pjbull / benchmarked

Data Analysis of Supreme Court Rulings

Hello, friends 👋

I'm Peter. Nice to meet ya.

📈 Data science + machine learning 📊

I largely help social sector organizations get their data into a shape where machine learning can be valuable. Much of this work ends up on drivendata.org, where you can join a competition to help these organizations, learn from interesting data, try new methods, and make friends that care about impact. Here are some cool recent ones:

• Detect building heights and angles in satellite imagery
• Predict who is likely to get a flu vaccination
• Identify memes that are "hateful" based on both the text and the image
• Forecast geomagnetic solar storms

Competitions are great, but not every problem is a good fit, so our team of data scientists and software engineers also works with organizations directly to analyze data, build data systems, setup pipelines, train machine learning models, and design and deploy solutions. Check out DrivenData Labs to learn more. There I write case studies, publish on our blog, and maintain our open source work.

✨ Open source 📦

You can find me working on open source projects that are tools for data scientists and engineers using Python. I particularly care about reproducible data science and machine learning and AI ethics.

See below for the projects I regularly contribute to!

benchmarked's Issues

chartjunk

Project Website

Here are the requirements for our website:

• summarize the main results of your project and tell a story.
• keep the level of discussion at the appropriate level.
• Your iPython process book and data should be linked
• Also embed your main visualizations
• and your screencast in your website.

Screencast

Here are the requirements for the screencast:

• two minutes with narration
• ipython notebook or slides
• embed it on the webpage
• make sure "your main contributions are front and center"

iPython Process Book

From cs109 site (http://cs109.org/projects/projects.php), these are the requirements for the process notebook:

• Overview and Motivation
  • overview
  • motivation
  • project goals
• Related Work:
  • sources: such as a paper, a web site, or something we discussed in class.
• Initial Questions:
  • What questions are you trying to answer?
  • How did these questions evolve over the course of the project?
  • What new questions did you consider in the course of your analysis?
• Data:
  • source
  • scraping method
  • cleanup, etc.
• Exploratory Analysis:
  • visualizations
  • statistical methods
  • Justify the decisions you made, and show any major changes to your ideas.
• Final Analysis:
  • What did you learn about the data?
  • How did you answer the questions?
  • How can you justify your answers?

Peer Assessment

https://docs.google.com/forms/d/1tf3SApcC5aqG2t6mjasDV82Oiq4hsdVg99oAQL4qZ7I/viewform
It is important to provide positive feedback to people who truly worked hard for the good of the team and to also make suggestions to those you perceived not to be working as effectively on team tasks. We ask you to provide an honest assessment of the contributions of the members of your team, including yourself. The feedback you provide should reflect your judgment of each team member’s:

Preparation – were they prepared during team meetings?
Contribution – did they contribute productively to the team discussion and work?
Respect for others’ ideas – did they encourage others to contribute their ideas?
Flexibility – were they flexible when disagreements occurred?

Submit to Course Dropbox

To submit your projects, create a folder named lastname_firstinitial_project and place your iPython notebook and your other files in this folder. Compress the folder (please use .zip compression) and submit it in the appropriate dropbox folder. If we cannot access your work or links because these directions are not followed correctly, we will not grade your work. You can submit once per team or submit once per team member.