Lifecycle of Data Science Project

The Lifecycle Summarized

1. Identify & Define the problem
2. Define and document data sources
3. Statistical data profiling
4. Implementation
5. Sharing and collaboration
6. Maintenance & Support

http://www.datasciencecentral.com/profiles/blogs/life-cycle-of-data-science-projects

Plus M&Ms, Jackknife (Swiss Army Style?) logistic and linear regression.
http://www.datasciencecentral.com/profiles/blogs/jackknife-logistic-and-linear-regression

Plus jackknifing your results in @ 4 lines of R code.
http://ryouready.wordpress.com/2008/12/19/r-jackknife-the-coefficients-of-a-linear-regression-model/

Random Forests in Tableau with R
http://boraberan.wordpress.com/2014/02/07/decision-trees-in-tableau-using-r/

Finally, using a jackknife to cut down some Hidden Decision Trees.
http://www.datasciencecentral.com/profiles/blogs/hidden-decision-trees-revisited