* Office Hours held via Zoom

Computational social scientists increasingly need to grapple with data that is either too big for a local machine and/or code that is too resource intensive to process on a local machine. In this course, students will learn how to effectively scale their computational methods beyond their local machines. The focus of the course will be social scientific applications, ranging from training machine learning models on large economic time series to processing and analyzing social media data in real-time. Students will be introduced to several large-scale computing frameworks such as MPI, MapReduce, Spark, and OpenCL, with a special emphasis on employing these frameworks using cloud resources and the Python programming language.

Prerequisites: CAPP 30121 and CAPP 30122, or equivalent.

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 content in these modules on the Canvas course site. Each module consists of a series of asynchronous lectures, readings, and assignments, which will make up a portion of the instruction time each week. If students have any questions about the asynchronous content, they should post these questions in the Piazza forum for the course, which they can access by clicking the "Piazza" 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, please visit the GitHub Course Repository, available here.

Additionally, students will attend short, interactive Zoom sessions during regular class hours (starting at 9:10 AM CDT) on Mondays and Wednesdays meant to give them the opportunity to discuss and apply the skills they have learned asynchronously and receive live instructor feedback. Attendance to the synchronous class sessions is mandatory and is an important component of the final course grade. Students can access the Zoom sessions by clicking on the "Zoom" tab on the left side of the screen on the Canvas course site. 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.

Students will also virtually participate in a hands-on Python lab section each week, meant to instill practical large-scale computing skills related to the week’s topic. These labs are accessible on the Canvas course site and related code will be posted here in this GitHub repository. In order to practice these large-scale computing skills and complete the course assignments, students will be given free access to UChicago's Midway Research Computing Cluster, 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 presentation).

For their final project (due December 11, 2020), students will write their own large-scale computing code that solves a social science research problem of their choosing. For instance, students might perform a computationally intensive demographic simulation, collect and analyze large-scale streaming social media data, or train a machine learning model on a large-scale economic dataset. Students will additionally record a short video presentation about their project in the final week of the course. 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 2:00pm, the following percentage points will be deducted based on the time stamp of the last commit.

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 other students' work will result in a zero grade and potential further academic discipline. If you have any questions about citations and references, 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.

Any suggestions for how we might further such objectives both in and outside the classroom are appreciated and will be given serious consideration. Please share your suggestions or concerns with your instructor, your preceptor, or your program’s Diversity and Inclusion representatives: Darcy Heuring (MAPSS), Matthias Staisch (CIR), and Chad Cyrenne (Computation). You are also welcome and encouraged to contact the Faculty Director of your program.

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 disabilities@uchicago.edu) and the instructor as soon as possible.

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