A rough compendium of resources that cover data science topics (a.k.a. statistics, econometrics, actuarial science, etc.). These will cover general theory, methodology, applications, and R tools and methods. The listing is not intended to be comprehensive, but will be resources I find as part of my projects or random happenstance. The emphasis will be on web resources, although published texts will also be included where appropriate.
An overview of doing good work
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Sharon Machlis' "How do I?" table of common R tasks
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- including linking to GitHub
The specific topics thus far:
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Business analytics (a catch-all for the quantification, analysis, and modeling of organization performance)
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Raking (also known as iterative proportional fitting procedure, or IPFP; uses include weighting survey responses to accurately match the population proportions)
Protecting the anonymity and confidentiality of individuals (whether the data are from administrative records or surveys) is essential.
The methods associated with this protection are sometimes refered to by the umbrella topics of "statistical disclosure control" and "data masking".
You've done a cracker-jack job with your statistics, and made some bang-up charts, graphs, and other visualizations. Now how do you tell the world?
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Design elements -- including colours, fonts, etc.
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Effective presentations -- what makes an effective presentation, and R tools for making slide decks
This work by Martin Monkman is licensed under a
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Canada License.