles, a priori bounds, Fourier transform
Concepts in Computing with Data -- Statistics (STAT) 133 [3 units]
Description: An introduction to computationally intensive applied statistics. Topics will include organization and use of databases, visualization and graphics, statistical learning and data mining, model validation procedures, and the presentation of results
Econometric Analysis -- Economics (ECON) 141 [4 units]
Description: Introduction to problems of observation, estimation, and hypothesis testing in economics. This course covers the statistical theory for the linear regression model and its variants, with examples from empirical economics.
My 4th class will probably be one of the following:Probability for Applications -- Statistics (STAT) 204 [4 units]
Description: A treatment of ideas and techniques most commonly found in the applications of probability: Gaussian and Poisson processes, limit theorems, large deviation principles, information, Markov chains and Markov chain Monte Carlo, martingales, Brownian motion and diffusion. Probability Theory -- Statistics (STAT) C205A [4 units]
Description: Some knowledge of real analysis and metric spaces, including compactness, Riemann integral. Knowledge of Lebesgue integral and/or elementary probability is helpful, but not essential, given otherwise strong mathematical background. Measure theory concepts needed for probability. Expectation, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations; martingales and theory convergence. Markov chains. Stationary processes.
Applied Stochastic Process I -- Industrial Engineering (IND ENG) 263A [4 units]
Description: Conditional Expectation. Poisson and renewal processes. Renewal reward processes with application to inventory, congestion, and replacement models. Discrete and continuous time Markov chains; with applications to various stochastic systems--such as exponential queueing systems, inventory models and reliability systems.
I would like to take 205 but I've heard it is quite difficult from the grad students.
