Interview Jamie Nunnelly NISS

An interview with Jamie Nunnelly, Communications Director of National Institute of Statistical Sciences

Ajay– What does NISS do? And What does SAMSI do?

Jamie– The National Institute of Statistical Sciences (NISS) was established in 1990 by the national statistics societies and the Research Triangle universities and organizations, with the mission to identify, catalyze and foster high-impact, cross-disciplinary and cross-sector research involving the statistical sciences.

NISS is dedicated to strengthening and serving the national statistics community, most notably by catalyzing community members’ participation in applied research driven by challenges facing government and industry. NISS also provides career development opportunities for statisticians and scientists, especially those in the formative stages of their careers.

The Institute identifies emerging issues to which members of the statistics community can make key contributions, and then catalyzes the right combinations of researchers from multiple disciplines and sectors to tackle each problem. More than 300 researchers from over 100 institutions have worked on our projects.

The Statistical and Applied Mathematical Sciences Institute (SAMSI) is a partnership of Duke University,  North Carolina State University, The University of North Carolina at Chapel Hill, and NISS in collaboration with the William Kenan Jr. Institute for Engineering, Technology and Science and is part of the Mathematical Sciences Institutes of the NSF.

SAMSI focuses on 1-2 programs of research interest in the statistical and/or applied mathematical area and visitors from around the world are involved with the programs and come from a variety of disciplines in addition to mathematics and statistics.

Many come to SAMSI to attend workshops, and also participate in working groups throughout the academic year. Many of the working groups communicate via WebEx so people can be involved with the research remotely. SAMSI also has a robust education and outreach program to help undergraduate and graduate students learn about cutting edge research in applied mathematics and statistics.

Ajay– What successes have you had in 2010- and what do you need to succeed in 2011. Whats planned for 2011 anyway

Jamie– NISS has had a very successful collaboration with the National Agricultural Statistical Service (NASS) over the past two years that was just renewed for the next two years. NISS & NASS had three teams consisting of a faculty researcher in statistics, a NASS researcher, a NISS mentor, a postdoctoral fellow and a graduate student working on statistical modeling and other areas of research for NASS.

NISS is also working on a syndromic surveillance project with Clemson University, Duke University, The University of Georgia, The University of South Carolina. The group is currently working with some hospitals to test out a model they have been developing to help predict disease outbreak.

SAMSI had a very successful year with two programs ending this past summer, which were the Stochastic Dynamics program and the Space-time Analysis for Environmental Mapping, Epidemiology and Climate Change. Several papers were written and published and many presentations have been made at various conferences around the world regarding the work that was conducted as SAMSI last year.

Next year’s program is so big that the institute has decided to devote all it’s time and energy around it, which is uncertainty quantification. The opening workshop, in addition to the main methodological theme, will be broken down into three areas of interest under this broad umbrella of research: climate change, engineering and renewable energy, and geosciences.

Ajay– Describe your career in science and communication.

Jamie– I have been in communications since 1985, working for large Fortune 500 companies such as General Motors and Tropicana Products. I moved to the Research Triangle region of North Carolina after graduate school and got into economic development and science communications first working for the Research Triangle Regional Partnership in 1994.

From 1996-2005 I was the communications director for the Research Triangle Park, working for the Research Triangle Foundation of NC. I published a quarterly magazine called The Park Guide for awhile, then came to work for NISS and SAMSI in 2008.

I really enjoy working with the mathematicians and statisticians. I always joke that I am the least educated person working here and that is not far from the truth! I am honored to help get the message out about all of the important research that is conducted here each day that is helping to improve the lives of so many people out there.

Ajay– Research Triangle or Silicon Valley– Which is better for tech people and why? Your opinion

Jamie– Both the Silicon Valley and Research Triangle are great regions for tech people to locate, but of course, I have to be biased and choose Research Triangle!

Really any place in the world that you find many universities working together with businesses and government, you have an area that will grow and thrive, because the collaborations help all of us generate new ideas, many of which blossom into new businesses, or new endeavors of research.

The quality of life in places such as the Research Triangle is great because you have people from around the world moving to a place, each bringing his/her culture, food, and uniqueness to this place, and enriching everyone else as a result.

