Analysis with Programming

I decided to read this book since I've been doing maps using R . Hence it is better to learn the literature and science behind mapping and how to do a proper analysis on it. In addition, I would like to see what Python can offer in this discipline. The book has 10 chapters contained in a 364 pages. The first three chapters was a long reading, not much on coding, but rather on discussions of introduction to Geospatial Analysis. Impression: I like the idea that the author spent three chapters talking about the overall story (I would say) of Geospatial Analysis. Just a preview, the first chapter is of course the introduction; second is the data types, which surprisingly has a variety of formats; and third is all about the libraries and packages used in the said study. I am familiar with ArcGIS and QGIS , but this book lets you aware with other tools as well. The simple illustration that complements the discussion is very helpful in telling the overall story of the subject. ...

I have been reading this book since last week, and now I want to share my thoughts about it. I was excited to review this because I've never heard most of the tools it features, like OpenRefine, MongoDB, and MapReduce. The book has 360 pages and surprisingly it covers a lot of topics. Along with that, is the Github repository for all the codes.  Practical Data Analysis is all about applications of statistical methodologies on computer science. I find it very useful since this was not taught in my statistics class. In college, we only practice statistics on fields like sociology, psychology, agriculture, economics, chemistry, biology, industrial engineering, and many others, but we were not onto computer science, we only deal with it when coding in R or SAS. Hal Varian once said in this video that, . . . we've got at least hundred statisticians on Google . . . And I was curious about that, I mean, what are they doing on Google? What are the statistical tools d...