So, according to ‘them’ the global meltdown fuelling the use of this paradigm is just no more than a coincidence. There is a ton of research being done in various fields like Technology Adoption, Information Security and Organizational Behavior. Like any other innovation there are early adopters of technology and later adopters of the technology. The early adopters are usually characterized by their risk-seeking or entrepreneurial mindsets and late adopters by their risk-averse mindsets. What is interesting is to see if the above mentioned mindset, let’s call it ‘Perceived Risk’ linearly reduces with time or not and what factors mediate this relationship. This definitely will have implications on the success or failure of an innovation and can be useful to innovators and technology adopters alike. There is an awesome video (2 parts) from a CloudCamp session in Frankfurt where Simon Wardley from Canonical, Sponsors for Ubuntu talks about the state of confusion when it comes to understanding what Cloud Computing really is. He describes how this paradigm of computing wasn’t realized overnight, how it traveled through the different phases that any innovation usually does and why its’ time really has come. Totally recommend it.

Since the time I revealed my intention of pursuing the highest of all ‘academic accolades’, I got a lot of people who started asking questions/directions to help them get’ on board’. I did my bit of customizing my advice depending upon the level of awareness of the inquirer, but I thought it will be useful to churn up a write up which could be considered as sought of a primer on ‘How to get admitted into a Doctoral degree’ . Of course what will follow is a series of personal reflections and should no way be considered as an established formula.

The first thing that you need to ask yourself is why would you want to do a PhD?

• Is it because you want better life monetarily?
• If you are aiming at an International school, is it because you are attracted to higher standard of living?

Well, all I say is that getting a PhD could be necessary but not a sufficient condition to guarantee enhanced living both in terms of finance and standard of living. If you are motivated by money then you need to look at other degrees like an MBA or MS programs.

Enduring the academic rigour is going to be the main challenge. If you think that the time you spend as a student will translate into quick financial returns, again its best that you stop thinking of pursuing this avenue.

So bottom line is that only those people who are willing to sacrifice 3-4 years of their lives, irrespective of what stage they are in – fresh post-graduate, single working professional, working professional with a family etc., and people who are passionate about adding an original academic contribution to the ever growing body of knowledge should consider pursuing a PhD degree.

If you think you fit the description above then read on, for everyone else what follows is just mindless FYI.

Preparation for getting into a school:

‘A winning effort begins with preparation.’

-          Joe Gibbs

The whole process took approximately 2 years for me and this partly may have been due to the fact that I was working at the same time, nevertheless the minimum amount of time you should give yourself is a good year just for preparation. This includes giving all the required tests (GMAT, GRE, TOEFL, IELTS etc.,). Depending upon where you are planning to apply the requirements for the tests vary, please refer to individual Universities’ websites for that. Please find this useful table about the differences in the admission requirements between Universities in USA and other West European and Asia-Pacific schools

So depending upon where you want to apply your strategy changes. I decided to study in New Zealand purely because as an International Post-Graduate student my wife gets to work here for the entire duration of my course.

Roadmap and check-list with time frame for applying at the University of Auckland:

• Lodging the ‘Expression of interest’ (EOI) here: http://www.postgrad.auckland.ac.nz/doctoral/interest/ You will need a fully developed research proposal and your degree transcripts to make an EOI. This process took a good 9 weeks for me. Basically you will get an email from the school stating whether your EOI has been accepted or not. EOI is nothing but a pre-assessment of the strength of your application.  But getting through this stage is a positive sign as half the battle is won.
• Next step is to launch the formal application. The University will give you login details for you to lodge a formal application online. You will have to complement your online application with certified hard copies of your degree transcripts and certificates and sealed and signed reference letters. You can ask your referees to send the letters electronically as well. Reference letters are crucial, so please make sure that you give enough time to your referees to write the letters, as often, hastily written letters may not give a holistic description of your abilities of worse leave out important aspects of your abilities. Once you complete your formal application and the University receives your documents it should take another 9 weeks before they come back to you with a result.
• Assuming that they do return with a positive result, the next important step is the visa process. This for me by far was the most frustrating and infuriating experience as nothing realistically is under your control. The first requirement is you need a Police Clearance Certificate (PCC) and for that you will have to deal with your local police. For a detailed account of my experience with this check the following link:                                                                                               http://www.biztrivia.org/blog/2009/11/getting-a-police-clearance-certificate-is-such-a-pain/

