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.

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?

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.

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?

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"