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Book Review- Machine Learning for Hackers

This is review of the fashionably named book Machine Learning for Hackers by Drew Conway and John Myles White (O’Reilly ). The book is about hacking code in R.

 

The preface introduces the reader to the authors conception of what machine learning and hacking is all about. If the name of the book was machine learning for business analytsts or data miners, I am sure the content would have been unchanged though the popularity (and ambiguity) of the word hacker can often substitute for its usefulness. Indeed the many wise and learned Professors of statistics departments through out the civilized world would be mildly surprised and bemused by their day to day activities as hacking or teaching hackers. The book follows a case study and example based approach and uses the GGPLOT2 package within R programming almost to the point of ignoring any other native graphics system based in R. It can be quite useful for the aspiring reader who wishes to understand and join the booming market for skilled talent in statistical computing.

Chapter 1 has a very useful set of functions for data cleansing and formatting. It walks you through the basics of formatting based on dates and conditions, missing value and outlier treatment and using ggplot package in R for graphical analysis. The case study used is an Infochimps dataset with 60,000 recordings of UFO sightings. The case study is lucid, and done at a extremely helpful pace illustrating the powerful and flexible nature of R functions that can be used for data cleansing.The chapter mentions text editors and IDEs but fails to list them in a tabular format, while listing several other tables like Packages used in the book. It also jumps straight from installation instructions to functions in R without getting into the various kinds of data types within R or specifying where these can be referenced from. It thus assumes a higher level of basic programming understanding for the reader than the average R book.

Chapter 2 discusses data exploration, and has a very clear set of diagrams that explain the various data summary operations that are performed routinely. This is an innovative approach and will help students or newcomers to the field of data analysis. It introduces the reader to type determination functions, as well different kinds of encoding. The introduction to creating functions is quite elegant and simple , and numerical summary methods are explained adequately. While the chapter explains data exploration with the help of various histogram options in ggplot2 , it fails to create a more generic framework for data exploration or rules to assist the reader in visual data exploration in non standard data situations. While the examples are very helpful for a reader , there needs to be slightly more depth to step out of the example and into a framework for visual data exploration (or references for the same). A couple of case studies however elaborately explained cannot do justice to the vast field of data exploration and especially visual data exploration.

Chapter 3 discussed binary classification for the specific purpose for spam filtering using a dataset from SpamAssassin. It introduces the reader to the naïve Bayes classifier and the principles of text mining suing the tm package in R. Some of the example codes could have been better commented for easier readability in the book. Overall it is quite a easy tutorial for creating a naïve Bayes classifier even for beginners.

Chapter 4 discusses the issues in importance ranking and creating recommendation systems specifically in the case of ordering email messages into important and not important. It introduces the useful grepl, gsub, strsplit, strptime ,difftime and strtrim functions for parsing data. The chapter further introduces the reader to the concept of log (and affine) transformations in a lucid and clear way that can help even beginners learn this powerful transformation concept. Again the coding within this chapter is sparsely commented which can cause difficulties to people not used to learn reams of code. ( it may have been part of the code attached with the book, but I am reading an electronic book and I did not find an easy way to go back and forth between the code and the book). The readability of the chapters would be further enhanced by the use of flow charts explaining the path and process followed than overtly verbose textual descriptions running into multiple pages. The chapters are quite clearly written, but a helpful visual summary can help in both revising the concepts and elucidate the approach taken further.A suggestion for the authors could be to compile the list of useful functions they introduce in this book as a sort of reference card (or Ref Card) for R Hackers or atleast have a chapter wise summary of functions, datasets and packages used.

Chapter 5 discusses linear regression , and it is a surprising and not very good explanation of regression theory in the introduction to regression. However the chapter makes up in practical example what it oversimplifies in theory. The chapter on regression is not the finest chapter written in this otherwise excellent book. Part of this is because of relative lack of organization- correlation is explained after linear regression is explained. Once again the lack of a function summary and a process flow diagram hinders readability and a separate section on regression metrics that help make a regression result good or not so good could be a welcome addition. Functions introduced include lm.

