Syllabus

Learning Goals/Outcomes:
This is the first course in the data analysis sequence in the DAV program.

Students will learn fundamental concepts related to data analysis and visualization in context. We will focus on three key ideas: a critical view of data practices, the logic of analysis, and data management in Python. We will relate theory-informed views of data to some practical skills in working with data.

The sociological perspective on data practices is intended to give students a broad understanding of the context in which many of us will spend our careers: data and society. We’ll discuss how society is in the code, and how code is in society.

After taking this course, students will:
1. understand the fundamental principles of ethical use of data;
2. understand the benefits and limitations of some common types of publicly available data;
3. master basic data acquisition and management skills.
4. acquire familiarity with a data analysis tool, such as Python, including basic data visualization techniques.

The course will be hands-on and applied, including an ongoing reflection of the relationship between data analytics and significant social processes and institutions.

Method of Assessment:
In-class activities will be low-stakes writing and classroom discussion connected to the assigned readings and relevant contemporary events. Credit will be assigned for participation in the class meeting (on Zoom in fall 2023) or through the Slack channel.

Weekly assignments will be done individually and will include writing and documenting code using the tools included in the course. We will focus on Python code. Students can complete the tasks with other tools if they wish by prior arrangement. A three point rubric will be employed: 3, exceeds expectations; 2, meets expectations; 1, fails to meet expectations.

The project may be done individually or in a group. It will involve identifying a data source (which might be a combination of datasets) that relates to a research question of your choosing. You’ll apply the seven principles from Data Feminism to this data source and give a critical reflection of the data with regard to your question. Part of this critical reflection will be a plan for analysis that would allow you to answer your research question in the context of a critical view of these data that illustrates the logic of inquiry. The project will contain four assignments: a proposal in which the question is identified and explained, a draft of the project, the project report posted to the course site at the end of the term, and reflective comments on other students’ projects.

Grading:
My goal is to help you achieve your learning goals for the course. I’ll ask you by the end of week two to identify those goals and explain how they relate to the four learning goals for the course specified here. At the end of the semester, I’ll ask you for a summary assessment: how well did you achieve your learning goals? As part of this assessment, I’ll ask you to reflect on the course in the form of a letter to future students; in the letter you can discuss what you think worked in the course and what didn’t, as well as things that you would have liked to be included, etc. I’ll create a page on the site for these letters (without identifying info) so that future students can see them.

If you do not complete these assessment tasks, I’ll have to assign a grade based on my best estimation of how well you’ve achieved the four course goals based on the work you turn in.