Tag: blog

Why search optimization can make you like Rebecca Black

Felicia Day, actress and web content producer.
Image via Wikipedia

A highly optimized blog post or web content can get you a lot of attention just like Rebecca Black’s video (provided it passes through the new quality metrics \change*/ in the Search Engine)

But if the underlying content is weak, or based on a shoddy understanding of the content-it can drive lots of horrid comments as well as ensuring that bad word of mouth is spread about the content or you/despite your hard work.

An example of this is copy and paste journalism especially in technology circles, where even a bigger Page Ranked website /blog can get away with scraping or stealing content from a lower page ranked website (or many websites)  after adding a cursory “expert comment”. This is also true when someone who is basically a corporate communication specialist (or PR -public relations) person is given a techinical text and encourage to write about it without completely understanding it.

A mild technical defect in the search engine algorithm is that it does not seem to pay attention to when the content was published, so the copying website or blog actually can get by as fresher content even if it is practically has 90% of the same words). The second flaw is over punishment or manual punishment of excessive linking – this can encourage search optimization minded people to hoard links or discourage trackbacks.

A free internet is one which promotes free sharing of content and does not encourage stealing or un-authorized scraping or content copying. Unfortunately current search engine optimization can encourage scraping and content copying without paying too much attention to origin of the words.

In addition the analytical rigor by which search algorithms search your inboxes (as in search all emails for a keyword) or media rich sites (like Youtube) are quite on a different level of quality altogether. The chances of garbage results are much more while searching for media content and/or emails.

It's a code code summer

East-German pupils ("Junge Pioniere"...
Image via Wikipedia

and soc is back!

also expecting some #Rstats entries (open source!)

from https://code.google.com/soc/

Google Summer of Code 2011

Visit the Google Summer of Code 2011 site for more details about the program this year.

For a detailed timeline and further information about the program, review our Frequently Asked Questions.

About Google Summer of Code

Google Summer of Code is a global program that offers student developers stipends to write code for various open source software projects. We have worked with several open source, free software, and technology-related groups to identify and fund several projects over a three month period. Since its inception in 2005, the program has brought together over 4500 successful student participants and over 3000 mentors from over 100 countries worldwide, all for the love of code. Through Google Summer of Code, accepted student applicants are paired with a mentor or mentors from the participating projects, thus gaining exposure to real-world software development scenarios and the opportunity for employment in areas related to their academic pursuits. In turn, the participating projects are able to more easily identify and bring in new developers. Best of all, more source code is created and released for the use and benefit of all.

To learn more about the program, peruse our 2011 Frequently Asked Questions page. You can also subscribe to the Google Open Source Blog or the Google Summer of Code Discussion Group to keep abreast of the latest announcements.

Participating in Google Summer of Code

For those of you who would like to participate in the program, there are many resources available for you to learn more. Check out the information pages from the 2005, 2006, 2007, 2008, 2009, and 2010 instances of the program to get a better sense of which projects have participated as mentoring organizations in Google Summer of Code each year. If you are interested in a particular mentoring organization, just click on its name and you’ll find more information about the project, a summary of their students’ work and actual source code produced by student participants. You may also find the program Frequently Asked Questions (FAQs) pages for each year to be useful. Finally, check out all the great content and advice on participation produced by the community, for the community, on our program wiki.

If you don’t find what you need in the documentation, you can always ask questions on our program discussion list or the program IRC channel, #gsoc on Freenode.

 

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Is Random Poetry Click Fraud

Meta-search-vi
Image via Wikipedia

Is poetry when randomized

Tweaked, meta tagged , search engine optimized

Violative of unseen terms and conditional clauses

Is random poetry or aggregated prose farmed for click fraud uses

 

 

 

I dont know, you tell me, says the blog boy,

Tapping away at the keyboard like a shiny new toy,

Geeks unfortunately too often are men too many,

Forgive the generalization, but the tech world is yet to be equalized.

 

If a New York Hot Dog  is a slice of heaven at four bucks a piece

Then why is prose and poetry at five bucks an hour considered waste

Ah I see, you have grown old and cynical,

Of the numerous stupid internet capers and cyber ways

 

The clicking finger clicks on

swiftly but mostly delightfully virally moves on

While people collect its trails and

ponder its aggregated merry ways

 

All people are equal but all links are not,

Thus overturning two centuries of psychology had you been better taught,

But you chose to drop out of school, and create that search engine so big

It is now a fraud catchers head ache that millions try to search engine optimize and rig

 

Once again, people are different, in so many ways so prettier

Links are the same hyper linked code number five or earlier

People think like artificial artificial (thus natural) neural nets

Biochemically enhanced Harmonically possessed.

 

rather than  analyze forensically and quite creepily

where people have been

Gentic Algorithms need some chaos

To see what till now hasnt been seen.

 

Again this was a random poem,

inspired by a random link that someone clicked

To get here, on a carbon burning cyber machine,

Having digested poem, moves on, unheard , unseen.

