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Gailmard Statistical Modeling and Inference for Social Science
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Freedman, Pisani and Purves Statistics
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OpenIntro Statistics https://www.openintro.org/stat/textbook.php?stat_book=os
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John Hopkins
- Peng Report Writing for Data Science in R https://leanpub.com/reportwriting
- Caffo Statistical Inference for Data Science https://leanpub.com/LittleInferenceBook
- Caffo Regression Models for Data Science in R https://leanpub.com/regmods
- Peng Exploratory Data Analysis with R https://leanpub.com/exdata
- Peng R Programming for Data Science
- Leek The Elements of Data Analytic Style https://leanpub.com/datastyle
- Peng and Matsui The Art of Data Science https://leanpub.com/artofdatascience
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Allen Downey, Think Stats, http://greenteapress.com/thinkstats/
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Shasha and Wilson Statistics is Easy
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Reinhardt, Statistics Done WRong
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Gelman and Hill Data Analysis Using Regression and Multilevel/Hierarchical Models
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Grolemund and Wickham, *R for Data Science page, [github] , page.
Other
- Gandrud Reproducible Research in R and RStudio http://www.amazon.com/exec/obidos/ASIN/1498715370/7210-20
- Xie Dynamic Documents with R and knitr
- Nolan and Lang Data Science in R: A Case Studies Approach
- Zumel Practical Data Science in R
- Kabakoff R in Action
- Grus Data Science from Scratch
- Chang R Graphics Cookbook
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Harvard Gov 1000/2000/2000e/Stat E-190: Quantitative Research Methodology. Matthew Blackwell. http://www.mattblackwell.org/files/teaching/gov2000-syllabus.pdf
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UC Berekeley, PS 231A: Quantitative Analysis in Political Research, Sean Gailmard. PhD course using SMISS. https: //www.ocf.berkeley.edu/~gailmard/syl.ps231a.pdf
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SOC 504 Spring 2015: Advanced Data Analysis for the Social Sciences. site. It would be equivalent to 503 or 510, but it uses github and is influenced by online courses.
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JHU Data Science specialization on Coursera. Source for the courses is on github. It has 9 courses and a capstone project.
- Data Scientists' Toolbox
- R Programming
- Getting and Cleaning Data
- Exploratory Data Analysis
- Reproducible Research
- Statistical Inference
- Regression Models
- Practical Machine Learning (not relevant)
- Developing Data Products (not relevant)
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Sta 101: Duke (undergrad) but innovative in pedagogy. Mine Cetinkaya-Rundel: Fall 2015 site, github. Sta 104 Data Analysis and Statistical Inference site.
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UBC Stat 545: Data Wrangling, exploration and analysis with R. by Jenny Bryan site, github.
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Software Carpentry courses
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John Hopkins, Winter R Booktcamp, Sean Cross. R programming. Also uses Swirl examples.
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Brenton Kenkel, Vanderbilt PSCI 8357 https://github.com/vanderbilt-psci8357/00-hello-world.git (closer to 503)
Also look at the classes listed in Aronow's intro stats book proposal: http://aronow.research.yale.edu/aronowmillerproposal.pdf
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Data viz
- ggplot2
- ggExtra
- https://bchartoff.shinyapps.io/ggShinyApp/
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Data wrangling
- dplyr
- tidyr
- broom
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Stats: ????
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Data IO
- foreign
- readr
- readxl
- haven
- rio
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Clients
- WDI
- countrycode
- Quandl
- dvn
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Presentation
- rmarkdown
- stargazer
- xtable
- pander
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Shpariro Social Science Code and Data https://people.stanford.edu/gentzkow/sites/default/files/codeanddata.pdf
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Code Review For and By Scientists: http://arxiv.org/abs/1407.5648
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Nine Simple Ways to Make it Easier for Others to Reuse Your Data: https://peerj.com/preprints/7/
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Best Practices for Scientific Computing http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745. Slides
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Software Carpentry Lessons Learned http://f1000research.com/articles/3-62/v1 Slides
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Workflows and project organization
- What do DataCarpentry, SoftwareCarpentry and Stat 545 suggest?
