Course Description

Quarter 2 of the capstone sequence focuses on executing on group project proposals and presenting your work to a variety of audiences, in a variety of formats.

Course Components

Lecture (data science methodology)

In quarter 2, lecture will focus on project management, group work, visually representing material, and giving presentations. Lecture will be minimal, as you will be focused on your project.

Discussion (domain)

As in quarter 1, discussion section will focus on your domain. Every week, a representative from each group will give a ‘weekly check-in’: they will present their week’s work to the rest of their domain (see weekly check-in assignment).

The weekly check-in gives students an opportunity to practice talking about technical material, solicit feedback from their mentor, and learn from other team’s successes and obstacles. As such, attendence in discussion each week is mandatory, like in quarter 1.

Course Deliverables

See the page on Q2 assignments

Assessments and Grades

The course grade will be computed using the following proportions:

Component % of Grade
Checkpoints 10%
Participation 10%
Primary of {Report, Webpage, Product} 45%
Secondary of {Report, Webpage, Product} 20%
Final Code Artifact (Methodology) 10%
Final Presentation 5%

Note: the ‘default’ primary output is a project report and the ‘default’ secondary output is a wepage (e.g. blog). If your project output is something else, be sure that’s stated in your proposal and that this output has been cleared by your domain mentor!

Grading Policy

Implementing a consistent grading scheme for work in such a diverse collection of areas is helped by both a clear rubric and a coarse grading scheme.

  • Each assignment will have a (generally applicable) grading rubric that will help guide your grading.
  • Each assignment will be graded using a coarse schema that reflects broad checkpoints that students met. This schema helps maintain focus on large, impactful things that students can improve on and should reduce grading disaggreements.

The grading scheme for assignments in the course are given on an A/B/C/F scale (without plus/minus). Generally, these grades reflect the following criteria (credit: Shannon Ellis),

Grade Criteria
A (4.0) Accomplishes the task accurately, completely, and clearly. Code is clear, effective, and efficient. Written component is concise, at the appropriate level, and correct. Oral component (when applicable) is effective both visually and explanation; is within the time limit.
B (3.0) Accomplishes the task well, but lacks some completeness or clarity. Code runs but lacks some aspect of clarity, effectiveness, and or efficiency. Written component is logical and generally correct, but lacks either clarity or accuracy. Oral component (when applicable) is moderately effective and/or slightly outside the time window.
C (2.0) The task is somewhat accomplished, but lacks significantly when it comes to completeness and clarity. Code present but does not accomplish the task up to the standards of a data science graduating senior. Written component lacks substantial clarity/correctness. Oral component (when applicable) significantly lacks effectiveness/clarity.
F (0.0) The task largely remains unaccomplished. Code lacks completeness, structure, and is unclear. Written component lacking required information to understand what you did and/or your results. Oral component (when applicable) is nonsensical/unclear.

Final grades will be computed using the grade-points above, using the proportions given in the course components table. Letter grades will be assigned using the standard university cutoffs.

Collaboration Policy and Academic Integrity

In DSC 180, we expect you to work hard and engage with material that originates outside the academic walls. All ideas and work must be your own, that of your approved group, or properly cited. Act with integrity and don’t cheat.

In DSC 180 you are encouraged to use outside resources to help with your work. However, you must properly cite any concepts, writing, or code that originates from other sources. If you are unsure of whether something needs a citation, it’s best to:

  • consult the domain expert for your section, and
  • follow the examples in course readings.
  • place code citations with the relevant link in comments.

The following activities are considered cheating and ARE NOT ALLOWED in DSC 180 (this is not an exhaustive list):

  • Using or submitting either writing or code acquired from other students (except your partner, where allowed).
  • Not properly citing ideas, writing, or code acquired from outside sources. (Citations are a good thing!)
  • Having any other student complete any part of an assignment on your behalf.
  • Completing an assignment on behalf of someone else.

The following activities are examples of appropriate collaboration and ARE ALLOWED in DSC 180:

  • Discussing the general approach to understanding or solving a problem.
  • Talking about debugging/cleaning strategies or issues you ran into and how you solved them.
  • Using outside material with proper citations (including StackOverflow code!).