Two advantages the Research Triangle has over Silicon Valley are that the Research Triangle has a bigger diversity of industries, so when the telecommunications industry busted back in 2001-02, the region took a hit, but the biotechnology industry was still growing, so unemployment rose, but not to the extent that other areas might have experienced.

The latest recession has hit us all very hard, so even this strategy has not made us immune to having high unemployment, but the Research Triangle region has been pegged by experts to be one of the first regions to emerge out of the Great Recession.

The other advantage I think we have is that our cost of living is still much more reasonable than Silicon Valley. It’s still possible to get a nice sized home, some land and not break the bank!

Ajay– How do you manage an active online social media presence, your job and your family. How important is balance in professional life and when young professional should realize this?

Jamie– Balance is everything, isn’t it? When I leave the office, I turn off my iPhone and disconnect from Twitter/Facebook etc.

I know that is not recommended by some folks, but I am a one person communications department and I love my family and friends and feel its important to devote time to them as well as to my career.

I think it is very important for young people to establish this early in their careers because if they don’t they will fall victim to working way too many hours and really, who loves you at the end of the day?

Your company may appreciate all you do for them, but if you leave, or you get sick and cannot work for them, you will be replaced

. Lee Iacocca, former CEO of Chrystler, said, “No matter what you’ve done for yourself or for humanity, if you can’t look back on having given love and attention to your own family, what have you really accomplished?” I think that is what is really most important in life.

About-

Jamie Nunnelly has been in communications for 25 years. She is currently on the board of directors for Chatham County Economic Development Corporation and Leadership Triangle & is a member of the International Association of Business Communicators and the Public Relations Society of America. She earned a bachelor’s degree in interpersonal and public communications at Bowling Green State University and a master’s degree in mass communications at the University of South Florida.

You can contact Jamie at http://niss.org/content/jamie-nunnelly or on twitter at

Quantifying Analytics ROI

Japanese House Crest “Go-Shichi no Kiri”
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I had a brief twitter exchange with Jim Davis, Chief Marketing Officer, SAS Institute on Return of Investment on Business Analytics Projects for customers. I have interviewed Jim Davis before last year https://decisionstats.com/2009/06/05/interview-jim-davis-sas-institute/

Now Jim Davis is a big guy, and he is rushing from the launch of SAS Institute’s Social Media Analytics in Japan- to some arguably difficult flying conditions in time to be home in America for Thanksgiving. That and and I have not been much of a good Blog Boy recently, more swayed by love of open source, than love of software per se. I love equally, given I am bad at both equally.

Anyways, Jim’s contention  ( http://twitter.com/Davis_Jim ) was customers should go in business analytics only if there is Positive Return on Investment.  I am quoting him here-

What is important is that there be a positive ROI on each and every BA project. Otherwise don’t do it.

That’s not the marketing I was taught in my business school- basically it was sell, sell, sell.

However I see most BI sales vendors also go through -let me meet my sales quota for this quarter- and quantifying customer ROI is simple maths than predictive analytics but there seems to be some information assymetry in it.

Here is a paper from North Western University on ROI in IT projects-.

but overall it would be in the interest of customers and Business Analytics Vendors to publish aggregated ROI.

The opponents to this transparency in ROI would be market leaders in market share, who have trapped their customers by high migration costs (due to complexity) or contractually.

A recent study listed Oracle having a large percentage of unhappy customers who would still renew!, SAP had problems when it raised prices for licensing arbitrarily (that CEO is now CEO of HP and dodging legal notices from Oracle).

Indeed Jim Davis’s famous unsettling call for focusing on Business Analytics,as Business Intelligence is dead- that call has been implemented more aggressively by IBM in analytical acquisitions than even SAS itself which has been conservative about inorganic growth. Quantifying ROI, should theoretically aid open source software the most (since they are cheapest in up front licensing) or newer technologies like MapReduce /Hadoop (since they are quite so fast)- but I think that market has a way of factoring in these things- and customers are not as foolish neither as unaware of costs versus benefits of migration.

The contrary to this is Business Analytics and Business Intelligence are imperfect markets with duo-poly  or big players thriving in absence of customer regulation.

You get more protection as a customer of $20 bag of potato chips, than as a customer of a $200,000 software. Regulators are wary to step in to ensure ROI fairness (since most bright techies are qither working for private sector, have their own startup or invested in startups)- who in Govt understands Analytics and Intelligence strong enough to ensure vendor lock-ins are not done, and market flexibility is done. It is also a lower choice for embattled regulators to ensure ROI on enterprise software unlike the aggressiveness they have showed in retail or online software.

