By: Kenji Oman
Courses from: January - August, 2018
This repo contains the programming assignments I completed for the UW data science certificate course series, as an example of my work. The assignments are fairly raw (in the same form as how I submitted them), so with time, I may clean them up a bit more for public consumption.
The assignments for Class 1 built upon each other, so the final presentation would be a good place to look for an overview of that work. Also, to note, the assignments were required to be in .py files, although jupyter notebooks are a more natural way to represent the work required. As such, the final presentation was done in the form of a jupyter notebook.
For Class 2, all the assignments/ milestones were fairly independent of each other.
For Class 3, the assignments were independent of each other, but the milestones were on the same project (merging Kaggle's traffic violations data set for Montgomery County with Weather/ Daylight data).
TOC to come
A note on license -- this work is strictly for educational purposes, and as such, I open it to anyone else that might be able to learn from it (feel free to fork, copy, tweak, etc, but don't submit it/ portions of it to other courses as if it were your own!). Additionally, this work should not be used for any commercial purposes, even in modified form. If derivative educational works are created based on my work, I would appreciate attribution (standard academic stuff -- don't plagiarize), and I retain copyright for any parts created by me.
And when in doubt, ping me and ask! If I do grant any notable exceptions to any individual/ organization, I'll make note of it here.
One final note -- some portions of the notebooks in Class 2 and 3 were provided as a part of an assignment template (listing assignment requirements, etc), so I recognize that these portions are probably technically UW property. This also includes the data in the "Data" directories. If I hear from them about tweaking the contents of this repo/ taking anything down, I am happy to comply.