Chapter 6 showcases Generalized Additive Model (GAM) and Polynomial Regression, including an introduction to singularity and of over-fitting. Functions included in this chapter are transform, and poly while the package glmnet is also used here. The chapter also introduces the reader formally to the concept of cross validation (though examples of cross validation had been introduced in earlier chapters) and regularization. Logistic regression is also introduced at the end in this chapter.

Chapter 7 is about optimization. It describes error metric in a very easy to understand way. It creates a grid by using nested loops for various values of intercept and slope of a regression equation and computing the sum of square of errors. It then describes the optim function in detail including how it works and it’s various parameters. It introduces the curve function. The chapter then describes ridge regression including definition and hyperparameter lamda. The use of optim function to optimize the error in regression is useful learning for the aspiring hacker. Lastly it describes a case study of breaking codes using the simplistic Caesar cipher, a lexical database and the Metropolis method. Functions introduced in this chapter include .Machine$double.eps .

Chapter 8 deals with Principal Component Analysis and unsupervised learning. It uses the ymd function from lubridate package to convert string to date objects, and the cast function from reshape package to further manipulate the structure of data. Using the princomp functions enables PCA in R.The case study creates a stock market index and compares the results with the Dow Jones index.

Chapter 9 deals with Multidimensional Scaling as well as clustering US senators on the basis of similarity in voting records on legislation .It showcases matrix multiplication using %*% and also the dist function to compute distance matrix.

Chapter 10 has the subject of K Nearest Neighbors for recommendation systems. Packages used include class ,reshape and and functions used include cor, function and log. It also demonstrates creating a custom kNN function for calculating Euclidean distance between center of centroids and data. The case study used is the R package recommendation contest on Kaggle. Overall a simplistic introduction to creating a recommendation system using K nearest neighbors, without getting into any of the prepackaged packages within R that deal with association analysis , clustering or recommendation systems.

Chapter 11 introduces the reader to social network analysis (and elements of graph theory) using the example of Erdos Number as an interesting example of social networks of mathematicians. The example of Social Graph API by Google for hacking are quite new and intriguing (though a bit obsolete by changes, and should be rectified in either the errata or next edition) . However there exists packages within R that should be atleast referenced or used within this chapter (like TwitteR package that use the Twitter API and ROauth package for other social networks). Packages used within this chapter include Rcurl, RJSONIO, and igraph packages of R and functions used include rbind and ifelse. It also introduces the reader to the advanced software Gephi. The last example is to build a recommendation engine for whom to follow in Twitter using R.

Chapter 12 is about model comparison and introduces the concept of Support Vector Machines. It uses the package e1071 and shows the svm function. It also introduces the concept of tuning hyper parameters within default algorithms . A small problem in understanding the concepts is the misalignment of diagram pages with the relevant code. It lastly concludes with using mean square error as a method for comparing models built with different algorithms.

 

Overall the book is a welcome addition in the library of books based on R programming language, and the refreshing nature of the flow of material and the practicality of it’s case studies make this a recommended addition to both academic and corporate business analysts trying to derive insights by hacking lots of heterogeneous data.

Have a look for yourself at-
http://shop.oreilly.com/product/0636920018483.do

Interview Michal Kosinski , Concerto Web Based App using #Rstats

Here is an interview with Michal Kosinski , leader of the team that has created Concerto – a web based application using R. What is Concerto? As per http://www.psychometrics.cam.ac.uk/page/300/concerto-testing-platform.htm

Concerto is a web based, adaptive testing platform for creating and running rich, dynamic tests. It combines the flexibility of HTML presentation with the computing power of the R language, and the safety and performance of the MySQL database. It’s totally free for commercial and academic use, and it’s open source

Ajay-  Describe your career in science from high school to this point. What are the various stats platforms you have trained on- and what do you think about their comparative advantages and disadvantages?  

Michal- I started with maths, but quickly realized that I prefer social sciences – thus after one year, I switched to a psychology major and obtained my MSc in Social Psychology with a specialization in Consumer Behaviour. At that time I was mostly using SPSS – as it was the only statistical package that was taught to students in my department. Also, it was not too bad for small samples and the rather basic analyses I was performing at that time.

