To kick off my blog, I’d like to share my workflow as a researcher in the urban field, focusing primarily on the technical aspects. While I don’t have formal training in computer science (e.g., earning a C.S. or D.S. degree), I do have a strong foundation in econometrics and statistics—by taking extensive coursework at USC Price. My workflow has been shaped by and largely benefited from my experience as a research assistant with Prof. Geoff Boeing and coursework with Prof. Nic Duquette.
Several tools form the backbone of my workflow. While they may seem daunting at first, they’ve proven to be incredibly valuable and a worthwhile investment of time in the long run:
- Git (version control)
- GitHub (collaboration and code hosting)
- Visual Studio Code (all-in-one IDE for python, stata, mardown, and LaTeX)
- Zotero (reference management)
- Stata (econometrics) — I know you need to pay, but I learned my stats in stata and it is still the most convenient one to run most of the ‘popular’ econometrics.
- LaTeX (typewriting, hard to start but perfect for style and version control).
There are several tools that I gave up or intentionally avoided:
- R (It can be very helpful for certain purposes, but I find it less powerful than Stata for stats task and less general-purpose than Python for data science tasks)
- Jupyter Notebook (I ony use it for visualization purposes)
- Overleaf (I find it slow to compile large files and lack freedom to customize the LaTeX environment, however, it is useful for collaboration and quick sharing)
- Notion (I find it has a good intention but not a good execution. I prefer just keep my reading notes in Zotero, my project notes in .md files in the project file, and other notes in the Note app.)
From there, I also build a GitHub project template that includes all the necessary folders and files to get started quickly. You can check it out here.