Terrorists Robots and Data Scientists

Will Smith stars in

The rules for Robots from “Handbook of Robotics, 56th Edition, 2058 A.D.”, are:

  1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
  2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
  3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws

0. A robot may not harm humanity, or, by inaction, allow humanity to come to harm.

The zeroth law is why democratic governments kill terrorists using all means possible. One dead terrorist  is better than many innocents killed.

to be continued

Java by Code Academy

I love the Java by Code Academy course, even though it is very new and quite possibly one of the first of many Java courses that hopefully appear on this beloved website. I crossed 800 points today on CodeAcademy.


Summaries  from Code Academy on Java- these are sourced by the nifty help provided and are cited to Code Academy ( how does one cite it!)

Java is an object-oriented programming (OOP) language, the coder can design classes, objects, and methods that can perform certain actions. These behaviours are important in the construction of larger, more powerful Java programmes. Java is a programming language designed to build secure, powerful applications that run across multiple operating systems. The Java language is known to be flexible, scalable, and maintainable.


  • Data Types are int, boolean, and char.
  • Variables are used to store values.
  • Whitespace helps make code easy to read for you and others.
  • Comments describe code and its purpose.
  • Arithmetic Operators include +, -, *,/, and %.
  • Relational Operators include <, <=, >, and >=.
  • Equality Operators include == and !=.



Control flow allows Java programs to execute code blocks depending on Boolean expressions.

  • Boolean Operators: &&, ||, and ! are used to build Boolean expressions and have a defined order of operations
  • Statements: if, if/else, andif/else if/else statements are used to conditionally execute blocks of code
  • Ternary Conditional: a shortened version of an if/else statement that returns a value based on the value of a Boolean expression
  • Switch: allows us to check equality of a variable or expression with a value that does not need to be a Boolean


Ajay Ohri interviews Dr Bradley Jones for StatisticsViews.com

I had the good fortune and privilege to interview a  genuine statistical hero, Dr Bradley Jones


He holds a patent on the use of DOE for minimizing registration errors in the manufacture of laminated circuit boards and is the inventor of the prediction profile plot for interactive exploration of multiple input and output response surfaces. In both 2009 and 2011, he received the American Society for Quality’s Brumbaugh Award for the paper making the largest contribution to industrial quality control. He also won the 2010 Lloyd S. Nelson Award for the article having the greatest immediate impact to practitioners. Jones is the Editor-in-Chief of the Journal of Quality Technology, a Fellow of the ASA and co-author of the award winning Optimal Design of Experiments with Peter Goos.

Typically, DOE is taught by rote using pre-packaged designs. This makes it hard for an engineer to see the practical applicability of DOE. In addition, most DOE texts devote most of their pages to analysis rather than the core principles of design. Students do not learn how to evaluate and compare prospective designs for their appropriateness to a specific problem. The textbooks (and professors) need to catch up with the software.

You can read the complete article at http://www.statisticsviews.com/details/feature/8510051/For-me-the-fun-of-working-with-scientists-and-engineers-is-helping-them-generate.html

Famous in X but Failure in Y

Some of my friends on the internet and in real life love food. Note the distinction between internet friends and real life friends. There is more to genuine long lasting relationships than exchange of engaging bits bytes and moving your mouse on icons to say I love this, I plus one that, I really adore it.

Well my friends and I, we love food. Some of us , in fact most of us eat food. Some of us click pictures and share in on the anti-social media. Anti-social because it is anti-real life socializing. A few of us cook food. One or two write recipes. Occasionally one of us tries to make food his business by floating the idea of opening a restaurant. This is despite the fact that just eating food is ahem easy and running a restaurant  business  is inherently risky.

Making your passion into a business is a dream and privilege that is offered to very few of us.  Athletes, technology startup founders, Drug Lords.

Occasionally one may be a success in one line of the business. Someone who loves food can write good books, but will you exchange your mom’s apple pie for that telegenic chef. Someone who writes good books on food can automatically run a very good restaurant. No. Good in books doesnot mean good in business in the same thing.

Genius doesnt travel. IF you are reading this, probability says you are not a genius anyway.



Twitter Analysis Redefined

Because code keeps changing on Twitter

#dev.twitter.com and apps.twitter.com to generate these tokens
reqURL <- "https://api.twitter.com/oauth/request_token"
accessURL <- "https://api.twitter.com/oauth/access_token"
authURL <- "https://api.twitter.com/oauth/authorize"

consumerKey <- "4LEjfrnbzMQvxpJzRKnx6v0JM"
consumerSecret <- "aCsJA6jEHhpqFioKmxwtu9BzMm0TnOFQyZv6mgCUo1j82PzRIn"


a=searchTwitter("delhi", n=2000)
tweets_dfa = twListToDF(a)
b=searchTwitter("mumbai", n=200)
tweets_dfb = twListToDF(b)
c=searchTwitter("bangalore", n=200)
tweets_dfc = twListToDF(c)
b=Corpus(VectorSource(tweets$text), readerControl = list(language = "eng"))
b=tm_map(b, PlainTextDocument)
b<- tm_map(b, content_transformer(tolower))
#Changes case to lower case
b<- tm_map(b, stripWhitespace) #Strips White Space
b <- tm_map(b, removePunctuation) #Removes Punctuation
tdm <- TermDocumentMatrix(b)
m1 <- as.matrix(tdm)
v1<- sort(rowSums(m1),decreasing=TRUE)
wordcloud(d2$word,d2$freq,colors =brewer.pal(7,"Set1"))


Workflows in R compared to Workflows in Python

A workflow consists of an orchestrated and repeatable pattern of business activity enabled by the systematic organization of resources into processes that transform materials, provide services, or process information.

Both R and Python have similar workflows but slightly different syntax. one of the biggest difference is how they refer to parts of object ( $ [] in R while [] in Python) as well as how they apply functions ( fun(object) in R while object.fun() in Python)


a workflow in Python


Screenshot from 2015-10-24 08:31:22

a workflow in R


Screenshot from 2015-10-24 08:31:07

Garbage Collection in a technology startup

Garbage collection (GC) is a form of automatic memory management. The garbage collector, or justcollector, attempts to reclaim garbage, or memory occupied by objects that are no longer in use by the program. Thats garbage collection from Jimmypedia. When you dont do enough garbage collection in a program you can end up with Stack Overflow.

Tech Startups have garbage collections too. A garbage collector looks only for garbage. In the edges, on the floor, below the carpet, under ths stairs. In meetings, in team discussions, in stock options, in business plans, in cash burn projections. These are negative anti social emotionally dysfunctional people. Their over abundant IQ (Intelligence Quotient)is balanced by their teenager like EQ ( Emotional Quotient). To balance the cyncial Einstein, you generally need a shiny eyed startup founder who has dreams of ringing the NASDAQ bell every night.

Tech Startups also have unicorn catterpillars. These are people who think they will shed their legs, wrap them in a silky cocoon and become unicorn butterfliess with wings. The shiny eyed founder can become an unicorn butterfly very fast, till the garbage is collected from the cocoon and the oyster returns to being an oyster than turning into a pearl.

Tech Startups have over caffienated engineers and under caffienated salesmen. I wonder how the industry would react if they introduce mandatory drug testing for startups. Maybe we will all migrate to Canada under a Treudian utopia of hemp and grass.


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