 

My more recent research performed during my Mphil course in Psychometrics at Cambridge University followed by my current PhD project in social networks and research work at Microsoft Research, requires significantly more powerful tools. Initially, I tried to squeeze as much as possible from SPSS/PASW by mastering the syntax language. SPSS was all I knew, though I reached its limits pretty quickly and was forced to switch to R. It was a pretty dreary experience at the start, switching from an unwieldy but familiar environment into an unwelcoming command line interface, but I’ve quickly realized how empowering and convenient this tool was.

 

I believe that a course in R should be obligatory for all students that are likely to come close to any data analysis in their careers. It is really empowering – once you got the basics you have the potential to use virtually any method there is, and automate most tasks related to analysing and processing data. It is also free and open-source – so you can use it wherever you work. Finally, it enables you to quickly and seamlessly migrate to other powerful environments such as Matlab, C, or Python.

Ajay- What was the motivation behind building Concerto?

Michal- We deal with a lot of online projects at the Psychometrics Centre – one of them attracted more than 7 million unique participants. We needed a powerful tool that would allow researchers and practitioners to conveniently build and deliver online tests.

Also, our relationships with the website designers and software engineers that worked on developing our tests were rather difficult. We had trouble successfully explaining our needs, each little change was implemented with a delay and at significant cost. Not to mention the difficulties with embedding some more advanced methods (such as adaptive testing) in our tests.

So we created a tool allowing us, psychometricians, to easily develop psychometric tests from scratch an publish them online. And all this without having to hire software developers.

Ajay -Why did you choose R as the background for Concerto? What other languages and platforms did you consider. Apart from Concerto, how else do you utilize R in your center, department and University?

Michal- R was a natural choice as it is open-source, free, and nicely integrates with a server environment. Also, we believe that it is becoming a universal statistical and data processing language in science. We put increasing emphasis on teaching R to our students and we hope that it will replace SPSS/PASW as a default statistical tool for social scientists.

Ajay -What all can Concerto do besides a computer adaptive test?

Michal- We did not plan it initially, but Concerto turned out to be extremely flexible. In a nutshell, it is a web interface to R engine with a built-in MySQL database and easy-to-use developer panel. It can be installed on both Windows and Unix systems and used over the network or locally.

Effectively, it can be used to build any kind of web application that requires a powerful and quickly deployable statistical engine. For instance, I envision an easy to use website (that could look a bit like SPSS) allowing students to analyse their data using a web browser alone (learning the underlying R code simultaneously). Also, the authors of R libraries (or anyone else) could use Concerto to build user-friendly web interfaces to their methods.

Finally, Concerto can be conveniently used to build simple non-adaptive tests and questionnaires. It might seem to be slightly less intuitive at first than popular questionnaire services (such us my favourite Survey Monkey), but has virtually unlimited flexibility when it comes to item format, test flow, feedback options, etc. Also, it’s free.

Ajay- How do you see the cloud computing paradigm growing? Do you think browser based computation is here to stay?

Michal - I believe that cloud infrastructure is the future. Dynamically sharing computational and network resources between online service providers has a great competitive advantage over traditional strategies to deal with network infrastructure. I am sure the security concerns will be resolved soon, finishing the transformation of the network infrastructure as we know it. On the other hand, however, I do not see a reason why client-side (or browser) processing of the information should cease to exist – I rather think that the border between the cloud and personal or local computer will continually dissolve.

About

Michal Kosinski is Director of Operations for The Psychometrics Centre and Leader of the e-Psychometrics Unit. He is also a research advisor to the Online Services and Advertising group at the Microsoft Research Cambridge, and a visiting lecturer at the Department of Mathematics in the University of Namur, Belgium. You can read more about him at http://www.michalkosinski.com/

You can read more about Concerto at http://code.google.com/p/concerto-platform/ and http://www.psychometrics.cam.ac.uk/page/300/concerto-testing-platform.htm

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/

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.

 

 

Heritage prize= 3mill now open

I am still angry with THE netflix for 1 mill I lost out. No sweat! this time the money is 3 times as much, it is legit, and yes baby you can change the world, make it a better place and get rich.! see details below-http://www.heritagehealthprize.com/c/hhp/Data

HERITAGE HEALTH PRIZE DATA FILES

You must accept this competition’s rules before you’ll be able to download data files.

