• Location: 1155 E. 60th Street, Rm. 140C
• Time: Monday/Wednesday Lecture from 4:30-5:50 PM (CT), Friday Lab Section from 4:30-5:50 PM (CT)
• Canvas Course Site

Computational social scientists increasingly need to grapple with data that is too big and code that is too resource intensive to run on a local machine. Using Python, students in this course will learn how to effectively scale their computational methods beyond their local machines -- optimizing and parallelizing their code across clusters of CPUs and GPUs, both on-premises and in the cloud. The focus of the course will be on social scientific applications, such as: accelerating social simulations by several orders of magnitude, processing large amounts of social media data in real-time, and training machine learning models on economic datasets that are too large for an average laptop to handle.

Prerequisites: MACS 30121 and MACS 30122, or equivalent (e.g. CAPP 30121 and CAPP 30122). Note that this the accelerated version of MACS 30113.

This course is structured into several modules, or overarching thematic learning units, focused on teaching students fundamental large-scale computing concepts, as well as giving them the opportunity to apply these concepts to Computational Social Science research problems. Students can access the readings, assignments, and resources for each of the class sessions within these modules on the Canvas course site. If students have any questions about the course content, they should post these questions in the Ed Discussion forum for the course, which they can access by clicking the "Ed Discussion" tab on the left side of the screen on the Canvas course site. To see an overall schedule and syllabus for the course, as well as access additional course-related files (which we will walk through in in-class activities), students should visit (and clone/fork) the GitHub Course Repository, available here.

During regular class hours, we will meet for a mixture of lecture, group activities, and in-class coding exercises related to the topic for the day. Attendance to the class sessions is mandatory and is an important component of the final course grade. Students should prepare for these classes by reading the assigned readings ahead of every session. All readings are available online and are linked in the course schedule below (and in the corresponding module on Canvas). Starting in Week 2, there will also be an additional TA-led lab section on Fridays from 4:30-5:50 PM CT (in our regular classroom), focused on providing additional practice with scalable computing strategies.

In order to practice scalable computing skills and complete the course assignments, students will be given free access to on-premise cluster computing resources, Amazon Web Services (AWS) cloud computing resources, and DataCamp. More information about accessing these resources will be provided to registered students in the first several weeks of the quarter.

There will be an assignment due at the end of each unit (3 in total). Each assignment is worth 20% of the overall grade, with all assignments together worth a total of 60%. Additionally, attendance and participation will be worth 10% of the overall grade. Finally, students will complete a final project that is worth 30% of the overall grade (25% for the project itself, and 5% for an end-of-quarter video presentation).

Grades are not curved in this class or, at least, not in the traditional sense. We use a standard set of grade boundaries:

• 95-100: A
• 90-95: A-
• 85-90: B+
• 80-85: B
• 75-80: B-
• 70-75: C+
• <70: Dealt on a case-by-case basis

We curve only to the extent we might lower the boundaries for one or more letter grades, depending on the distribution of the raw scores. We will not raise the boundaries in response to the distribution.

So, for example, if you have a total score of 82 in the course, you are guaranteed to get, at least, a B (but may potentially get a higher grade if the boundary for a B+ is lowered).

If you would like to be graded on a Pass/Fail (P/F) basis, send a private message to the course staff on the Ed Discussion forum before the Final Project is due. A total score of 75 and above in the class will qualify for a "P" in the class.

We expect all students to participate in each class session in person (having read all of the readings listed for the day ahead of class time). Your participation grade (10% of your overall grade in the class) will be based on your engagement and completion of in-class activities.

If, for whatever reason, you cannot attend a class session, send a private message to the course staff ahead of the class session on the class Ed Discussion forum. We will evaluate these requests on a case-by-case basis and assign an alternative assignment to make up participation credit for the day.

For their final project, students will write large-scale computing code that solves a social science research problem of their choosing. For instance, students might perform a computationally intensive demographic simulation, or they may choose to collect, analyze, and visualize large social media data, or do something else that employs large-scale computing strategies. Students will additionally record a short video presentation about their project. Detailed descriptions and grading rubrics for the project and presentation are available on the Canvas course site.

Unexcused Late Assignment/Project Submissions will be penalized 10 percentage points for every hour they are late. For example, if an assignment is due on Wednesday at 11:59pm, the following percentage points will be deducted based on the time stamp of the last commit in your private GitHub assignment repository.

If, for whatever reason, you need an extension on an assignment deadline, send a private message to the course staff ahead of the assignment deadline on the class Ed Discussion forum and we will evaluate these requests on a case-by-case basis.

Academic honesty is an extremely important principle in academia and at the University of Chicago.

• Writing assignments must quote and cite any excerpts taken from another work.
• If the cited work is the particular paper referenced in the assignment, no works cited or references are necessary at the end of the composition.
• If the cited work is not the particular paper referenced in the assignment, you MUST include a works cited or references section at the end of the composition.
• Any copying of work other than your own will result in a zero grade and potential further academic discipline.
• If we discover that you have shared or posted questions/solutions from any class assignments or exams in a public, online space, this will also result in a zero grade and potential further academic discipline.
• You are permitted to consult with an AI Assistant, such as ChatGPT or GitHub Copilot, as you work on assignments (just note that these tools will not always produce the most scalable or up-to-date responses). If you do use an AI Assistant, you must submit a complete log of the prompts that you used and properly cite the use of the AI tool. To get the most out of this class, we recommend that you only use an AI Assistant in the following way:
  • First, write code/text on your own without AI assistance
  • Turn to your AI Assistant if you are stuck and have run out of ideas on how to proceed. Some areas where AI Assistants can be helpful are in idea generation, syntax correction, and providing alternative (potentially more scalable) solutions
  • Critically evaluate AI responses and assess their strength (by consulting course materials and/or writing formal tests for code)
  • Rewrite code/text from scratch (in your own voice) to consolidate what you have learned

If you have any questions about citations, references, or what constitutes plagiarism, consult with your instructor.

The University of Chicago is committed to diversity and rigorous inquiry from multiple perspectives. The MAPSS, CIR, and Computation programs share this commitment and seek to foster productive learning environments based upon inclusion, open communication, and mutual respect for a diverse range of identities, experiences, and positions.

This course is open to all students who meet the academic requirements for participation. Any student who has a documented need for accommodation should contact Student Disability Services (773-702-6000 or [email protected]) and the instructor as soon as possible.

Note that there will also be weekly lab sections in Weeks 2-9 on Fridays from 4:30-5:50pm CT in our normal classroom.

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