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MACS 30200 - Perspectives on Computational Research (Spring 2019)

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 3 30
Total Points 150
  • All assignments related to the final project will be turned in via their own public GitHub repositories. You can create this repository here.
  • Each problem set will be submitted via pull request to individual repositories (e.g. hw01, hw02, hw03)

Late Problem Sets

Late problem sets will be penalized 1 point for every hour they are late. For example, if an assignment is due on Monday at 11:30am, the following points will be deducted based on the time stamp of the last commit.

Example PR last commit Points deducted
11:31am to 12:30pm -1 point
12:31pm to 1:30pm -2 points
1:31pm to 2:30pm -3 points
2:31pm to 3:30pm -4 points
9:30pm and beyond -10 points (no credit)

Disability services

If you need any special accommodations, please provide me (Dr. Soltoff) 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 Slides Assignment due dates
Apr 1 Mon Overview/reproducibility in science Slides
Apr 3 Wed Identifying a research question CR ch 3-6 Slides
Apr 8 Mon Writing the literature review/theory sections Slides
Apr 10 Wed Methods/results section Slides Research proposal
Apr 15 Mon Individual meetings - proposal feedback
Apr 17 Wed Dealing with missingness Notes Slides
Apr 22 Mon Deep learning fundamentals ESL ch 11; DLPyR ch 1-2 Slides
Apr 24 Wed Deep learning fundamentals ESL ch 11; DLPyR ch 1-2 Slides
Apr 29 Mon Deep learning and Keras/Tensorflow ESL ch 11; DLPyR ch 3-4 Slides
May 1 Wed Class cancelled Lit review draft
May 6 Mon Individual meetings - literature review feedback
May 8 Wed Deep learning - texts and sequences DLPyR ch 6 Slides HW01
May 13 Mon Data visualization and evaluating statistical graphs Slides
May 15 Wed Best practices in designing statistical graphics Slides HW02
May 20 Mon Class imbalance (Sushmita) Slides
May 22 Wed Effective presentations Slides Methods/results draft
May 27 Mon No class (Memorial Day)
May 29 Wed No class (out of town) HW03
Jun 3 Mon Writing the abstract/intro/conclusion
Jun 5 Wed Poster session Research poster
Jun 12 Wed Final paper (5pm)

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.