Month: November 2011

Analyzing AdSense

I ran a recent experiment on my website- subjugating it to CTR ads (and not just the banner ads). Of course there is hardly a choice I have except for Google Adsense-and let me know if you know any reliable alternatives.

This is what the analytics says

So basically 43 ads out of 147,305 Ads were clicked.

This makes a Google Adsense ad/algorithm/you almost 99.971% of the time to ignore it, ( I am assuming some of the 147,362 ads which were not clicked were a bit annoying)

So I apologize to yall -Adsense aint making no sense, as they would say in old Tennessee

Still $12 per month when directed to charity is good enough…I donated some to Wikipedia.

$19.77 AdSense Revenue
$0.42 AdSense Revenue / 1000 Visits
43 AdSense Ads Clicked
0.00 AdSense Ads Clicked / Visit
0.09% AdSense CTR
$0.42 AdSense eCPM
147,305 AdSense Ads Viewed
2.92 AdSense Unit Impressions / Visit
46,585 AdSense Page Impressions
0.99 AdSense Page Impressions / Visit

Google Webinar on Web Analytics

Google webinar on web analytics-

recommended for anyone with anything to do with the WWW

From

http://analytics.blogspot.com/2011/11/webinar-reaching-your-goals-with.html

 

Webinar: Reaching Your Goals with Analytics

 

 

Is your website performing as well as it could be? Do you want to get more out of your digital marketing campaigns, including AdWords and other digital media? Do you feel like you have gaps in your current Google Analytics setup?

We’ve heard from many of our users who want to go deeper into their Analytics — with so much data, it can be hard to know where to look first. If you’d like to move beyond standard “pageview” metrics and visitor statistics, then please join us next Thursday:

Webinar: Reaching Your Goals with Analytics
Date: Thursday, December 1
Time: 11am PST / 2pm EST
Sign up here!

During the webinar, we’ll cover:

  • Key questions to ask for richer insights from your data
  • How to define “success” (for websites, visitors, or campaigns)
  • How to set up and use Goals
  • How to set up and use Ecommerce (for websites with a shopping cart)
  • How to link AdWords to your Google Analytics account

Whatever your online business model — shopping, lead-generation, or pure content — these tools will deliver actionable insights into your buying cycle.

This webinar will be led by Joe Larkin, a technical specialist on the Google Analytics team, and it’s designed for intermediate users of Google Analytics. If you’re comfortable with the basics, but you’d like to do more with your data, then we hope you’ll join us next week!

Data Documentation Initiative

Here is a nice initiative in standardizing data documentation for social sciences (which can be quite a relief to legions of analysts)

http://www.ddialliance.org/what

 

 

 

 

Benefits of DDI

The DDI facilitates:

  • Interoperability. Codebooks marked up using the DDI specification can be exchanged and transported seamlessly, and applications can be written to work with these homogeneous documents.
  • Richer content. The DDI was designed to encourage the use of a comprehensive set of elements to describe social science datasets as completely and as thoroughly as possible, thereby providing the potential data analyst with broader knowledge about a given collection.
  • Single document – multiple purposes. A DDI codebook contains all of the information necessary to produce several different types of output, including, for example, a traditional social science codebook, a bibliographic record, or SAS/SPSS/Stata data definition statements. Thus, the document may be repurposed for different needs and applications. Changes made to the core document will be passed along to any output generated.
  • On-line subsetting and analysis. Because the DDI markup extends down to the variable level and provides a standard uniform structure and content for variables, DDI documents are easily imported into on-line analysis systems, rendering datasets more readily usable for a wider audience.
  • Precision in searching. Since each of the elements in a DDI-compliant codebook is tagged in a specific way, field-specific searches across documents and studies are enabled. For example, a library of DDI codebooks could be searched to identify datasets covering protest demonstrations during the 1960s in specific states or countries.
Also see-
  1. http://www.ddialliance.org/Specification/DDI-Codebook/2.1/DTD/Documentation/DDI2-1-tree.html
  2. http://www.ddialliance.org/Specification/DDI-Lifecycle/3.1/

 

Free Machine Learning at Stanford

One of the cornerstones of the technology revolution, Stanford now offers some courses for free via distance learning. One of the more exciting courses is of course- machine learning

 

 

http://jan2012.ml-class.org/

About The Course

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

The Instructor

Professor Andrew Ng is Director of the Stanford Artificial Intelligence Lab, the main AI research organization at Stanford, with 20 professors and about 150 students/post docs. At Stanford, he teaches Machine Learning, which with a typical enrollment of 350 Stanford students, is among the most popular classes on campus. His research is primarily on machine learning, artificial intelligence, and robotics, and most universities doing robotics research now do so using a software platform (ROS) from his group.

