CSS 2 Syllabus: Winter 2024#
Course Overview#
This course will teach how you to think about data and modeling for computational social science. This will involve raising and examining questions like:
How is a dataset formatted, and is this the appropriate format for what I want to do?
Is this dataset representative or does it reflect a biased sample?
What ethical considerations should I take into account when obtaining and analyzing data?
What kind of model is the most appropriate for these data?
How do I design and implement these models––ranging in complexity from linear regression to support vector machines?
These topics will be discussed in the context of hands-on practice with real-world datasets in a Python programming environment.
Key Learning Outcomes#
This course is designed to give students a range of conceptual and technical tools. My goal is that by the end, you are able to:
Identify and explain ethical issues that arise in CSS, along with analytical issues more broadly.
Design and implement clear, concise, and accurate visualizations of data.
Propose and test hypotheses about data using statistical models.
Construct statistical models in Python and interpret the results.
Weigh the pros and cons of different model evaluation metrics.
Course Logistics#
Teaching Team:#
Sean Trott: Assistant Teaching Professor in Cognitive Science and CSS.
TAs: Joshua Burrows
Teaching Team OH#
Who? |
When? |
Where? |
|---|---|---|
Sean Trott |
Monday 11-12 |
CSB 259 |
When/Where?:#
Lecture: MWF 9-10am (Podemos 1A18)
Coding Lab Sections (optional):
Monday, 3-4 (CSB 005)
Friday, 10-11 (CSB 005)
Grading#
Grade Components#
Your grade will be determined by three kinds of assessments: coding labs, problem sets, and a final project.
Grade Component |
Percentage of Final Grade |
|---|---|
8 Coding Labs |
50% (6.25% each) |
4 Problem Sets |
32% (8% each) |
1 Final Project |
18% |
Letter Grades#
If you’re taking the course for a letter grade, your grade will be determined according to the scale below.
Note that the number on the right-hand side of the range is not included in that range: that is, an “A-” ranges from 90% all the way to 91.99% but does not include 93% (93% is an A).
Percentage |
Letter Grade |
|---|---|
97%+ |
A+ |
93-97% |
A |
90-93% |
A- |
87-90% |
B+ |
83-87% |
B |
80-83% |
B- |
77-80% |
C+ |
73-77% |
C |
70-73% |
C- |
60-69% |
D |
<60% |
F |
On Rounding#
Note that my policy is not to round up grades for two reasons:
If rounding is applied selectively (i.e., only to students who ask), it is unfair to other students.
If rounding is applied across the board, it simply redefines the boundary between two letter grades (e.g., making an 89% the cut-off for an A-).
Late submissions#
Students may submit late assignments up to 48 hours after the submission deadline, for 75% credit of what you would’ve received (i.e., if you scored 90%, you’d get 67.5% with the late penalty).
Questions, feedback, and communication#
Instructors can be reached in the following ways:
Office hours.
Public question on Piazza.
Private message over Piazza.
Email.
The course Piazza can be found here: https://piazza.com/ucsd/winter2024/602
Note that in general, we prefer communication over Piazza as opposed to email.
Other Information#
Academic Integrity#
Please turn in your own work. While you are encouraged to work together on some assignments (e.g., on labs), you should still understand the code you’ve submitted. Problem sets and final project should be completed independently.
Please review academic integrity policies here. Cheating and plagiarism are unfair to other students and ultimately to yourself, and you will be penalized if caught. Instead, if you’re struggling with something, please come to office hours and ask for help!
Class Conduct#
All of us (instructors + students) should treat others with respect and follow the UC San Diego Principles of Community. This class should be a welcoming and inclusive environment for everyone, regardless of gender, gender identity and expression, sexual orientation, physical appearance, disability, race, or ethnicity.
Please be considerate and respectful of your fellow classmates (and instructors), refrain from discriminatory language and harassment, and interact with good faith.
Schedule of Course Content#
See the schedule here.