Syllabus, emails, readings, slides

[briatte]

Slides

Current size of slide sets (cap at 25, except Week 1). Revise readings, practice sessions and exercises, and include screenshots of videos when relevant.

• 1. 37 -- OK, cap at ~ 40
• 2. 24 -- OK
• 3. 33 -- slightly too long, cut down a bit
• 4. 25 -- OK
• 5. 17 -- expand (description, sampling) -- add 'how to get help online' for Exercise 5
• 6. 20 -- expand a bit (association) -- cover bootstrapping, Bayesian reasoning?
• 7. TODO (correlation) -- cover bivariate OLS geometry
• 8. 15 -- expand (regression)
  • expand further with slides specifically on regression output (use SRQM)
• 9. 16 -- expand (logit) -- cover nonlinearity via LOESS, splines?
• 10. TODO (surveys) -- cover data, again, data wrangling, again, weighting
• 11. 30 -- slightly too long, cut down a bit (classification)
• 12. 11 -- expand a bit

Syllabus

• Port essentials from the current syllabus from PDF to Google Docs Done.
• Continue moving session recaps from syllabus to DSR-outline-2.txt to GitHub README and emails
• Fix Import the best IDA examples #28
• Possibly use stuff from EMSS-emails-2013
  • Pandoc template
  • ‘Bilan’ section
  • Examples
    • https://github.com/briatte/aad/tree/master/ess4-israel
    • ESS5 France
    • Reinhart and Rogoff
    • Piketty and Saez
• Look at more old stuff esp.
  • QUANTI1-2020
  • QUANTI2-2019
• Store the emails online: GitHub wiki? emails/ folder?

Other courses and tutorials

Make better use of great tutorials:

• https://inductivestep.github.io/R-notes/
• Vegetti, surveys
• https://www.statology.org/tutorials/ (stats)
• https://www.statology.org/r-guides/ (R)
• https://ourcodingclub.github.io/tutorials.html
• UCLA: https://stats.oarc.ucla.edu/r/
• https://m-clark.github.io/documents.html (more advanced models)
• Tidy models: https://www.tmwr.org/ (actually a book, recommend in Session 11?)
• David Robinson's videos
• Julia Silge's videos

Finish digging into (and reorganising…) those:

• https://r-bootcamp.netlify.app/ (biologists)
• https://ready4r.netlify.app/labbook/ (biologists)
• http://post8000.svmiller.com/lab-scripts/ (poli-sci)
  • http://post8000.svmiller.com/lab-scripts/logistic-regression-lab.html (logit)
• https://grodri.github.io/glms/ (and his other demography courses perhaps)
• https://chester.rbind.io/teaching/
• https://rudeboybert.rbind.io/
• https://rudeboybert.github.io/MATH116/problem-sets/ (math/stat)
• https://www.amelia.mn/STAT220labs
• QUANTI1-courses folder
• QUANTI1-handbooks folder
• QUANTI2-courses folder
• QUANTI2-handbooks folder
• DSR-handbooks folder
• DSR-courses folder
• Any other 'courses' folder…

Possible additions for the wiki:

• https://github.com/UChicago-pol-methods/
• https://github.com/UChicago-pol-methods/IntroQSS-F22
• https://github.com/UChicago-pol-methods/plsc-40502-statistical-models
• https://github.com/UChicago-pol-methods/plsc-30600-causal-inference

Paper to turn into an exercise

Amelia McNamara, Nick Horton, "Wrangling categorical data in R," citing her website:

Wrangling categorical data in R, a paper co-authored with Nick Horton. This paper describes some common mistakes data analysts make when working with categorical data (factors) in R. The paper was published jointly in The American Statistician, Vol. 72, Issue 1 and as a pre-print in the Practical Data Science for Stats collection on PeerJ.

• Final exercise is great. Use it.
• Cite in readings.

Readings

Finalize the list:

• Finalize
  • Sync emails with list (working backwards…)
• Document readings in slides, with "(on Google Drive)" markers when relevant
• List (almost) all material mentioned in emails and slides
• Establish session-per-session roadmap
• Copy to syllabus Link to wiki in syllabus

Handbooks:

