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Grading the the class is as follows:

  • 20% Homework
    • There are homework assignments for nearly every class
    • All homework is "turned in" on the wiki
    • This means that all answers are open for other students to use
    • If you refer to someone else's homework while doing your own, note that in your assignment!  
      • We encourage this:  homework helps you develop the skill-sets needed for the projects.
      • This is to give the other person credit for assisting you
      • We can tell when you use someone's homework and don't give them credit (and will subtract points)
  • 30% Midterm Project
  • 35% Final project
  • 15% Class participation
    • In class attendance and participation
    • Wiki comments
    • Wiki activity
    • Helping other students
    • Number of other students referring to your homework
    • If you have had extra help from another student, please let us know.
  • Peer-review: No Slacker Policy!
    • Many parts of the class involve group projects.
    • Each member of a group will grade and evaluate teammates
    • These evaluations are the only private part of the class and seen only by the professors
    • Your grade on these projects (homework, midterm, and final) is heavily influenced by your evaluation
    • Your grade on these projects is negatively influenced if you do not submit evaluations of your teammates (you will lose points!)
  • Graduate students:
    • 10% of your final grade is based an:
      • An additional paper
      • Mentoring undergrads
      • Project leadership
    • The general guidelines for this paper are:
      • Be on a class topic to which they directly contributed in the midterm and/or final project
      • Integrate the theme of the class on Frontiers in Massive Data Analysis/Data Driven Science/Data Intensive Science/4th Paradigm
      • Integrate the importance of using cyberinfrastructure
      • Integrate the importance of team science
      • Include references
      • May include reference to specific CI contributed by the student E.g. building specific CI components such as:
        • UA HPC algorithm integration
        • Distributed computing
        • Scalable analytics
        • Data visualization
        • Domain specific workflows 
      • Length: 2-3 pages
    • The general guidelines for mentoring are:
      • Come chat with the professors
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