Professional Graduate Data Science Coursework - Spring semester 2017
The course is the second part of a series about advanced data science methods. Topics include the handling of big data and database management with spark, the use of non-linear statistical models such as smoothers and GAM, unsupervised learning, and deep learning with neural networks. The major programming languages are R and Python.
The directory structure is as follows:
| DIRECTORY | DESCRIPTION |
|---|---|
. |
Files such as README and gitignore |
./hw/ |
The homework project files |
./labs/ |
Material from the labs |
./lectures/ |
Material from the lectures |
./videos/ |
Videos from the lectures and labs |
You can access all the coursework etc. here.