Who will Analyze the Analysts and who can quantify the value of quants (or penalize them for shoddy quantitative analytics)- is an interesting phenomenon we expect to see more of.

 

 

Doing Time Series using a R GUI

The Xerox Star Workstation introduced the firs...
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Until recently I had been thinking that RKWard was the only R GUI supporting Time Series Models-

however Bob Muenchen of http://www.r4stats.com/ was helpful to point out that the Epack Plugin provides time series functionality to R Commander.

Note the GUI helps explore various time series functionality.

Using Bulkfit you can fit various ARMA models to dataset and choose based on minimum AIC

 

> bulkfit(AirPassengers$x)
$res
ar d ma      AIC
[1,]  0 0  0 1790.368
[2,]  0 0  1 1618.863
[3,]  0 0  2 1522.122
[4,]  0 1  0 1413.909
[5,]  0 1  1 1397.258
[6,]  0 1  2 1397.093
[7,]  0 2  0 1450.596
[8,]  0 2  1 1411.368
[9,]  0 2  2 1394.373
[10,]  1 0  0 1428.179
[11,]  1 0  1 1409.748
[12,]  1 0  2 1411.050
[13,]  1 1  0 1401.853
[14,]  1 1  1 1394.683
[15,]  1 1  2 1385.497
[16,]  1 2  0 1447.028
[17,]  1 2  1 1398.929
[18,]  1 2  2 1391.910
[19,]  2 0  0 1413.639
[20,]  2 0  1 1408.249
[21,]  2 0  2 1408.343
[22,]  2 1  0 1396.588
[23,]  2 1  1 1378.338
[24,]  2 1  2 1387.409
[25,]  2 2  0 1440.078
[26,]  2 2  1 1393.882
[27,]  2 2  2 1392.659
$min
ar        d       ma      AIC
2.000    1.000    1.000 1378.338
> ArimaModel.5 <- Arima(AirPassengers$x,order=c(0,1,1),
+ include.mean=1,
+   seasonal=list(order=c(0,1,1),period=12))
> ArimaModel.5
Series: AirPassengers$x
ARIMA(0,1,1)(0,1,1)[12]
Call: Arima(x = AirPassengers$x, order = c(0, 1, 1), seasonal = list(order = c(0,      1, 1), period = 12), include.mean = 1)
Coefficients:
ma1     sma1
-0.3087  -0.1074
s.e.   0.0890   0.0828
sigma^2 estimated as 135.4:  log likelihood = -507.5
AIC = 1021   AICc = 1021.19   BIC = 1029.63
> summary(ArimaModel.5, cor=FALSE)
Series: AirPassengers$x
ARIMA(0,1,1)(0,1,1)[12]
Call: Arima(x = AirPassengers$x, order = c(0, 1, 1), seasonal = list(order = c(0,      1, 1), period = 12), include.mean = 1)
Coefficients:
ma1     sma1
-0.3087  -0.1074
s.e.   0.0890   0.0828
sigma^2 estimated as 135.4:  log likelihood = -507.5
AIC = 1021   AICc = 1021.19   BIC = 1029.63
In-sample error measures:
ME        RMSE         MAE         MPE        MAPE        MASE
0.32355285 11.09952005  8.16242469  0.04409006  2.89713514  0.31563730
Dataset79 <- predar3(ArimaModel.5,fore1=5)

 

And I also found an interesting Ref Sheet for Time Series functions in R-

http://cran.r-project.org/doc/contrib/Ricci-refcard-ts.pdf

and a slightly more exhaustive time series ref card

http://www.statistische-woche-nuernberg-2010.org/lehre/bachelor/datenanalyse/Refcard3.pdf

Also of interest a matter of opinion on issues in Time Series Analysis in R at

http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm

Of course , if I was the sales manager for SAS ETS I would be worried given the increasing capabilities in Time Series in R. But then again some deficiencies in R GUI for Time Series-

1) Layout is not very elegant

2) Not enough documented help (atleast for the Epack GUI- and no integrated help ACROSS packages-)

3) Graphical capabilties need more help documentation to interpret the output (especially in ACF and PACF plots)

More resources on Time Series using R.

http://people.bath.ac.uk/masgs/time%20series/TimeSeriesR2004.pdf

and http://www.statoek.wiso.uni-goettingen.de/veranstaltungen/zeitreihen/sommer03/ts_r_intro.pdf

and books

http://www.springer.com/economics/econometrics/book/978-0-387-77316-2

http://www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-75960-9

http://www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-75958-6

http://www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-75966-1

Microsoft Online Games

No, this is not about the X Box kind of games. It is about Microsoft ‘s tactical shift in the online space from going it alone, and building stuff itself, –to partnering, and sometimes investing and exiting business.

