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May 7, 2014

Posted by on This page includes resource to resolve problems using the R statistics language.

“*A collection of useful one-page resources for a data miner, data scientist, and/or a decision scientist. The modules include code, lectures, and one-page recipes for getting things done.*” (Kdnuggets, 2014).

Link: http://www.kdnuggets.com/2014/02/one-page-r-survival-guide-data-science-with-r.html

Kdnuggets. http://www.kdnuggets.com/2014/02/one-page-r-survival-guide-data-science-with-r.html, Available 2014.

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October 8, 2013

Posted by on A big deal of researches are point out the videos of David Mease as a good source for learning statistic applied to Data Mining using R. If you want to try it out, take a look at his play list on Youtube here.

September 26, 2013

Posted by on I took the Mathematical Biostatistics Boot Camp 1 last offering and I found it great. Now a new version of this course Mathematical Biostatistics Boot Camp 2 starts next week and it is an opportunity to continue interacting with great courses.

Information about the course is found below (**retrieved from the course website**).

This class presents fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples. Students having taken this class should be able to summarize samples, perform relevant hypothesis tests and perform a collection of two sample comparisons. Classical non-parametric methods and discrete data analysis methods are discussed. The class is taught at a master’s of biostatistics introductory level and requires Mathematical Biostatistics Boot Camp 1 as a prerequisite.

- Hypothesis Testing
- Power and sample size and two group tests
- Tests for binomial proportions
- Two sample binomial tests, delta method
- Fisher’s exact tests, Chi-squared tests
- Simpson’s paradox, confounding
- Retrospective case-control studies, exact inference for the odds ratio
- Methods for matched pairs, McNemar’s, conditional versus marginal odds ratios
- Non-parametric tests, permutation tests
- Inference for Poisson counts
- Multiplicity