Open Intro - free introductory statistics handbook.

Beautiful project, free books that can be used with free R and R studio software, suitable for highschools and colleges: https://www.openintro.org/stat/textbook.php?stat_book=os

Book for holidays for Data Miner?

The story of Bayesian theory and its applications? Sharon Bertsch McGrayne: "The Theory That Would Not Die" | Talks at Google .

Villard noise cancelling shortwave antenna loop demo - Don't quit SWL ov...

Not only I tried it :-) My attempt: https://www.youtube.com/watch?v=qGipnTk-fyk&feature=youtu.be&a=



 I wonder if it can also transmit. I know about high voltage between the capacitor created by overlapping foil. Would it withstand 3W of power from a CW homemade QRP transceiver on 7MHz?

Here a transmitting loop made of copper tube. https://www.youtube.com/watch?v=Cv_RnLpZ9gw
Note the use of coax to form a capacitor and tune the antenna. In fact the entire antenna can be made just from the coax.

Another solution: http://www.rfa.org/english/about-old/help/Anti-Jamming.html?searchterm:utf8:ustring=antenna

R: Braindump for this week: classification, ETL, etc.

1. I am pondering about the use of clustering to classify unknown data.

• first prepare data (what to do with categorical, ordinal?, mutate - create new variables?)
• for this i found some interesting tutorials:
  http://www.r-bloggers.com/clustering-mixed-data-types-in-r-2/
  http://www.sthda.com/english/wiki/partitioning-cluster-analysis-quick-start-guide-unsupervised-machine-learning
• I also found an algorhitm that proved to be very precise:
• then run through a clustering algorytm (k-means, local density, other?)
• first I chose to play with various algorythms to see how they detect known clusters
  eg. the three species of famous iris database, I played with "cclust", "cluster", and "densityclust" and discovered "factoextra"for quick graphs/diagnostics
• finally throw out a decision tree (or other non-black box tool to show rules and dependencies).

2. Getting out tidy data: broom (in .r documents), pander (in .Rmd documents), memisc:mtable + pander to compare linear models (http://stackoverflow.com/questions/24342162/regression-tables-in-markdown-format-for-flexible-use-in-r-markdown-v2)

3. ETL concept:
https://cran.r-project.org/web/packages/dplyr/vignettes/databases.html
https://github.com/beanumber/etl
http://www.r-bloggers.com/r-and-sqlite-part-1/

Free books: Exploring Data Science by John Mount and Nina Zumel

https://dzone.com/articles/practical-data-science-with-r

TED: Peter donnelly shows how stats fool juries

Especially starting from 11th minute... reminds me about the need to review the measures of the model: specificity, sensitivity... more here: https://en.wikipedia.org/wiki/Sensitivity_and_specificity

xgboost, caret, cleaning, time series - a reading list for next weeks.

Go through this to learn xgboost:
https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/discoverYourData.Rmd
https://cran.r-project.org/web/packages/xgboost/vignettes/discoverYourData.html
https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd

https://github.com/dmlc/xgboost/tree/master/R-package/demo
https://rpubs.com/flyingdisc/practical-machine-learning-xgboost

http://courseprojects.souravsengupta.com/tag/xgboost/
http://xgboost.readthedocs.io/en/latest/R-package/xgboostPresentation.html
https://www.kaggle.com/jkapila/caterpillar-tube-pricing/0-24-with-xgboost-in-r/run/29813
http://www.analyticsvidhya.com/blog/2016/01/xgboost-algorithm-easy-steps/

Material from recent meetup in Warsaw:
https://github.com/mi2-warsaw/SER/blob/master/SER_XIX/xgboost.R


Cleaning and  some interesting packages (editrules, deducorrect):
https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf
http://www.r-bloggers.com/three-quick-and-simple-data-cleaning-helper-functions-december-2013/
http://www.meetup.com/amst-R-dam/events/57161682/ and especially:
http://cran.r-project.org/web/packages/editrules/index.html
http://cran.r-project.org/web/packages/deducorrect/index.html
Very good examples: https://github.com/data-cleaning
 http://www.r-bloggers.com/deductive-imputation-with-the-deducorrect-package/

Time Series:
http://www.r-bloggers.com/time-series-analysis-and-mining-with-r/

Ensemble:
https://cran.r-project.org/web/packages/caretEnsemble/vignettes/caretEnsemble-intro.html

Caret: (very good stuff): https://github.com/pbiecek/DataMining/tree/master/MINI_2015


Other:  
http://www.datasciencecentral.com/m/blogpost?id=6448529%3ABlogPost%3A352821

Github new tools (also offline):
Write a book:  
https://www.gitbook.com/
Collect and share snippets of code:
https://gist.github.com/