I will argue here that standardizing the formatting and styling of code on a team is not immaterial to building and deploying high quality software. Though I give examples using Python and GitHub Actions, the logic is applicable for any other language and CI/CD toolset.

I had a project recently where I created a private software package and hosted it on Azure Artifacts, a similar platform to Pypi or Github for distributing packages. There was a scenario where I needed to pass values from Azure Library into Docker.

During and shortly after my undergraduate education, I tried a few different career paths that didn’t work out. Eventually, I felt like GIS could be a way to continue using my environmental degree while pursuing a renewed interest in tech. This was very much true- with some caveats. To be successful in GIS, you should be able to code and have some knowledge in statistics. During my graduate education, I took as many computer science and programming-centric courses I could- and I still felt like there was much, much more to learn. But, it was enough to get me through the door.

Earlier today, I used for the first time the Power BI REST API to access some reports in my company’s workspace. This API can be used with Windows PowerShell. After spending a couple hours familiarizing myself with both PowerShell and the API, I think I’ve finally got the hang of it.

The applications for hosting an R Shiny web application on the cloud are huge, and learning to work with cloud services in general is critical for data analysts, engineers, and scientists. Dashboards that rely on cloud-hosted data do not need to be constantly redeployed; as long they point to the right file on S3 (or Redshift or RDS database), your Shiny application will use the most recently available data. This has the potential to reduce overhead and provide near-real time access to information for your clients.

This collaborative project between myself and Priyanka Verma seeks to gain insights from particulate matter air pollutant trends in the New York Metropolitan Area. Specifically, we look at particulate matter that is less than 2.5 micrometers (PM2.5), and only data obtained during winter months, due to the higher level of PM2.5 during that season. We developed this in a way that should be easy to reproduce and understand.