• Make a choice between Irizarry and Rodrigues, (1) assigning both is too much, (2) too much overlap, (3) need space to assign more from Wickham and Grolemund
• Add Gelman et al. chapters
• Add Fogarty - Quantitative Social Science Data with R. An Introduction (2019) (videos and screencasts)
  • (Week 1, ‘Introduction to R and RStudio’) videos, screencasts
  • (Week 3?) Finding Data, Finding Data
  • (Week 3) Data Management, Data Management
  • (Week 3, again) Variables & Manipulation, Variables & Manipulation
  • (Week 4) Visualising Data, Visualising Data
  • (Week 5) Univariate & Descriptive Statistics, Univariate & Descriptive Statistics
  • (Week 6, ‘Hypothesis Testing’) videos, screencasts
  • (Week 7) Bivariate Analysis, Bivariate Analysis
  • (Week 8, 7 perhaps if easy) linear Regression & Model Building, Linear Regression & Model Building
  • (Week 8) OLS Assumptions & Diagnostic Testing, OLS Assumptions & Diagnostic Testing
  • (Week 12, wrap-up) https://study.sagepub.com/fogarty/student-resources/r-screencast-tutorials/chapter-13-putting-it-all-together
• Add Baumer et al. -- https://mdsr-book.github.io/mdsr2e/
• (Re)add Peng? (probably not) (nope, use Irizarry instead)
• Add 'ModernDive' book, Statistical Inference via Data Science to main readings
  • Session 1: https://moderndive.com/1-getting-started.html
  • Session 4: https://moderndive.com/2-viz.html
  • Session 3: https://moderndive.com/3-wrangling.html
  • Session 5 (glossary): https://moderndive.com/A-appendixA.html
  • Session 5 (concepts, illustrated): https://moderndive.com/B-appendixB.html
  • Session 5 (perhaps): https://moderndive.com/7-sampling.html
  • Session 5(also, also perhaps): https://moderndive.com/8-confidence-intervals.html
  • Session 6: https://moderndive.com/9-hypothesis-testing.html
  • Session 8 (or 7 perhaps): https://moderndive.com/5-regression.html
  • Session 8, again: https://moderndive.com/6-multiple-regression.html
  • Session 8, again, perhaps: https://moderndive.com/10-inference-for-regression.html
• Add stuff from syllabus to slides or readings/roadmap
  • Add 'Quanti. Soc. Sci. Data' (3)
  • Add R Graphics Cookbook (4)
  • Add CRAN Task Views (12)
  • Add StackOverflow (and CrossValidated) (5, after instructions for first exercise)
• Document optional scientific articles (the readings are numbered in ref. to the sessions, but listing them all in the syllabus and/or the wiki should be enough)
  • (1) Amelia McNamara, Key attributes of a modern statistical computing tool: The American Statistician, Vol. 73, Issue 4, preprint
  • (1) McCullough, B.D. and Yalta, A.T. 2013. “Spreadsheets in the Cloud – Not Ready Yet,” Journal of Statistical Software 52(7). doi:10.18637/jss.v052.i07
  • (3) Broman, K.W. and Woo, K.H. 2018. “Data Organization in Spreadsheets,” The American Statistician 78: 2–10.
    doi:10.1080/00031305.2017.1375989 -- https://kbroman.org/dataorg/
  • (3) Ellis, S.E. and Leek, J.T. 2017. "How to Share Data for Collaboration," PeerJ Preprints 5:e3139v5.
    doi:10.7287/peerj.preprints.3139v5
  • (3) Wickham, Tidy Data, Journal of Statistical Software, 2014
  • (4) Wickham, A Layered Grammar of Graphics, Journal of Computational and Graphical Statistics, 2010
  • (4?8?) Wickham et al, Visualizing Statistical Models.
    Published in Statistical Analysis and Data Mining.
  • (8) Schrodt, P.A. 2010. “Seven Deadly Sins of Contemporary Quantitative Political Analysis.” APSA 2010 Annual Meeting. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1661045
  • (8) Kam, C. and Franzese, R. 2007. Modeling and Interpreting Interactive Hypotheses in Regression Analysis. University of Michigan Press, ch. 2 (‘Interactions in social science’) and 3 (‘Theory to practice’). https://www.press.umich.edu//206920
  • (10) Damico, surveys: https://journal.r-project.org/archive/2009/RJ-2009-018/index.html
  • (10) Gelman, A. 2007. “Struggles with Survey Weighting and Regression Modeling.” Statistical Science. http://www.stat.columbia.edu/~gelman/research/published/STS226.pdf
  • (11) Breiman, L. 2001. “Statistical Modeling: The Two Cultures.” Statistical Science. https://doi.org/10.1214/ss/1009213726

[briatte]

Hey @kantunez

I'm close to being done with my readings list:

https://github.com/briatte/dsr/wiki/Readings

I still need to integrate the few things listed above in this issue, but most of the content is there.

Some of the links come from your own material.

Feel free to use how you see fit!