posts

As I have gone from one job interview to the next, I have come to the realization that I have been exploring quite a wide-range of industries building strategic, data-informed decision making to their operations. So instead of burying my pre-interview research prep inside a notebook fated to be forgotten inside a drawer, I’m posting those here for posterity. This one is on the Renewable Energy Market.

Atomic Habits by James Clear is one of the most well-known productivity books out there, and for good reasons: it’s a straightforward and easy read with intuitive insights about transforming the habits we take for granted into deliberate behavior. If you’ve heard of the concept of how the compounding effect of 1% daily improvements yields a return of doing something 37 times better after a year (whereas doing something worse by 1% over that same time period results in a 3% decline), it was probably regurgitated from this book.

[Currently a work in progress]. This project is a major data portfolio piece within the framework of my data mentorship at SharpestMinds: a comprehensive analytical report delivered via Jupyter Book on the activities of Lyft’s bike-share service, Bay Wheels, in the city of San Francisco from 2019 to 2021. This project showcases my mastery of the ETL process as I deconstruct the data dimensionalities of a large dataset analyzing temporal, spatial, consumer, and revenue trends.

Comprehensive summary of Breathing Between the Lines, a project connecting socio-economic determinants of health, local monitoring, and the legacy of redlining for the city of Seattle, WA.

Code repository of “Breathing Between the Lines”.

This project was presented and recorded on August 25th, 2020 for ParsonsTKO’s Data for Social Impact: Racial and Economic Justice Conference. The presentation’s material and recording can be accessed at https://parsonstko.com/data-for-social-impact-conference/

Reflection about maintaining professional relationships.

An introduction to K-Nearest Neighbors with the scikit-learn library.

A tutorial on spatial intersects in Python, using an overlay of PurpleAir’s sensors as datapoints with Seattle’s city limits as a polygon.

An introduction to APIs in Python with OpenAQ, within the scope of the TechSoup and ParsonsTKO Summer 2020 Data Strategy Mentorship Program.

A map of local resources which includes the location of pop-up food banks, lunch distribution for San Francisco K-1 thr 12 students, public pit stops and hand-washing stations, and dedicated hours of selected supermarkets and grocery stores for senior and immunocompromised folks. The map’s database is available and downloadable as a Google Spreadsheet.

Pool patronage tally for 2019 using Plotly Dash (location redacted).

Indexing my python pandas code snippets and tribulations into one notebook for your viewing pleasure. This cheatsheet is dedicated to data cleaning problems (field type conversions, splitting values from fields, etc etc.)

ArchiTech uses publicly available data to run zoning analysis scenarios on a property in San Francisco. Working in a team of five, my role was to gather, edit, and clean GIS data (using Python Pandas, and using some SQL in QGIS). I learned how to use MapBox’s API to render 3D visualization of a target property model. More info on the project can be found on Devpost.

I made this tutorial over the course of my internship at Caravan Studios, a subdivision of TechSoup Global, on how to assemble a low-cost micro-sensing fine particle detector using open-source software.

One of my anxieties of living in the San Francisco Bay Area over the past 10 years has revolved around “The Big One”: the next great earthquake born out of the San Andreas’ fault, one equivalent or greater than the 1906 event which left most of the city in ashes. However I have taken for granted a more common place type of disaster: fires.

This post is based on content presented at the Association of Environmental Professionals’ 2018 CEQA Essentials Workshop, which took place in Oakland on November 7th.

My cohort’s final capstone project from my 2018 experience at the UC Berkeley Extension’s Data Analytics program. Honestly a bit of a mess in which I pretty much had to figure out–on my own–how to build and interpret predictive Machine Learning models (I settled with a logistic regression and KNN model). The results were…a lesson in the pitfalls of ML engineering, but I did learn a lot!

Written for HIP Consult Inc, a Washington D.C. consulting firm specialized in telecommunication contracting in the Global South. This post is about the impact of fiber optic networks on healthcare services, with a focus on Malawi as a case study.

Over the course of my internship at National Geographic’s Education Division, I created a set of maps and written content on staple food crops using datasets from the Food and Agriculture Organization of the United Nations. The map layers can be accessed and visualized directly on the MapMaker Interactive.

A blog post I wrote during my internship at National Geographic’s Education Division on Tianducheng, a city emblematic of China’s dramatic—and at times bizarre—urbanization.

A short think-piece about the role of geospatial information in everyday life for the Berkeley Scientific Journal’s blog.