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(Inspired by the Hyper Link at http://goo.gl/a8ijW )

Also-

Protected: Whats behind that pretty SAS Blog?

This content is password-protected. To view it, please enter the password below.

Spam Analysis Akismet-WPStats-Blogging

Here is a brief dataset I out after one hour of cutting and pasting from WordPress.com’s creative data style formats. It shows spam,comments,traffic, and number of posts written monthly.

Clearly monthly traffic is directly related to number I write (suppose A + B* Posts)

But Spam is showing a discontinuous growth especially after a big month (in which Reddit helped)

Akismet had some missing historical values (which is curious)

So what can we do with this dataframe in R or any other statistical software.

Spam Analysis
Month Spam detected Traffic excluding spam Posts Written Traffic /Post Spam /Post Spam/Traffic Ham detected Missed spam False positives
Feb-11 1848 5079 18 282.17 102.6667 36.39% 4.00 6.00 0.0%
Jan-11 3724 10238 35 292.51 106.4 36.37% 0.00 3.00 0.0%
Dec-10 3676 10345 35 295.57 105.0286 35.53% 8.00 6.00 0.0%
Nov-10 3680 11723 71 165.11 51.83099 31.39% 24.00 3.00 0.0%
Oct-10 2292 16430 71 231.41 32.28169 13.95% 24.00 18.00 0.0%
Sep-10 0 17913 63 284.33 0 0.00% 0.00 0.00 0.0%
Aug-10 0 5403 17 317.82 0 0.00% 0.00 0.00 0.0%
Jul-10 2 5041 10 504.1 0.2 0.04% 0.00 0.00 0.0%
Jun-10 5 4271 11 388.27 0.454545 0.12% 10.00 1.00 0.0%
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PAW Blog Partnership

Please use the following code  to get a 15% discount on the 2 Day Conference Pass: AJAY11.

 

 

 

 

Predictive Analytics World announces new full-day workshops coming to San Francisco March 13-19, amounting to seven consecutive days of content.

These workshops deliver top-notch analytical and business expertise across the hottest topics.

Register now for one or more workshops, offered just before and after the full two-day Predictive Analytics World conference program (March 14-15). Early Bird registration ends on January 31st – take advantage of reduced pricing before then.

Driving Enterprise Decisions with Business Analytics – March 13, 2011
James Taylor, CEO, Decision Management Solutions
NEW – R for Predictive Modeling: A Hands-On Introduction – March 13, 2011
Max Kuhn, Director, Nonclinical Statistics, Pfizer
The Best and Worst of Predictive Analytics: Predictive Modeling Methods and Common Data Mining Mistakes – March 16, 2011
John Elder, Ph.D., CEO and Founder, Elder Research, Inc.
Hands-On Predictive Analytics – March 17, 2011
Dean Abbott, President, Abbott Analytics
NEW – Net Lift Models: Optimizing the Impact of Your Marketing – March 18-19, 2011
Kim Larsen, VP of Analytical Insights, Market Share Partners

Download the Conference Preview or view the Predictive Analytics World Agenda online

Make savings now with the early bird rate. Receive $200 off your registration rate for Predictive Analytics World – San Francisco (March 14-15), plus $100 off each workshop for which you register.

Register now before Early Bird Price expires on January 31st!

Additional savings of $200 on the two-day conference pass when you register a colleague at the same time.

 

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Interview Ajay Ohri Decisionstats.com with DMR

From-

http://www.dataminingblog.com/data-mining-research-interview-ajay-ohri/

Here is the winner of the Data Mining Research People Award 2010: Ajay Ohri! Thanks to Ajay for giving some time to answer Data Mining Research questions. And all the best to his blog, Decision Stat!

Data Mining Research (DMR): Could you please introduce yourself to the readers of Data Mining Research?

Ajay Ohri (AO): I am a business consultant and writer based out of Delhi- India. I have been working in and around the field of business analytics since 2004, and have worked with some very good and big companies primarily in financial analytics and outsourced analytics. Since 2007, I have been writing my blog at http://decisionstats.com which now has almost 10,000 views monthly.

All in all, I wrote about data, and my hobby is also writing (poetry). Both my hobby and my profession stem from my education ( a masters in business, and a bachelors in mechanical engineering).

My research interests in data mining are interfaces (simpler interfaces to enable better data mining), education (making data mining less complex and accessible to more people and students), and time series and regression (specifically ARIMAX)
In business my research interests software marketing strategies (open source, Software as a service, advertising supported versus traditional licensing) and creation of technology and entrepreneurial hubs (like Palo Alto and Research Triangle, or Bangalore India).

DMR: I know you have worked with both SAS and R. Could you give your opinion about these two data mining tools?

