Practical DevOps

Practical DevOps by Joakim Verona published by Packt Publishing 2016

I am taking a DevOps class thru Hack Oregon. I found this book useful and recommended it to my class. We are learning how to use Ansible to provision and this book was most helpful. Chapter Seven has code to do Ansible and Docker together. I am working on getting this to work.

Vagrant UP?

I am taking a DevOps class. We are using vagrant. Saturday I lost my box. I typed vagrant up on the command line in a terminal window and nothing happened. I was thinking it would pop up like web servers do.
The command that I was missing was vagrant ssh.  This command ssh (secure shell) into the virtual box.

vagrant provision command allows you to make changes and add things like games to your virtual box.

Useful vagrant commands:

vagrant up

vagrant provision

vagrant ssh

Python Data Science Essentials

Authors: Alberto Boschetti and Luca Massaron published by Packt April 2015.

I am a Data Scientist who usually codes in R. It was a challenge to get comfortable  enough in python code to review the book. Python come in a lot of flavors.  I used Anaconda Launcher to run jupyter notebooks. The code is on the publishers page.

With broad strokes in six chapters it cover the fundamentals of Data Science using python. The pretty blue mosaic tile swirl on the cover catches your eye.

My favorite chapter is chapter five on Social Network Analysis. I like the table on graph types, node and edges. For example Twitter, a directed graph, people are nodes and followers are edges. Very useful table for writing code.

Get the code, run the notebooks, have fun.



Mastering Social Media Mining with R

Mastering Social Media Mining with R

Sharan Kumar Ravindran September 2015 Packt publisher

Useful R book that covers current Social Media and  data science techniques.

My favorite library in this book is from chapter six, SocialMediaMineR.

The function get_facebook from SocialMediaMineR package takes a URL and returns a data frame of shares, likes etc.  The function is easy to use. You do not need OAuth just a link. Works like this:

> library(SocialMediaMineR)
> get_facebook(“”)
trying URL ‘’
Content type ‘¸’
ýþ’ length 256338 bytes (250 Kb)
opened URL
downloaded 250 Kb

url normalized_url
share_count like_count comment_count total_count click_count
1 432 361 155 948 0
comments_fbid commentsbox_count
1 10150745127795008 0

This one function could keep you occupied for a long time.

But there are other useful libraries in this book: ROAuth for OAuth, twitterR for Twitter, Rfacebook for facebook, and rgithub for github.

The book covers exploratory data analysis, EDA. in the chapter on github.

Sentiment Analysis in the chapter on Twitter.

The book briefly covers a lot. There are many other books that cover a single topic in more detail. Read this book to discover what you want to explore.


Building a Recommendation System with R

Written by  Suresh K. Gorakala and Michele Usuelli, published by Packt Press 2015

This is whole book on a topic that is often only a single chapter in a book. It is a book for people who already know R and machine learning .

The book uses Math equations not just code for teaching the concepts.

Covers confusion matrix for classification. Along with sensitivity and specification.  Lots of details about type one and type two errors. This  clearly written section will help you understand why you don’t want either type of error and what they are.

Classification similarity measures include Euclidean Distance, Cosine Distance and Pearson Correlation.

Dimensionality  reduction techniques include Principle Component Analysis.

Data Mining techniques include K-means clustering and Support Vector Machine.

Recommender System includes collaborative filtering and content based filtering.


R package for the book is recommenderlab.

recommenderlab: Lab for Developing and Testing Recommender Algorithms by Michael Hahsler at

Other packages used are lsa, e1071, cluster.


On Meaningful Scientific Laws

On Meaningful Scientific Laws book cover
On Meaningful Scientific Laws

Written by Jean-Claude Falmange and Christopher Doble published by Springer 2015

This is a concise book full of proofs. They define Scientific Law in the Preface. An equation in which the variables represent quantities that are physical or geometrical. Meaningful is described in Chapter 5, Defining meaningfulness.

I read this book to take a break from the tangles data messes I am straightening out. It was a pleasant break.  This is a well written book with concise definitions. Well constructed proofs. I think a patient beginner could understand the book.

Useful ideas that would lead to not tangling data in the first place, like ratio scales in Chapter 2.

Chapter 9 Dimensional Invariance and Dimensional Analysis. You have to read thru the whole book because each section builds on previous to get to this valuable part. It is worth for the knowledge.

Now I am ready to get back to untangling data.

Sample Bias or is this a Classification Problem?

curious white  dog
curious white dog

I am very frustrated with the hiring process. This is going to focus on logic. Given that a diverse workforce is wanted. Take 500 diverse qualified applicants screen to 50 then 5. One screening criteria, phone screens.

Chance is .01 to make it thru the process.
Let us go back to the screening criteria, a cell phone.

A successful phone screen requires a quality cell phone and a quiet place to chat.
Did the diverse workforce even make it to start of the process? Or is the screening process starting earlier making sure that you only get the like you people.

Data Manipulation with R second edition

Jaynal Abedin, Kishor Kumar Das wrote
Data Manipulation with R second edition March 2015 published by Packt Publishing.
I found the first book useful. The second edition continues to be very useful. Chapter 5 covers R and databases. The limits of using R in memory and practical ideas of workarounds to solve the problem. Like ff and filehash packages.

Chapter 4 covers the melt function in the reshape2 package.

A lot of useful information for the hard work of getting data in shape.