Course Syllabus
Contents
Course Syllabus¶
Description
The project portion of the capstone sequence is cross-listed:
PSTAT 197B (Winter)/197C (Spring)
CMPSC 190DE (Winter)/190DF (Spring)
Students practice their data science and applied statistics skills by completing a hands-on team project on a practical problem proposed by a project sponsor. Students are expected to give regular oral presentations and prepare at least one written report on their research.
Each team will work closely with their sponsor and a project mentor on their research throughout both quarters. In addition, all students will meet once per week for discussions, workshops, and presentations to complmement their project experiences and support their progress in research.
Meetings
(Winter) Mondays 2:00pm – 3:15pm NH 1105 or via Zoom
(Spring) Mondays 2:00pm – 3:15pm in HSSB1173
Weekly project team meetings as determined by project groups
Instructors
Teaching Assistants
Winter schedule
The tentative weekly schedule is indicated below and subject to change based on the progress of the class.
Week |
Topic |
Project updates |
|---|---|---|
1 |
Introductions |
|
2 |
Data science philosophies |
|
3 |
Github repositories and collaborative computing |
|
4 |
Project directory and file organization |
Initial post due |
5 |
Creating team repositories |
|
6 |
Asking good questions |
|
7 |
Data tidying and visualization |
Second post due |
8 |
Storytelling with data |
|
9 |
Communication and collaboration |
|
10 |
Interim presentations I |
|
11 (finals week) |
Interim presentations II |
Spring schedule
Week |
Topic |
Project updates |
|---|---|---|
1 |
Spring term logistics |
|
2 |
The big data paradox |
|
3 |
Mid-quarter presentations I |
|
4 |
Mid-quarter presentations II |
Third post due |
5 |
Mid-quarter presentations III |
|
6 |
Guest lecture: Jessica Santana |
|
7 |
Mid-quarter presentations IV |
|
8 |
Mid-quarter presentations V |
Fourth post due |
9 |
TBD |
|
10 |
Final presentations I |
|
11 (finals week) |
Final presentations II |
Outcomes and Assessments¶
Learning outcomes
Students are expected to fully engage with their project groups and with class meetings. In so doing, students will:
Carry out a hands-on research or industry project involving applications of statistics and data science from start to finish.
Develop and practice effective strategies for collaborative research and for communicating research outcomes.
Think critically about data ethics and the social implications of research.
Assessments
Attainment of these outcomes will be measured by the following group and individual assessments; letter grades will be assigned according to the relative weighting indicated for each assessment category.
Meetings/Participation (individual): 20%
Attendance in project and class meetings
Assigned Reading (individual): 20%
Offer at least one comment (question, answer, thought, etc.) on assigned readings
Deliverables (group): 50%
Written progress updates
End-of-quarter presentations
Sponsor and mentor evaluations (individual): 10%
Based on engagement and effort