Grading

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

      • Application of machine learning to solve scientific challenges

      • Scalable analytics

      • Data visualization

      • Domain specific workflows 

    • Length: 2-3 pages

  • The general guidelines for mentoring are:

    • Set up a time to talk with the professors