This repository holds some of my programs from classes (Numerical Analysis and Computational Physics), and from extra-curricular activities (Data Analysis Competition 2017). Here is an overview of what to expect in each directory.
This course focused on handling large datasets in python, leveraging physical problems. Here, you will find some (admittedly poorly documented) labs, homework, and final project. The labs ranged from finding the moments of inertia, to graphing the energy in a finite well. The homework typically was a problem from Mark Newman's Computational Physics textbook. While my final project, was graphing the illumination of an object--globe and scanning tunneling microscope based on the angle to the light.
This course focused on mathematical problems on a computer. Instead of solving the problem symbolically with pen and paper, this solves (typically by approximation) root-finding problems, integrals, derivatives. Along with these solutions, we can typically find or approximate the error associated with each solution.
This my first data analysis competition. I was pared with two math majors, and I was the only one who was familiar with programming. Therefore, I did most of the models, using Python's sci-kit learn and pandas modules. I was ill-prepared for this competition, but I tried out many models using sci-kit learn, found the cross-validation score associated with each, and then prepared a presentation with my fellow mathematicians on my team, which is a violation of the NDA, which would be a violation of a NDA to include in the repository.