 

  1. When does the class start?The class will start in January 2012 and will last approximately ten weeks.
  2. What is the format of the class?The class will consist of lecture videos, which are broken into small chunks, usually between eight and twelve minutes each. Some of these may contain integrated quiz questions. There will also be standalone quizzes that are not part of video lectures, and programming assignments.
  3. Will the text of the lectures be available?We hope to transcribe the lectures into text to make them more accessible for those not fluent in English. Stay tuned.
  4. Do I need to watch the lectures live?No. You can watch the lectures at your leisure.
  5. Can online students ask questions and/or contact the professor?Yes, but not directly There is a Q&A forum in which students rank questions and answers, so that the most important questions and the best answers bubble to the top. Teaching staff will monitor these forums, so that important questions not answered by other students can be addressed.
  6. Will other Stanford resources be available to online students?No.
  7. How much programming background is needed for the course?The course includes programming assignments and some programming background will be helpful.
  8. Do I need to buy a textbook for the course?No.
  9. How much does it cost to take the course?Nothing: it’s free!
  10. Will I get university credit for taking this course?No.Interested in learning machine learning-

    Well here is the website to enroll http://jan2012.ml-class.org/

Should you buy Zynga or Wait for the FB IPO

I am going to make a case for whether to buy or not buy  Zynga, and waiting to buy Facebook instead. Of course if Mark Pincus offers you a deep discount, and Mark Zuckenberg totally goes over the top with his P/E multiple, all bets would be re-valuated.

In the interest of your time, and my personal happiness, I am going to use a fairly standard way to measure attractiveness of both these companies- notably the Porter’s Five Forces Model. I will also review the recent experiences of Groupon and LinkedIn valuation to underscore what subtle differences in culture, and reputation of founders can affect the eventual value creation or destruction in an IPO.

(to be continued)

#Rstats Credit Scoring using R

I came across a nice, lucid and very readable document at the http://cran.r-project.org/doc/contrib/Sharma-CreditScoring.pdf

Credit Scoring is really a bread and butter activity at many analytics shopfloors, and I really liked the way Credit Scoring is explained and executed by the author- which can be used by any user regardless of experience.
Sharma-CreditScoringhttp://www.scribd.com/embeds/74139509/content?start_page=1&view_mode=list&access_key=key-ttkkmxe3hkmq3ic746c//

 

Interview Zach Goldberg, Google Prediction API

Here is an interview with Zach Goldberg, who is the product manager of Google Prediction API, the next generation machine learning analytics-as-an-api service state of the art cloud computing model building browser app.
Ajay- Describe your journey in science and technology from high school to your current job at Google.

Zach- First, thanks so much for the opportunity to do this interview Ajay!  My personal journey started in college where I worked at a startup named Invite Media.   From there I transferred to the Associate Product Manager (APM) program at Google.  The APM program is a two year rotational program.  I did my first year working in display advertising.  After that I rotated to work on the Prediction API.

Ajay- How does the Google Prediction API help an average business analytics customer who is already using enterprise software , servers to generate his business forecasts. How does Google Prediction API fit in or complement other APIs in the Google API suite.

Zach- The Google Prediction API is a cloud based machine learning API.  We offer the ability for anybody to sign up and within a few minutes have their data uploaded to the cloud, a model built and an API to make predictions from anywhere. Traditionally the task of implementing predictive analytics inside an application required a fair amount of domain knowledge; you had to know a fair bit about machine learning to make it work.  With the Google Prediction API you only need to know how to use an online REST API to get started.

You can learn more about how we help businesses by watching our video and going to our project website.

Ajay-  What are the additional use cases of Google Prediction API that you think traditional enterprise software in business analytics ignore, or are not so strong on.  What use cases would you suggest NOT using Google Prediction API for an enterprise.

Zach- We are living in a world that is changing rapidly thanks to technology.  Storing, accessing, and managing information is much easier and more affordable than it was even a few years ago.  That creates exciting opportunities for companies, and we hope the Prediction API will help them derive value from their data.

The Prediction API focuses on providing predictive solutions to two types of problems: regression and classification. Businesses facing problems where there is sufficient data to describe an underlying pattern in either of these two areas can expect to derive value from using the Prediction API.

Ajay- What are your separate incentives to teach about Google APIs  to academic or researchers in universities globally.

Zach- I’d refer you to our university relations page–

Google thrives on academic curiosity. While we do significant in-house research and engineering, we also maintain strong relations with leading academic institutions world-wide pursuing research in areas of common interest. As part of our mission to build the most advanced and usable methods for information access, we support university research, technological innovation and the teaching and learning experience through a variety of programs.

Ajay- What is the biggest challenge you face while communicating about Google Prediction API to traditional users of enterprise software.

Zach- Businesses often expect that implementing predictive analytics is going to be very expensive and require a lot of resources.  Many have already begun investing heavily in this area.  Quite often we’re faced with surprise, and even skepticism, when they see the simplicity of the Google Prediction API.  We work really hard to provide a very powerful solution and take care of the complexity of building high quality models behind the scenes so businesses can focus more on building their business and less on machine learning.

 

 

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