In Blogs- It recently announced a migration of MS Live Spaces to WordPress.com – It gives Automattic 30 million more users- no small change consider there were 26 million existing WP users.

Microsoft Messenger, which is the oldest online app in the suite, now provides instant messaging services to about 350 million users, and from now on Windows Live Writer works specifically with the WordPress.com blog service by default. Hopefully Skype, and Google Voice will show MS the way to monitize that business app yet.

Google buying blogger-blogspot seems to have done little, but given Biz Stone room to create another content disruption-Twitter.

With the round of lawsuits by proxy, in Android -Motorola, or for acquisitions – MS is just doing what Marc Anderseen (who’s apparently a better VC than Paul Allen was), Sun and co did to it in the nineties.

Google seems to be regretting putting a spade in the Yahoo acquisition- that would have tied up a big chunk of Idle MS cash- leaving it little room for niche investments (like the 250 mill that helped Facebook ramp up in time).

The real surprise here could be Apple- it has shown little interest in cloud computing- and it seems to be testing the waters with Ping. But Apple sure smells competition- and Android is doing to Iphone what Windows did to the Mac in the early 1990’s.

Google lacks presence in online gaming (despite it’s own Zynga investment)- and needs to start monetizing properties like Android OS (say 10$ for every phone license ??), Google Maps (as an app for GPS) and Google Voice. Indeed it may be time for the big G to start thinking of spinning off atleast some products- earning better returns, while retaining control (dual stock splits) and killing those anti trust lawyer fees forever.

As the Ancient Chinese said, May you live in interesting times. Fun to watch the online games people play.

 

 

Interview Michael J. A. Berry Data Miners, Inc

Here is an interview with noted Data Mining practitioner Michael Berry, author of seminal books in data mining, noted trainer and consultantmjab picture

Ajay- Your famous book “Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management” came out in 2004, and an update is being planned for 2011. What are the various new data mining techniques and their application that you intend to talk about in that book.

Michael- Each time we do a revision, it feels like writing a whole new book. The first edition came out in 1997 and it is hard to believe how much the world has changed since then. I’m currently spending most of my time in the on-line retailing world. The things I worry about today–improving recommendations for cross-sell and up-sell,and search engine optimization–wouldn’t have even made sense to me back then. And the data sizes that are routine today were beyond the capacity of the most powerful super computers of the nineties. But, if possible, Gordon and I have changed even more than the data mining landscape. What has changed us is experience. We learned an awful lot between the first and second editions, and I think we’ve learned even more between the second and third.

One consequence is that we now have to discipline ourselves to avoid making the book too heavy to lift. For the first edition, we could write everything we knew (and arguably, a bit more!); now we have to remind ourselves that our intended audience is still the same–intelligent laymen with a practical interest in getting more information out of data. Not statisticians. Not computer scientists. Not academic researchers. Although we welcome all readers, we are primarily writing for someone who works in a marketing department and has a title with the word “analyst” or “analytics” in it. We have relaxed our “no equations” rule slightly for cases when the equations really do make things easier to explain, but the core explanations are still in words and pictures.

The third edition completes a transition that was already happening in the second edition. We have fully embraced standard statistical modeling techniques as full-fledged components of the data miner’s toolkit. In the first edition, it seemed important to make a distinction between old, dull, statistics, and new, cool, data mining. By the second edition, we realized that didn’t really make sense, but remnants of that attitude persisted. The third edition rectifies this. There is a chapter on statistical modeling techniques that explains linear and logistic regression, naive Bayes models, and more. There is also a brand new chapter on text mining, a curious omission from previous editions.

There is also a lot more material on data preparation. Three whole chapters are devoted to various aspects of data preparation. The first focuses on creating customer signatures. The second is focused on using derived variables to bring information to the surface, and the third deals with data reduction techniques such as principal components. Since this is where we spend the greatest part of our time in our work, it seemed important to spend more time on these subjects in the book as well.

