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index.Rmd
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<style>@import url(style.css);</style>
# Introduction to Data Analysis
[François Briatte](http://f.briatte.org/) and [Ivaylo Petev](http://ipetev.org/)
[Sciences Po](http://www.sciences-po.fr/), [Euro-American Campus](http://college.sciences-po.fr/sitereims/)
Spring 2013
This course is an introduction to analyzing data with the [R](http://www.r-project.org/) software. It features some mathematics and statistics as well as some statistical computing and data visualization. You will need a laptop with an Internet connection to follow the class.
To get started, [download the entire course](https://github.com/briatte/ida/archive/master.zip). To take a look at what the course material is made of, [view it on GitHub first](https://github.com/briatte/ida). It's not a large download.
- [Syllabus][syllabus]
- [Introduction][001]
- [Readings][002]
[syllabus]: https://github.com/briatte/ida/raw/master/syllabus.pdf
[001]: 001_intro.html
[002]: 002_readings.html
> __Part 1: Introduction to Statistical Computing__. The course starts by one month dedicated to setting up R and learning its basic functionalities. All course logistics will be discussed in these weeks.
## 1. [Setup][010]
__Readings:__ Kabacoff, ch. 1 and Teetor, ch. 1 and 3.
- [Installing R][011]
- [Installing RStudio][012]
- [Practice][013]
[010]: 010_setup.html
[011]: 011_r.html
[012]: 012_rstudio.html
[013]: 013_practice.html
* * *
## 2. [Objects][020]
__Readings:__ Teetor, ch. 2 and 5.
- [Vectors and matrices][021]
- [Variables and factors][022]
- [Practice][023]
[020]: 020_objects.html
[021]: 021_vectors.html
[022]: 022_variables.html
[023]: 023_practice.html
* * *
## 3. [Functions][030]
__Readings:__ Kabacoff, ch. 5, and Teetor, ch. 8.
<!-- Matloff, ch. 8. -->
- [Control flow][031]
- [Iteration][032]
- [Practice][033]
[030]: 030_functions.html
[031]: 031_control.html
[032]: 032_iteration.html
[033]: 033_practice.html
* * *
## 4. [Data][040]
__Readings:__ Kabacoff, ch. 4, and Teetor, ch. 4 and 6-7.
- [Imports and exports][041]
- [Reshapes and aggregates][042]
- [Practice][043]
[040]: 040_data.html
[041]: 041_dataio.html
[042]: 042_reshaping.html
[043]: 043_practice.html
* * *
> __Part 2: Introduction to Statistical Analysis__. The course continues by showcasing some statistical techniques, from finding clusters of related data to modelling relationships between several variables.
## 5. [Clusters][050]
__Readings:__ Kabacoff, ch. 14, and Teetor, ch. 13.4, 13.6 and 13.9.
- [Heatmaps][051]
- [Principal components][052]
- [Practice][053]
[050]: / "050_clusters.html"
[051]: / "051_heatmaps.html"
[052]: / "052_pca.html"
[053]: / "053_practice.html"
* * *
## 6. [Distributions][060]
__Readings:__ Kabacoff, ch. 6, Teetor, ch. 10, and Urdan, ch. 4-6. See also Urdan, ch. 1-3, if you have forgotten everything about statistics.
- [Descriptive statistics][061]
- [Density functions][062]
- [Practice][063]
[060]: / "60_distributions.html"
[061]: / "061_description.html"
[062]: / "062_density.html"
[063]: / "063_practice.html"
* * *
## 7. [Differences][070]
__Readings:__ Kabacoff, ch. 7, Teetor, ch. 9, and Urdan, ch. 7, 9 and 14.
- [Confidence intervals][071]
- [Significance tests][072]
- [Practice][073]
[070]: / "070_differences.html"
[071]: / "071_intervals.html"
[072]: / "072_tests.html"
[073]: / "073_practice.html"
* * *
## 8. [Models][080]
__Readings:__ Kabacoff, ch. 8 and 11, Teetor, ch. 11, and Urdan, ch. 8, 10 and 13. Skim ANOVA to focus on OLS (simple and multiple linear regression).
- [Linear correlation][081]
- [Ordinary least squares][082]
- [Practice][083]
[080]: 080_models.html
[081]: 081_correlation.html
[082]: 082_ols.html
[083]: 083_practice.html
* * *
> __Part 3: Introduction to Data Visualization__. The course ends by focusing on the graphic dimension of quantitative data. We will also try to have guest speakers to talk about their professional use of data.
## 9. [Visualization in time: Time series][090]
__Reading:__ Teetor, ch. 14. Focus on detrending and read ARIMA only if you plan to earn millions as a financial analyst.
- [Autocorrelation][091]
- [Smoothing][092]
- [Practice][093]
[090]: 090_ts.html
[091]: 091_lags.html
[092]: 092_smoothing.html
[093]: 093_practice.html
* * *
## 10. [Visualization in space: Maps][100]
**Special guest:** [Joël Gombin][jg] on reproducible science.
[jg]: http://www.joelgombin.fr
<!-- __Reading:__ B. Sue Bell *et al.*, "[Current practices in spatial analysis of cancer data mapping health statistics to inform policymakers and the public](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1647272/)", 2006. -->
- [Geocoding][101]
- [Choropleths][102]
[100]: 100_maps.html
[101]: 101_geocoding.html
[102]: 102_choropleths.html
<!-- [Links][103] (read at home)
[103]: 103_practice.html -->
* * *
## 11. [Networks][110]
<!-- __Reading:__ David Siegel, "[Social Networks in Comparative Perspective](http://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=1045&context=pn_wp)", 2011. -->
**Special guest:** [Alexandre Léchenet][al] on data-driven journalism.
[al]: http://alphoenix.net
- [Influence][111]
- [Network(d)s][112]
[110]: 110_networks.html
[111]: 111_influence.html
[112]: 112_networkds.html
<!-- [Links][113] (read at home)
[113]: 113_practice.html -->
* * *
## 12. [Data-driven advances][120]
__Special guest:__ [Samuel Goëta][sg] will speak in the [Distinguished Lecture Series][dls-talk] on open data and open government.
[120]: 120_data.html
[sg]: http://coulisses-opendata.com
[dls-talk]: https://www.facebook.com/events/123760981144191/
* * *
We're done!
__Thanks to__ [Baptiste Coulmont](http://coulmont.com/), [Joël Gombin ](http://joelgombin.fr/) and [Timothée Poisot](https://github.com/tpoisot/RCourse2012) for very valuable advice and comments, to [GitHub](http://github.com/) for hosting and to users at [StackExchange](http://stackexchange.com/) for coding assistance.
Special thanks to the Sciences Po Reims staff and students for indefectible support.
__Inspired by__ [Christopher Adolph](http://faculty.washington.edu/cadolph/index.php?page=22), [Dave Armstrong](http://quantoid.net/ICPSRR.html), [Christopher Gangrud](https://github.com/christophergandrud/Introduction_to_Statistics_and_Data_Analysis_Yonsei/), [Andrew Gelman](http://andrewgelman.com/2012/12/textbook-for-data-visualization/), [Rebecca Nugent](http://www.stat.cmu.edu/~rnugent/teaching/introR/), [Gaston Sanchez](https://gastonsanchez.wordpress.com/), [Cosma Shalizi](http://www.stat.cmu.edu/~cshalizi/), [David Sparks](http://is-r.tumblr.com/) and [Hadley Wickham](http://had.co.nz/ggplot2/).
This course has its own [GitHub repository](https://github.com/briatte/ida); fork at will. This HTML version was compiled from source on `r format(Sys.time(), "%A %B %d, %Y")`.