Main Project (Group Submission)
  1. Grading is subjective: Insight over charts/tables
  2. Rubrics are weighted according to appropriate project context
    For example, project where clustering is inappropriate and data fitting is critical, points move from data fitting to clustering
  3. + indicates: must have (if appropriate, see previous point)
    * indicates: extra points (desirable, but do not try to artificially introduce)
  1. data:
    1. + characterize: incomplete, uncertain, outliers
    2. + filter/scrape/clean (complexity gets * )
    3. * large data sets
    4. * streaming data sets
    5. + combine different data sets
  2. hypotheses
    1. + clearly stated
    2. + devise numerical experiment(s) to confirm or deny
    3. * causality vs correlation
  3. fit data
    1. + regression, MLE, learning (increasing points)
    2. * prediction
  4. other data science techniques
    1. + cluster data
    2. ...
  5. presentation.
    1. * engaging visual representation of data
    2. + conclusions

Submit:
  1. supporting material that allows us to verify your work:  code or worksheet(s), overview, url or other means to access the data you used
  2. presentation material:  4 minute video presentation of your hypothesis and findings based on data science techniques. (hint: see the grading sheet below)
    Use zoom or youtube and send the url  so I (not you) can start it in class.
  3. A grading sheet (editable)
  4. A writeup of 2 pages or less (verbosity is not a criterion) that allows you to explain or expand on points that may be unclear when viewing just (b) and (c).

Presentation:
  1.      4 min video  
  2. group will come to the black board during the video
  3.      2 minutes questions