Algorithms and Computation
8th International Workshop, WALCOM 2014, Chennai, India, February 13-15, 2014, ProceedingsPal, S.P. (et al.) (Eds.)
Publication year 2014 by Springer Press
Interesting collection of papers from WALCOM conference. I would like to have been there. It is interesting to read about current research. Like Top-k Manhattan Spacial Skyline Queries.
A (k+1) Approximation Robust Flow Network Algorithm and a Tighter Heuristic Method using Iterative Multiroute Flow paper by Jean-Francois Buffier and Vorapong Suppakitpaisarn
There are many more insightful papers. I can’t wait to see what get developed next.
The Algorithm Design Manual by Steven S. Skiena published by Springer Press
This is detailed and timeless book that I keep reaching for. A useful book that I can not keep access to long enough. It is a important book to have in your library.
Unlike other algorithm books that I have the algorithms are written independent of any programming language.
The Cartoon Guide to Statistics by Larry Gonick & Woolcott Smith
Flaws and Fallacies in Statistical Thinking by Stephan K. Campbell, published by Dover
How to Lie with Statistics by Darrell Huff
R for Everyone by Jared P. Lander published Addison Welsey 2013
R Statistical Application Development by Example by PrabhanjanNarayanchar Tatter PACKT Publishing 2013
Instant R Starter by Donanto Teutonico PACKT Publishing 2013
Learning RStudio for R Statistical Computing by Mark P.J. van der Loo & Edwin de Jonge PACKT Publishing
Six Sigma with R by EmilioL. Cano, Javier M. Moguerza and Andres Redchuk pubshiled by Springer. Usr R! series
Max Shron wrote Thinking with Data, How to turn Information into Insights. Published by o’Reilly
I requested a review copy of this book because it looked interesting. Math and Philosophy meets data science.
There is no code in this book. It is worth reading because it goes over the concepts concisely. Reasoning and arguments. Examples are timely like does being close to mass transportation increase the cost of renting an apartment?
The book concludes with that the author hopes that in several years the material will be obvious to data scientists and a clear place to start.
R for Everyone Advanced Analytics and Graphics by Jared P. Lander published by Addison Wesley 2013.
A couple quotes from the book. Chapter 11 page 117 “data munging” a term coined by Simple founder Josh Reich. I have been curious about where the term data munging came from. And from Chapter 23 page 359 “The combination of knitr and Rstudio IDE is so powerful that is was possible to write this entire book inside the RStudio IDE using knitr to insert and run R code and graphics. ” Wow, a whole book, we have come a long way since I used EMACS as a word processor and wished EXCEL had a spell checker.
The beginning of the book covers the basics that are good for beginners to know. The most useful Chapter, six on reading data into R.
The book covers many advanced topics, like time series, survival analysis and splines with enough information on packages and code to point you in the right direction.
R Statistical Application Development by Example Beginner’s Guide by
Prabhanjan Narayanachar Tattar 1849519447 published by packtpub.com 2013
This book doesn’t do everything for you. It gets you started on topics covered in each chapter then gives you opened ended problems to solve. It took me awhile to work thru the book. The time for action exercises are worth the effort to puzzle thru and play with. The start of the book is good for beginners. The rest of the book has more advanced topics, like CART and ridge regression.
Rachel Schutt and Cathy O’Neil wrote Doing Data Science, Straight Talk from the Frontline published by O’ Reilly 2013
The book describes and prescribes how to do Data Science. It isn’t a how to manual, the book isn’t for beginners. In the there are plenty of references to good beginner materials, many which are reviewed in this blog. The R and Python code provides examples of how to go about doing data science.
I received a review copy of this book. I am very pleased to have read it. The book How to do Data Science succinctly describes topics that I have been trying to get across to people. Chapter 2 has excellent information about Populations and Samples in Big Data. Chapter 16 covers Next-generation Data Scientist, Hubris and Ethics, a good topic to include.
The book came out of a class, I would of liked to have been in the class.
R by Example by Jim Albert and Maria Rizzo. Published Springer Press 2013
The thoughtfulness of this book demonstrates the authors statement that this book was written to answer students questions.
Data sets used are varied, old and newer. Including horse kicks to Prussian army officers(my great,great grandpa Peter was in the Prussian Army) and Chapter 13.1 estimating when will Sam meet Annie from Sleepless in Seattle, using Monte Carlo method for computing intervals.
Chapter 3.4 shows how to make a contingency table in R. Something that I wish there was a good package for.
Chapter 11 on Simulating Experiments tells where the term Monte Carlo came from then continues on to show by example how to implement the code.
11.5 on Patterns of dependence in a sequence has good information and R code for computing the significance of a streak. Demonstrated with winning streaks in baseball.
Appendix A covers arrays, vectors and matrix.
This is something that I keep tripping up on in R programming. True is all caps when used as a logic operator. Same with false. Type TRUE when I want to know if something is true or set it to true. Same for false, type FALSE. And don’t leave the caps lock on.