• Then when you do get it, which is like Nirvana, you quickly have to fight the uphill battle of enduring the actual Visa process. It took a bloody long 8 months to get my visa, for reasons, well I simply don’t know. All visa applications lodged with Immigration New Zealand, though you apply in your home country, are sent to this clandestine branch called IPG (Immigration profiling group) in Wellington in New Zealand.  Neither your University nor anyone else can communicate with these folks. So you just have to wait, wait and wait. A Visa officer gets allocated to my application after about 3 months of lodging the application and then it takes another good 4 months to get the Visa. Here is the link to a forum where people with similar frustrations came together to discuss the Visa process: http://forums.immigration.com/showthread.php?305396-NZ-PhD-Student-Visa-5-Month-Processing-time

I hope that this write up would prove to be useful to all who are dilly dallying on the topic of pursuing a PhD and would make a realistic decision before they make the final leap. Again, this by no means is complete and is heavily biased towards the application and visa process in the context of a New Zealand University.

Initially there was a lot of confusion about the difference between the fields of Inferential Statistics and Data Mining and researchers around the world immediately started working on producing tons of literature to address this issue. Though there is no single universally accepted definition for either of them, many researchers have defined these fields through their individual application perspectives. From what I’ve gathered and known about these fields, this is how I draw a line between them.

Difference between Data mining & Inferential Statistics:

• Data Mining is a field where you discover hidden patterns from already existing large data sets. These hidden patterns discovered are later used for analysis and decision making scenarios in the area of concern. The process of using the discovered hidden patterns is also called as ‘Knowledge Discovery’. Inferential Statistics is the field where you prove or refute a pre-conceived hypothesis (or a null hypothesis) by performing classical statistical methods on a sample of a given population size.
• Data mining starts at an already existing database (usually large datasets) and Inferential Statistics generates its own database using sampling methods on data set.
• Data mining methods employed like classification, clustering, etc., scan the entire dataset in search of hidden patterns while classical statistical methods are run over only a small section of the dataset (the sample).

The above point also infers that Data mining methods are more computer intensive as they have to run through large data sets and hence should be used only when really needed.

So when do you use data mining over inferential statistics?  Well the answer is simple. If you don’t know what you are looking but want to make the best sense of the data you have then use data mining. If you know what you are looking for and want to back it up with proof by checking the data you have you should use inferential statistics.

Click Forensics, a leading solutions provider for improving traffic quality for online ad campaigns, reported that though there is an overall dip in click fraud rate in Q2 2009 over Q1, click fraud traffic from sophisticated sources and scripted programs rose again in Q2 2009. This is evident from the rise of Publisher Collusion Fraud where publishers are employing sophisticated IP rotation methods or botnets  in oder to inflate their commissions.

There is a huge incentive for the Ad Networks and Search Engines to look at this as a serious concern because any hint of discomfort advertiser will lead the advertiser and/or publisher to defect from the system which will eventually lead to revenue losses.  Infact, Click Forensics President, Tom Cuthbert states that the increased diligence from the Ad Networks and Search Engines is the main reason for the click fraud rates to steadily decrease over the past few quarters. Google for example has set up a resource center to raise awareness about this issue. All the docs, presos and videos at this link are excellent reads on the topic.

So, from a technology standpoint, Search Engines and Ad Networks are doing their bit by developing tools and techniques in order to mitigate the effects of click fraud. What is lacking though are studies that look at user, publisher and advertiser behavior towards click fraud. Do they know what click fraud is? How do they validate erratic behavior on their campaigns? Is there a certain level of awareness to help them make informed decisions ?etc., There is enough proof to tell you that often advertisers complain that a seemingly erratic behavior in their campaigns is click fraud while the real reasons for such a behavior might be something completely different. Conducting research hence is important for the Search Engine  and Ad Networks to develop a strategy around increasing awareness around this issue that actually is effective.