MACS 30200 - Perspectives on Computational Research (Spring 2018)

	Dr. Richard Evans	Dr. Benjamin Soltoff	Sushmita Gopalan (grader)	Xingyun Wu (grader)
Email	[email protected]	[email protected]	[email protected]	[email protected]
Office	208 McGiffert House	209 McGiffert House
Office Hours	W 2:30-4:30pm	Th 2-4pm
GitHub	rickecon	bensoltoff	sushmitavgopalan16

• Meeting day/time: MW 11:30am-1:20pm, Saieh Hall, Room 247
• Office hours also available by appointment

Course description

This course focuses on applying computational methods to conducting social scientific research through a student-developed research project. Students will identify a research question of their own interest that involves a direct reference to social scientific theory, use of data, and a significant computational component. The students will collect data, develop, apply, and interpret statistical learning models, and generate a fully reproducible research paper. We will identify how computational methods can be used throughout the research process, from data collection and tidying, to exploration, visualization and modeling, to the final communication of results. The course will include modules on theoretical and practical considerations, including topics such as epistemological questions about research design, writing and critiquing papers, and additional computational tools for analysis.

Grades

Assignment	Points	Quantity	Total points
Proposal	10	1	10
Literature review	15	1	15
Methods/initial results	15	1	15
Peer evaluations of posters	2	5	10
Poster presentation	30	1	30
Final paper	40	1	40
Problem set	10	4	40
Total Points			160

Students will turn assignments in via their own public GitHub repository named MACS30200proj. The directory structure of this repository should be the following.

• github.com/YourGithubHandle/MACS30200proj
  • ProblemSets
    • PS1
    • PS2
    • PS3
    • PS4
  • Proposal
  • LitReview
  • MethodsResults
  • Poster
  • FinalPaper

Disability services

If you need any special accommodations, please provide us with a copy of your Accommodation Determination Letter (provided to you by the Student Disability Services office) as soon as possible so that you may discuss with me how your accommodations may be implemented in this course.

Course schedule (lite)

Date	Day	Topic	Reading	Assignment due dates
Mar 26	M	Overview/reproducibility in science	Slides
Mar 28	W	Abstract/intro/conclusion	Slides
Apr 2	M	Theory section of paper	Slides
Apr 4	W	Proposal presentations		Proposal slides & present
Apr 9	M	Data section of paper
Apr 11	W	Computational results section of paper
Apr 16	M	Kernel density estimation		PS1
Apr 18	W	Interaction terms
Apr 23	M	Parallel computing		Literature review section
Apr 25	W	Workshop papers/office visits
Apr 30	M	Deep learning with Python/R		PS2
May 2	W	Deep learning with Python/R
May 7	M	Deep learning with Python/R
May 9	W	Deep learning with Python/R		Methods/initial results section
May 14	M	Workshop papers/office visits		PS3
May 16	W	Effective presentations, poster,slides
May 21	M	Markov and hidden Markov models
May 23	W	Markov and hidden Markov models
May 28	M	No class (Memorial Day Holiday)		PS4
May 30	W	In-class poster presentations		Poster
Jun 6	W	Final papers due at 5:00pm		Papers due

References and Readings

All readings are required unless otherwise noted. Adjustments can be made throughout the quarter. Be sure to check this repository frequently to make sure you know all the assigned readings.

• Interaction terms
  • Brambor, T., Clark, W. R., & Golder, M. (2006). Understanding interaction models: Improving empirical analyses. Political analysis, 63-82.
• Deep learning with Python/R
  • Chollet, F. (2017). Deep learning with Python. Manning Publications.
  • Chollet, F. with J.J. Allaire (2018). Deep learning with R. Manning Publications.