OSCON is over now. So much fun. Sad to see it over.
My Ignite talk went well. I appreciate the complements that I received.
Ignite OSCON 2013 (playlist): http://www.youtube.com/watch?v=DfmEgB1zW6o&list=PL055Epbe6d5ZfLyERAIRXVCATx9BUP3-Q via @youtube
Numerical Analysis for Statisticians by Kenneth Lange 2010
Although this book doesn’t have any code in in it, it is still useful. The theory and equations are well defined and easy enough to read.
I went to a talk on FFT and Python at OSCON 2013. Sound Analysis with the Fourier Transform and Python, given by Caleb Madrigal.
Chapter 19 on Fourier Transforms goes along nicely with the talk. Caleb presented the formulas and talked about which ones to use. This book gives you all the details you need for choosing formulas and libraries when implementing Fourier Transforms.
A Short History of Random Numbers, and Why You Need to Care given by Matthew Garrett, was another talk that I went to. Chapter 22 Generating Random Deviates is a nice over view of some of the material covered in the talk.
In general this is a good book, I just wish that it had some code examples, pseudo code, algorithms etc. It is not easy to take equations and turn them into code.
Author Daniel K Lyons published by Pakct Publishing
I wish that his book would of been available when I first started using PostgreSQL, it would of saved me a lot of trouble.
The Installation instructions are straight forward. The quick start section has clear SQL instructions.
Top 9 features you need to know about covers, things like properly storing passwords, encryption using pgcrypto and backup and restore which are necessary for all databases.
Wow this works sweet. Thank you Six Sigma with R.
I have an Excel worksheet that I need to analyze. They are not always to smoothest thing to read into R.
I just downloaded and used XLConnect. First try exactly is what I wanted.
wb <- loadWorkbook(“toyprob.xls”)
data.toyprob <- readWorksheet(wb, sheet = 3)
this side is the object <- what it is assigned to
I am presenting at Ignite OSCON 2013. Is There a Cat in Here, Data Mining with Toys. I am busy working on my slides. It is difficult to condense data mining into 20 slides in five minutes. I am having fun doing this. I have lots of great pictures for my slides. Books that I have been using for the theory and practice of data mining are: The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer Press. And one that I now owe the library fines on, Introduction to Algorithms by Thomas Cormen
I spoke at Open Source Bridge this year. My slides
Study Design OSB – https://docs.google.com/presentation/d/1DK2Y7SWKyNljORjHY8KaAsU3o72zo52uS9wkRa-gs94/edit?usp=sharing
Open Source Bridge is a great conference. We were sad to see it over. Registration is already open for next year. I hope to see you there. http://opensourcebridge.org/
Instant R Starter from Packpub.com is a book that I wish was available awhile ago. The book has information that I had to dig for when I needed it. Special values (NA, NaN, INF) NA is missing number. NaN not a number. And how they are used in R.
Clear directions on working with vectors. How vectors can be used as arguments of functions.
A clear concise book that is the missing piece. It covers R programming with code and examples of loops and how to make your own functions. The missing piece in my library of R books.
Six Sigma with R
Statistical Engineering for Process Improvement
Cano, Emilio L.; Martinez Moguerza, Javier; Redchuk, Andrés
Publication year 2012
I am a six sigma black belt. Six Sigma with R is a straight forward book that seamlessly matches my other Six Sigma books. The R code is understandable and easy to reuse. I am using the book to help me write a talk for Open Source Bridge 2013.
I did a session at bar camp 7 Portland. I brought a plastic bin of toys and asked the question Is there a cat in here? Talked and demoed how we would go about this. It is very slow to inspect each item and verify if it is a cat. First how would we know if we had a cat? We concluded that a cat had four legs, a head and fur. Took samples out of the bin and classified them into groups. Showed different types of classification trees, including discussion on red-black trees. Members of the group discussed their big data issues and sorts. Like coming up with an inspection criteria that allows you to make large cuts at the beginning and never look at that data again. We got thru 80% of the toys and concluded that there wasn’t a cat in the bin