The repository is to record all homeworks and projects completed during my enrollment in STATS 507. This will help me review the important programming skills taught in the class. The documents can be beneficial for both myself and others who want to apply Python to statistical research, in order to improve efficiency and accuracy.
The homeworks and projects are set by the professor and GSIs at the University of Michigan. All the execution codes are done by myself. Copyrights are protected.
STATS 507 surveys the software tools that are currently popular among data scientists in academia and industry. The course begins with an accelerated introduction to programming in Python. Next, we focus on Python’s scientific computing stack: numpy, scipy, pandas, and scikit-learn. We also cover regular expressions, relational databases, and the UNIX/Linux command line. The final part of the course is an introduction to deep learning using PyTorch.