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MACS 30200 - Perspectives on Computational Research (Spring 2018)

Dr. Richard Evans Dr. Benjamin Soltoff
Email [email protected] [email protected]
Office 208 McGiffert House 209 McGiffert House
Office Hours W 2:30-4:30pm Th 2-4pm
GitHub rickecon bensoltoff
  • Meeting day/time: MW 11:30am-1:20pm, Saieh Hall, Room 247
  • Graders: Sushmita V. Gopalan & Xingyun Wu
  • 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

Late Problem Sets

Late problem sets will be penalized 2 points 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 -2 points
12:31pm to 1:30pm -4 points
1:31pm to 2:30pm -6 points
2:31pm to 3:30pm -8 points
3:30pm and beyond -10 points (no credit)

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/methods section of paper Slides
Apr 11 W Computational results section of paper Slides
Apr 16 M Kernel density estimation Notes_a PS1
Notes_b
Apr 18 W Interaction terms Notes
Apr 23 M Parallel computing Notes Literature review section
Apr 25 W Workshop papers/office visits
Apr 30 M Missing data Notes PS2
May 2 W Deep learning with Python/R DL ch. 1-2
May 7 M Deep learning with Python/R DL ch. 3-4
May 9 W Deep learning with Python/R Methods/initial results section
May 14 M Workshop papers/office visits
May 16 W Effective presentations, poster,slides Notes PS3
May 21 M Markov and hidden Markov models Notes
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.