DECISION STATS

Are we in an Analytics Recession- a decline in SAS is a decline for all #rstats

I was intrigued by David Smith’s blog post at http://blog.revolutionanalytics.com/2013/08/job-trends-for-statistics-packages.html and played with some of the terms associated with analytics and data science.

Some points on that-

1) The term SAS is broader than the Statistical Software.

2) The term R is even broader. Accordingly I searched for R language- again it is a narrower term

3) Even by David’s own graph- SAS jobs have declined by 33% over two years, while R jobs have increased by 50%. However some jobs list both SAS and R so will be counted twice.

4) Even by David’s graph , SAS jobs are still twice as many as R jobs. So the overall market for analytics job is declined

5) I have no way to giving an exact conclusion unless I have access to the data, or I fire up a scraper myself.

6) Jobs remains a key area of concern for students and for future growth

7) Python statistical packages may need to be included shortly. I think sometimes it is easier to teach applied statistics (and data mining) to talented coders than teach scripting to talented statisticians.

8) Is hypothesis testing dead in the era of Big Data. What is a t test or chi square for a million rows. Almost all the better theory for such is locked in Bing or Google Research

9) The continued existence of Microsoft OS should be a sobering thought to people claiming ultimate victory too soon. Software needs to be sold, and sometimes the better sold software triumphs over the better designed software.

10) I would think that R has completely dominated the academic statistical market the same way SAS was doing it for business analytics some years back. However there exists much work to be done given some limitations of that software.

11) However SAS Institute revenue continues to grow. One reason for that could be the Institute work for big money clients including the US Government. Despite Drew Conway- I have yet to come across too many cases of the Big Fed using R.

SAS Jobs

The below is a

SAS,R language,data science,analytics,business analytics,SPSS Job Trends

SAS jobs – R Language jobs – Data Science jobs – Analytics jobs – Business Analytics jobs – Spss jobs

How to be a better writer

Background- I wrote this as an accident while trolling on Quora. I was not confident of what I wrote- in fact I wrote it anonymous except people kept asking me why! It was pure serendipity- I wrote it less than 4 minutes and submitted without thinking. Then edited once based on feedback.

Some one clearly more smarter than me made my tips for writing into a picture http://amandaonwriting.tumblr.com/post/54265230509

and it went popular on Tumblr just like it did on Quora!

Apparently if some guy like Wil Wheaton likes your words, it can go viral! It has 41799 notes ( reblogs+hearts) on Tumblr as of now.

http://wilwheaton.tumblr.com/post/54699823961/torteen-great-advice-to-writers

Words . Reposted by a member of STAR TREK:NG. I can now die a happy Geek! The Internet is a funny thing!

Thank you everyone! Now if only Google learnt to include OCR for Images as part of text search!

Write 50 words . That’s a paragraph.
Write 400 words . That’s a page.
Write 300 pages. That’s a manuscript.
Write everyday. That’s a habit.
Edit and Rewrite. That’s how you get better.
Spread your writing for people to comment. That’s called feedback.
Dont worry about rejection or publication. That’s a writer.
When not writing, read. Read from writers better than you. Read and Perceive.

But overall, just write more to get better.

1887+ votes on Quora!! 🙂 Probably my most viewed content ever- !

61036 people have viewed this answer!

https://www.quora.com/Writing/How-can-we-improve-our-writing-skill/answers/2048810?__snids__=94784810&__nsrc__=1

Also it got a mention here-

Ajay Ohri: The 8 Rules of Writing

Now I think I should take some of my own advice and get back to writing

Using ifelse in R for creating new variables #rstats #data #manipulation

The ifelse function is simple and powerful and can help in data manipulation within R. Here I create a categoric variable from specific values in a numeric variable

> data(iris)

> iris$Type=ifelse(iris$Sepal.Length<5.8,”Small Flower”,”Big Flower”)
> table(iris$Type)
Big Flower Small Flower
77 73

The parameters of ifelse is quite simple

Usage

ifelse(test, yes, no)
Arguments

test
an object which can be coerced to logical mode.

yes
return values for true elements of test.

no
return values for false elements of tes

Summer Reading :NBER

Some of the things I like to read to stay sharp as I grow old.

NBER

For example this paper says how foreign born PHDs do location choices.

Graduates with stronger academic ability, measured by whether they received university support for a research or a teaching assistantship, had a 6.8 percent higher stay rate. Assistantships were the primary means of support for 52 percent of the sample; 11 percent of the sample was supported by a university fellowship or scholarship, which was associated with a 2.7 percent higher stay rate. Students supported by foreign funding, 4 percent of the sample, had an intent to stay that was 26.7 points lower.

As one would expect, ties to the United States are also important. Foreign-born U.S. citizens have a 19.5 percent higher stay rate; green card holders have a 15.3 percent higher stay rate; and U.S. college graduates have a 3.6 percent higher stay rate.

FOAS becomes hottest thing in open source data science #rstats

Within a short time FOAS has lined up an interesting list of projects for Data Science statups and projects for open access and open source. FOAS accreditation provides the ultimate halmark of open in word and spirit for data science projects.

See it yourself at http://www.foastat.org/projects.html

How Geeks Become Hackers in China

The Path-

How?

Why?

Source-

routine activity theory⁵ (RAT), social learning theory¹ (SLT), and situational action theory²⁷ (SAT).

While RAT helps explain how hacking begins, SAT explains why talented young people take the road toward computer hacking, even when presented with many alternatives, and SLT calls for attention to environments that sustain hacking behavior and subculture. However, none of these theories explains the evolution of certain critical elements in the hacker process: motivation, knowledge and skill, opportunity, moral values and judgment, and the environment.

Based on our case evidence and the literature, we developed a framework for understanding and managing hackers and hacking behavior from an evolutionary perspective. The framework’s most significant contribution is its explication of the enablers and constraints influencing hackers, providing guidance for managing the hacking epidemic by schools, universities, and throughout society. This framework calls for zero tolerance for hacking in schools and early intervention (such as through courses in computer ethics in middle and high schools, supervised competitions in defending computer security, and organizing computer security services for organizations) to strengthen the moral values of students against hacking and channel their interest in computers in a positive direction.

http://cacm.acm.org/magazines/2013/4/162513-why-computer-talents-become-computer-hackers/fulltext