AO: As per my understanding, SAS stands for SAS language, SAS Institute and SAS software platform. The terms are interchangeably used by people in industry and academia- but there have been some branding issues on this.
I have not worked much with SAS Enterprise Miner , probably because I could not afford it as business consultant, and organizations I worked with did not have a budget for Enterprise Miner.
I have worked alone and in teams with Base SAS, SAS Stat, SAS Access, and SAS ETS- and JMP. Also I worked with SAS BI but as a user to extract information.
You could say my use of SAS platform was mostly in predictive analytics and reporting, but I have a couple of projects under my belt for knowledge discovery and data mining, and pattern analysis. Again some of my SAS experience is a bit dated for almost 1 year ago.

I really like specific parts of SAS platform – as in the interface design of JMP (which is better than Enterprise Guide or Base SAS ) -and Proc Sort in Base SAS- I guess sequential processing of data makes SAS way faster- though with computing evolving from Desktops/Servers to even cheaper time shared cloud computers- I am not sure how long Base SAS and SAS Stat can hold this unique selling proposition.

I dislike the clutter in SAS Stat output, it confuses me with too much information, and I dislike shoddy graphics in the rendering output of graphical engine of SAS. Its shoddy coding work in SAS/Graph and if JMP can give better graphics why is legacy source code preventing SAS platform from doing a better job of it.

I sometimes think the best part of SAS is actually code written by Goodnight and Sall in 1970’s , the latest procs don’t impress me much.

SAS as a company is something I admire especially for its way of treating employees globally- but it is strange to see the rest of tech industry not following it. Also I don’t like over aggression and the SAS versus Rest of the Analytics /Data Mining World mentality that I sometimes pick up when I deal with industry thought leaders.

I think making SAS Enterprise Miner, JMP, and Base SAS in a completely new web interface priced at per hour rates is my wishlist but I guess I am a bit sentimental here- most data miners I know from early 2000’s did start with SAS as their first bread earning software. Also I think SAS needs to be better priced in Business Intelligence- it seems quite cheap in BI compared to Cognos/IBM but expensive in analytical licensing.

If you are a new stats or business student, chances are – you may know much more R than SAS today. The shift in education at least has been very rapid, and I guess R is also more of a platform than a analytics or data mining software.

I like a lot of things in R- from graphics, to better data mining packages, modular design of software, but above all I like the can do kick ass spirit of R community. Lots of young people collaborating with lots of young to old professors, and the energy is infectious. Everybody is a CEO in R ’s world. Latest data mining algols will probably start in R, published in journals.

Which is better for data mining SAS or R? It depends on your data and your deadline. The golden rule of management and business is -it depends.

Also I have worked with a lot of KXEN, SQL, SPSS.

DMR: Can you tell us more about Decision Stats? You have a traffic of 120′000 for 2010. How did you reach such a success?

AO: I don’t think 120,000 is a success. Its not a failure. It just happened- the more I wrote, the more people read.In 2007-2008 I used to obsess over traffic. I tried SEO, comments, back linking, and I did some black hat experimental stuff. Some of it worked- some didn’t.

In the end, I started asking questions and interviewing people. To my surprise, senior management is almost always more candid , frank and honest about their views while middle managers, public relations, marketing folks can be defensive.

Social Media helped a bit- Twitter, Linkedin, Facebook really helped my network of friends who I suppose acted as informal ambassadors to spread the word.
Again I was constrained by necessity than choices- my middle class finances ( I also had a baby son in 2007-my current laptop still has some broken keys :) – by my inability to afford traveling to conferences, and my location Delhi isn’t really a tech hub.

The more questions I asked around the internet, the more people responded, and I wrote it all down.

I guess I just was lucky to meet a lot of nice people on the internet who took time to mentor and educate me.

I tried building other websites but didn’t succeed so i guess I really don’t know. I am not a smart coder, not very clever at writing but I do try to be honest.

Basic economics says pricing is proportional to demand and inversely proportional to supply. Honest and candid opinions have infinite demand and an uncertain supply.

DMR: There is a rumor about a R book you plan to publish in 2011 :-) Can you confirm the rumor and tell us more?

AO: I just signed a contract with Springer for ” R for Business Analytics”. R is a great software, and lots of books for statistically trained people, but I felt like writing a book for the MBAs and existing analytics users- on how to easily transition to R for Analytics.

Like any language there are tricks and tweaks in R, and with a focus on code editors, IDE, GUI, web interfaces, R’s famous learning curve can be bent a bit.

Making analytics beautiful, and simpler to use is always a passion for me. With 3000 packages, R can be used for a lot more things and a lot more simply than is commonly understood.
The target audience however is business analysts- or people working in corporate environments.

Brief Bio-
Ajay Ohri has been working in the field of analytics since 2004 , when it was a still nascent emerging Industries in India. He has worked with the top two Indian outsourcers listed on NYSE,and with Citigroup on cross sell analytics where he helped sell an extra 50000 credit cards by cross sell analytics .He was one of the very first independent data mining consultants in India working on analytics products and domestic Indian market analytics .He regularly writes on analytics topics on his web site www.decisionstats.com and is currently working on open source analytical tools like R besides analytical software like SPSS and SAS.

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