- Built a Alexa Skill quiz for a NASA mini-conference.
- Built internet connected pumpkin for Halloween that talked to small children.
- Assembled a Raspberry Shake, a personal seismometer.
- Deepened my D3.js skill-set and made several interactive data visualization, including dashboards, at work.
- Visualized network traffic of internet connected devices on an IoT lab network.
- Presented a talk on the changing data visualization landscape in large organizations (1974-2016).
- Competed in a machine-learning contest to predict well log facies put on by a geophysics journal.
- Created an augmented reality webpage / business card using AR.js, which leverages three.js, aframe.js, and ARtoolkit.
- Used SVM machine-learning approach to identify direct returns, reflections, multiples, and coherent noise in seismic gathers as part of a Geoscience Hackathon organized by Agile Scientific and Total. I participated virtually and the rest of my team was physically present in Paris.
Currently working on
- Continue using and exploring different visualization software and libraries in order to know the right tool for each problem and be able to advise others.
- Expanding React.js skills.
- Deepening my experience in Python back-end development, using postgreSQL and Flask.py more.
- Learning the R language and the process for building and deploying RShiny apps.
- Learning natural language processing for future projects.
- Improving my system admin skills for work.
- Using Docker for various projects to ensure standard behavior across different deployment locations and easier configuration.
- Building an application that can take any google forms results csv, pick the right charts, and create a data visualization in which interacting with the chart of results for any question filters the charts for every other question.
- Building a “where science happens in Houston” map the leverages web-scraping and machine-learning.
How did I get started?
My interest in coding resulted from my activities as a geologist. Although most of my time was spent on purpose-built software, sometimes I needed to do things that didn’t quite fit into standard software models or were annoyingly time intensive. I also ran up against memory limits in Excel on large datasets. Learning to code presented a way around these hurdles.Once I put aside time for learning to write code in a more series manner, and not just editing example snippets run inside ArcGIS and other software packages, I found a set of classes taught by Rice Professors and offered online through Coursera to be very helpful. Course syllabi are available at this link and this link. More general information about this sequence of python courses can be found here.I took:
An Introduction to Interactive Programming in Python (Part 1) . Certificate earned on April 10, 2015
An Introduction to Interactive Programming in Python (Part 2). Certificate earned on May 14, 2015
Principles of Computing (Part 1) by Rice University on Coursera. Certificate earned on October 17, 2015
I’ve written a blog post here about all the different ways to starting learning how to write code. Update: since I first wrote this, I’ve talked to several people about this topic. It seems most aren’t sure where to start, how much time needs to be put aside, or how much they’ll like writing code. For those people, I’ve been directing them to code.org or codeschool.com as those options present a zero setup way to quickly dive and see what you like or don’t like with minimal initial investment of time or frustration.