Some of the chapters have been beefed up a bit. The neural network chapter now includes radial basis functions in addition to multi-layer perceptrons. The clustering chapter has been split into two chapters to accommodate new material on soft clustering, self-organizing maps, and more. The survival analysis chapter is much improved and includes material on some of our recent application of survival analysis methods to forecasting. The genetic algorithms chapter now includes a discussion of swarm intelligence.

Ajay- Describe your early career and how you came into Data Mining as a profession. What do you think of various universities now offering MS in Analytics. How do you balance your own teaching experience with your consulting projects at The Data Miners.

Michael- I fell into data mining quite by accident. I guess I always had a latent interest in the topic. As a high school and college student, I was a fan of Martin Gardner‘s mathematical games in in Scientific American. One of my favorite things he wrote about was a game called New Eleusis in which one players, God, makes up a rule to govern how cards can be played (“an even card must be followed by a red card”, say) and the other players have to figure out the rule by watching what plays are allowed by God and which ones are rejected. Just for my own amusement, I wrote a computer program to play the game and presented it at the IJCAI conference in, I think, 1981.

That paper became a chapter in a book on computer game playing–so my first book was about finding patterns in data. Aside from that, my interest in finding patterns in data lay dormant for years. At Thinking Machines, I was in the compiler group. In particular, I was responsible for the run-time system of the first Fortran Compiler for the CM-2 and I represented Thinking Machines at the Fortran 8X (later Fortran-90) standards meetings.

What changed my direction was that Thinking Machines got an export license to sell our first machine overseas. The machine went to a research lab just outside of Paris. The connection machine was so hard to program, that if you bought one, you got an applications engineer to go along with it. None of the applications engineers wanted to go live in Paris for a few months, but I did.

Paris was a lot of fun, and so, I discovered, was actually working on applications. When I came back to the states, I stuck with that applied focus and my next assignment was to spend a couple of years at Epsilon, (then a subsidiary of American Express) working on a database marketing system that stored all the “records of charge” for American Express card members. The purpose of the system was to pick ads to go in the billing envelope. I also worked on some more general purpose data mining software for the CM-5.

When Thinking Machines folded, I had the opportunity to open a Cambridge office for a Virginia-based consulting company called MRJ that had been a major channel for placing Connection Machines in various government agencies. The new group at MRJ was focused on data mining applications in the commercial market. At least, that was the idea. It turned out that they were more interested in data warehousing projects, so after a while we parted company.

That led to the formation of Data Miners. My two partners in Data Miners, Gordon Linoff and Brij Masand, share the Thinking Machines background.

To tell the truth, I really don’t know much about the university programs in data mining that have started to crop up. I’ve visited the one at NC State, but not any of the others.

I myself teach a class in “Marketing Analytics” at the Carroll School of Management at Boston College. It is an elective part of the MBA program there. I also teach short classes for corporations on their sites and at various conferences.

Ajay- At the previous Predictive Analytics World, you took a session on Forecasting and Predicting Subsciber levels (http://www.predictiveanalyticsworld.com/dc/2009/agenda.php#day2-6) .

It seems inability to forecast is a problem many many companies face today. What do you think are the top 5 principles of business forecasting which companies need to follow.

Michael- I don’t think I can come up with five. Our approach to forecasting is essentially simulation. We try to model the underlying processes and then turn the crank to see what happens. If there is a principal behind that, I guess it is to approach a forecast from the bottom up rather than treating aggregate numbers as a time series.

Ajay- You often partner your talks with SAS Institute, and your blog at http://blog.data-miners.com/ sometimes contain SAS code as well. What particular features of the SAS software do you like. Do you use just the Enterprise Miner or other modules as well for Survival Analysis or Forecasting.

Michael- Our first data mining class used SGI’s Mineset for the hands-on examples. Later we developed versions using Clementine, Quadstone, and SAS Enterprise Miner. Then, market forces took hold. We don’t market our classes ourselves, we depend on others to market them and then share in the revenue.

SAS turned out to be much better at marketing our classes than the other companies, so over time we stopped updating the other versions. An odd thing about our relationship with SAS is that it is only with the education group. They let us use Enterprise Miner to develop course materials, but we are explicitly forbidden to use it in our consulting work. As a consequence, we don’t use it much outside of the classroom.