- Noble Quick Guide to Organizing Computational Biology Projects. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000424
- Project Template https://cran.r-project.org/web/packages/ProjectTemplate/index.html
- http://nicercode.github.io/blog/2013-05-17-organising-my-project/
- http://stats.stackexchange.com/questions/10987/what-are-efficient-ways-to-organize-r-code-and-output
- http://www.r-statistics.com/2010/09/managing-a-statistical-analysis-project-guidelines-and-best-practices/
- http://www.r-statistics.com/2010/09/managing-a-statistical-analysis-project-guidelines-and-best-practices/
- http://www.stat.ubc.ca/~jenny/STAT545A/block01_basicsWorkspaceWorkingDirProject.html
- http://blog.revolutionanalytics.com/2010/10/a-workflow-for-r.html
- https://www.reddit.com/r/bioinformatics/comments/3rdlhf/help_how_do_you_organize_your_projectsfiles/
- http://stackoverflow.com/questions/3759723/best-way-to-organize-bioinformatics-projects
- http://christianlemp.com/blog/2014/02/05/How-I-Manage-Data-Projects-with-RStudio-and-Git.html
See course list here
- Introduction to R: Cover as first
- Intermediate R Probably not needed in this course. Loops, functions, apply family, utilities (dates, regex). Some of these useful, but may be better for 503.
- Data Manipulation with dplyr Definitely cover in 501
- Data Analysis in R, the data.table way We're going to use dplyr in 501/503.
- Reporting with R Markdown Useful tutorial to R markdown
- Introduction to Statistics Some of the tutorials may be useful. Intro, Student's t test, Analysis of Variance, Repeated measure Anova, Correlation and Regression, Multiple regression
- How to work with Quandl in R
- Data visualization in R with ggvis Ignore. Going to use ggplot2 for 501/503. Will move to ggvis relatively soon as it matures.
- Data Analysis and Statistical Inference Cetinkaya-Rundel's course. Possibly some interest.
- EdX Texas Foundations of Data Analysis, Part 1: Statistics Using R and Part 2: Inferential Statistics
The O'Reilly Codeschool very simple intro to R: http://tryr.codeschool.com/
- Data Analysis
- Exploratory Data Analysis
- Getting and Cleaning Data
- Statistical Inference
- Regression Models
- OpenIntro Stats
- Devil in the Details http://www.nyu.edu/projects/beber/files/Beber_Scacco_The_Devil_Is_in_the_Digits.pdf. Beber and Scacco on Iran election (Hattip: Dartmouth GOV10)
- Gledisch and Ruggeri (2010) "Political opportunity structures, democracy, and civil war" JPR http://jpr.sagepub.com/content/47/3/299.full.pdf (Hattip: Dartmouth GOV10)
- Loewen, Koop, Settle, and Fowler (2014) "A Natural Experiment in Proposal Power and Electoral Success" AJPS http://onlinelibrary.wiley.com/doi/10.1111/ajps.12042/abstract (Hattip: Dartmouth GOV10)
- Panagopoulos (2013) "Extrensic Rewards, Intrinsic Motivation, and Voting" JOP http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=8820778&fileId=S0022381612001016 (Hattip: Dartmouth GOV10)
- Gerber, Green, and Larimer (2008, APSR) (Hattip: Harvard GOV 2000)
- Acemoglu, Johnson, and Robinson (2001, AER) (Hattip: Harvard GOV 2000)
- Nunn and Wantchekon (2011, AER) (Hattip: Harvard GOV 2000)
- Fisman and Miguel "Corrupation, Normals and Legal Enforcement: Evidence from Diplomatic Parking Tickets" http://emiguel.econ.berkeley.edu/research/corruption-norms-and-legal-enforcement-evidence-from-diplomatic-parking-tickets
- Chen "The Effect of Language on Economic Behavior" AER https://www.aeaweb.org/articles.php?doi=10.1257/aer.103.2.690
- Titanic Dataset
- Challenger Dataset
- Gapminder Data
- ANES
- OpenEvent Data; ACLED
- Polity - look at index
- Hainmueller and Hiscox, 2010. "Attitudes toward Highly Skilled and Low-skilled Immigration: Evidence from a Survey Experiment" APSR http://dx.doi.org/10.1017/S0003055409990372 In SMISS
- Beissiger 2002 Nationalist Mobilization and the Collapse of the Soviet State. Data is here. Used in SMISS
- Reinhardt and Rogoff, 2010. "Growth in a Time of Debt" AER 10.1257/aer.100.2.573 In SMISS
- Herndon, Ash, and Pollin, 2014. "Does High Public Debt Consistently Stifle Economic Growth? A Critique of Reinhardt and Rogoff" Cambridge Journal of Economics Data here In SMISS