Author Archives: sbgass

Serendipitously, I recently found myself signing up to volunteer for the Dutch climate change non-profit, Justdiggit. I’ll be helping out with the team that focuses on farmer management practices to improve tree cover on and around farmland in central Tanzania. These programs focus on teaching farmers the benefits of having trees on their land asContinue reading “Paper Reading – An unexpectedly large count of trees in the West African Sahara and Sahel (2020)”

In April, a classmate and I competed in the student competition at the 2021 UPSTAT Conference, a local research conference on applied statistical methods. This year’s theme was on applied social science for social justice in policing. In addition to holding presentations by researchers from Cornell, University of Texas in San Antonio, and the UniversityContinue reading “Gold Medal Winner in Data Analysis Competition”

The following paper was written and submitted by a classmate and I to the 2021 UPSTAT Conference data analysis competition. Introduction In following the example set by the Austin’s Office of Police Oversight (OPO) annual traffic reports on racial profiling, we aim to assess the present disparities in traffic enforcement and provide recommendation for addressingContinue reading “Data Analysis Project: Calculating Racial Disparity in Austin Policing”

The Twitter API is a great way to get started with Data Mining and NLP using real-world data. Everything that is publicly available on the Twitter App is free and easy to collect and analyze. And the best part is that it’s completely free! I highly recommend following this excellent article which contains sample codeContinue reading “How to access the Twitter API with python”

Academic Data Science curricula are unsurprising but disappointing in their stubborn omission of cloud technologies. It’s expected that academia would stick to theoretical knowledge and avoid teach any vendor-specific skills, but it’s disappointing because basically every entry-level software engineering job requires these two things: experience working in a CI/CD framework and experience deploying production-ready codeContinue reading “Studying Google Cloud’s “Data Scientist / Machine Learning Engineer learning path””

Intro Satellites track more than just the weather. In an upcoming class project, I’m going to be working with Google Earth Engine to combine various open-source satellite datasets for predictive analysis, so I wanted to spend a little time getting more familiar with the platform. Google Earth Engine is an incredible tool that pulls open-sourceContinue reading “Visualizing Air Pollution over Houston, TX”

9/24/2021 I had a busy week, mostly filled with interviews and a class project. Unfortunately, “busy” doesn’t always mean “productive” on the things I’d like to be working on, but anyway, here are some of the highlights: I had 5 job interviews this week. I’m graduating in December, so I’m currently looking for full-time positionsContinue reading “What I’m working on this week”

9/17/2021 This will be another quick and informal update. These are some of the things that I’m working on right now. I finished two courses in Google Cloud’s Data Scientist / Machine Learning Engineer learning path. These were the Coursera course called Google Cloud Big Data and Machine Learning Fundamentals and the Qwiklabs skill badgeContinue reading “What I’m working on this week”

In a previous article, I introduced my Machine Vision course project in which I trained a U-net deep neural network architecture for building extraction from satellite images over Khartoum, Sudan. The U-net model performed image segmentation quite well, after which I developed a post-processing pipeline to evaluate the model on building detection, in which itContinue reading “Project: Using Deep Learning for Automatic Building Footprint Extraction from Satellite Imagery (Part 2)”

I recently read 97 Things Every Data Engineer Should Know: Collective Wisdom from the Experts by a collection of data engineers and CEOs, young and old. The advice and overall quality was hit or miss, but it did provide some useful perspective on where the Data Engineering industry came from and where it’s heading. ForContinue reading “Books Review: 97 Things Every Data Engineer Should Know”