Ajay- Also any other software you use (apart from SQL and J)

Michael- We try to fit in with whatever environment our client has set up. That almost always is SQL-based (Teradata, Oracle, SQL Server, . . .). Often SAS Stat is also available and sometimes Enterprise Miner.

We run into SPSS, Statistica, Angoss, and other tools as well. We tend to work in big data environments so we’ve also had occasion to use Ab Initio and, more recently, Hadoop. I expect to be seeing more of that.

Biography-

Together with his colleague, Gordon Linoff, Michael Berry is author of some of the most widely read and respected books on data mining. These best sellers in the field have been translated into many languages. Michael is an active practitioner of data mining. His books reflect many years of practical, hands-on experience down in the data mines.

Data Mining Techniques cover

Data Mining Techniques for Marketing, Sales and Customer Relationship Management

by Michael J. A. Berry and Gordon S. Linoff
copyright 2004 by John Wiley & Sons
ISB

Mining the Web cover

Mining the Web

by Michael J.A. Berry and Gordon S. Linoff
copyright 2002 by John Wiley & Sons
ISBN 0-471-41609-6

Non-English editions available in Traditional Chinese and Simplified Chinese

This book looks at the new opportunities and challenges for data mining that have been created by the web. The book demonstrates how to apply data mining to specific types of online businesses, such as auction sites, B2B trading exchanges, click-and-mortar retailers, subscription sites, and online retailers of digital content.

Mastering Data Mining

by Michael J.A. Berry and Gordon S. Linoff
copyright 2000 by John Wiley & Sons
ISBN 0-471-33123-6

Non-English editions available in JapaneseItalianTraditional Chinese , and Simplified Chinese

A case study-based guide to applying data mining techniques for solving practical business problems. These “warts and all” case studies are drawn directly from consulting engagements performed by the authors.

A data mining educator as well as a consultant, Michael is in demand as a keynote speaker and seminar leader in the area of data mining generally and the application of data mining to customer relationship management in particular.

Prior to founding Data Miners in December, 1997, Michael spent 8 years at Thinking Machines Corporation. There he specialized in the application of massively parallel supercomputing techniques to business and marketing applications, including one of the largest database marketing systems of the time.

Public Opinion Quarterly

If you are interested in

SURVEY METHODOLOGY FOR PUBLIC HEALTH RESEARCHERS

There is a free virtual issue, Survey Methodology for Public Health Researchers: Selected Readings from 20 years of PublicOpinion Quarterly. The virtual issue’s 18 articles illustrate the range of survey methods material that can be found in POQ and include conclusions that are still valid today. Specially chosen by guest editor Floyd J. Fowler, the articles will be of interest to those who work and research in public health and health services more broadly

For beginners interested in software

1) For web development , get  into http://www.wordpress.org and its a pretty easy software to start making websites on.

You can maybe spend say 10 $ a month so that you can buy some server space on http://www.bluehost.com and tinker with his own website /blog in the meantime.

For learning language CSS ,PhP and HTML are the way to go.

2) If you knows some languages already, try  to make a Facebook application , and then play with Google’s open social API,or game, as that will get his interest besides giving him a skill thats useful. Ipod developer’s kit is another hot area to experiment.

3)For designing software solutions I would recommend the Microsoft Certification program. Try  to learn 1 language like Visual Basic or into .Net programming.These platforms will still be useful in coming years.

4) For statistical/business software try  to learn a language called R, which is good for data mining (www.r-project.org) . its quite easy to learn and has a good graphical user interface too.

5)For software careers it is best to learn multiple types of softwares to hedge your bets.

For sustaining interest, you  can join and network with fellow programmers using bulletin boards especially boards on http://www.google.com for google code and microsoft developers area..

6) You can also download Ubuntu linux (www.ubuntu.com) , which is a free Linux based Operation System (like Windows) and  be more familiar in it. Also add openoffice from http://www.openoffice.org This gives you perspectives on open source software.

7)I recommend him getting summer internship in a software startup (especially any software company in Silicon valley or Bangalore) ,and with established companies (like http://www.google.com , http://www.facebook.com,www.infosys.com

Software developers are the un-sung heroes of today’s modern world!!!

All the best !

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