This site is currently hosted on GitHub Pages (source code here) and serves as the landing page for my #100DaysOfCode documentation. Though the UI will continue to improve as project time allows, my primary focus is not to grow as a front-end developer - I am dedicated to data. My projects will vary (and I do feel it's important for data scientists to be familiar with web design/development) but should generally have data science and machine learning at their core.

Part of my motivation for this endeavor is to ensure that I am coding out in the open, as too much of my development has been on private projects.

  • Commit to at least 60 minutes per day of coding visibly - public projects held in public repos.
  • Document #100DaysOfCode activity daily.
  • Publish notes (with link to project repo) on GitHub Project Page for each project.
  • Organize projects by domain: Data Science / ML, Web Dev, Community Engineering, Fundamentals / Katas, etc.
  • Contribute generously, shooting for more than just commits. My pull requests and issues have increased, but I'd like to do more code reviews!
  • No need to tweet about it daily, just document it here and tweet as usual.*

*Note: The official challenge site (100daysofcode.com) has recommendations and resources for anyone wishing to undertake similar endeavors. They recommend tweeting about progress daily, but I'm choosing to tweet weekly or as needed.

  1. 2018_09_10: Monday -
    • Commit to #100DaysOfCode project,
    • tweet link to calendar.
  2. 2018_09_11: Tuesday -
    • Published goals/guidelines,
    • created my user profile on StackOverflow,
    • ran through get_earliest from Python Morsels,
    • wrote and deployed a Pelican static site to its GitHub Project Pages gh-pages branch,
    • registered a .design domain (free with code SHOPTALK),
    • installed Unity3d on my local machine for possible machine learning animation projects and Octave for Neural Network course projects,
    • started working on the data.world platform,
    • registered to audit Machine Learning (Stanford University - Andrew Ng) and Neural Networks for Machine Learning (University of Toronto - Geoffrey Hinton) on Coursera, &
    • completed week 1 of Univ. of Toronto's Neural Networks course,
    • published an overview of this project's purpose on my professional portfolio site.
  3. 2018_09_12: Wednesday -
  4. 2018_09_13: Thursday -
  5. 2018_09_14: Friday -
  6. 2018_09_15: Saturday -
    • Wrote backpropogation (with perceptron learning rule) into from-scratch neural net in Octave (learn_perceptron.m) and Python (learn_perceptron.py),
    • added css animations to fade-in page elements, &
    • worked on styling responsive thumbnails without Javascript.
  7. 2018_09_16: Sunday -
  8. 2018_09_17: Monday -
    • Added aria attributes to Markdown elements on Cleveland PyLadies website for accessibility,
    • started a public Kaggle kernel for analysis of my 2018 GitHub commit history,
    • completed multiple linear regression and random forest regression on my kernel for Kaggle's Housing Prices - Advanced Regression Techniques competition (multiple linear regression is performing better so far), &
    • forked the SimpleCV repo for tinkering after several hours' worth of failed attempts to get the source library to read OpenCV files.
  9. 2018_09_18: Tuesday -
    • Got my SimpleCV install to work(!!) by going the Raspberry Pi route - and by forcing IPython 4,
    • began making changes to my fork of the SimpleCV repo in order to try addressing dependency issues,
    • successfully fixed bugs in SimpleCV/Shell/Shell.py that prevented the package from opening after installation,
    • started a repo for my Arduino projects with some of my Hello World sketches as placeholders,
    • ...