IMPORTANT NOTE: The information provided below is intended only to provide general guidance to participants in the Heritage Health Prize Competition and is subject to the Competition Official Rules. Any capitalized term not defined below is defined in the Competition Official Rules. Please consult the Competition Official Rules for complete details.

Heritage Provider Network is providing Competition Entrants with deidentified member data collected during a forty-eight month period that is allocated among three data sets (the “Data Sets”). Competition Entrants will use the Data Sets to develop and test their algorithms for accurately predicting the number of days that the members will spend in a hospital (inpatient or emergency room visit) during the 12-month period following the Data Set cut-off date.

HHP_release2.zip contains the latest files, so you can ignore HHP_release1.zip. SampleEntry.CSV shows you how an entry should look.

Data Sets will be released to Entrants after registration on the Website according to the following schedule:

April 4, 2011 Claims Table – Y1 and DaysInHospital Table – Y2

May 4, 2011

All other Data Sets except Labs Table and Rx Table

From https://www.kaggle.com/

The $3 million Heritage Health Prize opens to entries

It’s been one month since the launch of the Heritage Health Prize. The prize has attracted some great publicity, receiving coverage from the Wall Street JournalThe EconomistSlate andForbes.

By now, people have had a good chance to poke around the first portion of the data. Now the fun starts! HPN have released two more years’-worth of data, set the accuracy threshold and are opening up the competition to entries. The data are available from the Heritage Health Prize page. Good luck to all participants!

The Deloitte/FIDE Chess Ratings Competition results

The Deloitte/FIDE Chess Ratings Competition attracted one of the strongest fields ever seen in a Kaggle Competition. The competition attracted 189 teams, ranging from chess ratings  experts to Netflix Prize winners. As Jeff Sonas wrote on the Kaggle blog last week, the  competition has far exceeded his expectations. A big congratulations the provisional winner, Tim Salimans, an econometrician at Erasmus University in Rotterdam. We look forward to reading about the approaches used by top performers on the Kaggle blog. We also look forward to the results of the FIDE prize, which could see the introduction of a new chess ratings system.

ICDAR 2011 Competition Results

The ICDAR 2011 competition also finished recently. The competiiton required participants to develop an algorithm that correctly matched handwriting samples. The winners were Lewis Griffin and Andrew Newell from the University College London who achieved Kaggle’s first ever perfect score by managing to match every sample correctly! Andrew and Lewis have posted a description of their winning method on the Kaggle blog.

Revolution R Enterprise

Since R is the most popular language used by Kaggle members, the Revolution Analytics team is making Revolution R Enterprise (the pre-eminent commercial version of R) available free of charge to Kaggle members. Revolution R Enterprise has several advantages over standard R, including the ability to seemlessly handle larger datasets. To get your free copy, visit http://info.revolutionanalytics.com/Kaggle.html.
Kaggle-in-Class

As many of you know, Kaggle offers a free platform, Kaggle-in-Class, for instructors who want to host competitions for their students. For those interested in hearing more about the use of Kaggle-in-Class as a teaching tool, Susan Holmes and Nelson Ray from Stanford University share their experience in a webinar organized by the Consortium for the Advancement of Undergraduate Statistics Education.

Interview Anne Milley JMP

Here is an interview with Anne Milley, a notable thought leader in the world of analytics. Anne is now Senior Director, Analytical Strategy in Product Marketing for JMP , the leading data visualization software from the SAS Institute.

Ajay-What do you think are the top 5 unique selling points of JMP compared to other statistical software in its category?

Anne-

JMP combines incredible analytic depth and breadth with interactive data visualization, creating a unique environment optimized for discovery and data-driven innovation.

With an extensible framework using JSL (JMP Scripting Language), and integration with SAS, R, and Excel, JMP becomes your analytic hub.

JMP is accessible to all kinds of users. A novice analyst can dig into an interactive report delivered by a custom JMP application. An engineer looking at his own data can use built-in JMP capabilities to discover patterns, and a developer can write code to extend JMP for herself or others.

State-of-the-art DOE capabilities make it easy for anyone to design and analyze efficient experiments to determine which adjustments will yield the greatest gains in quality or process improvement – before costly changes are made.

Not to mention, JMP products are exceptionally well designed and easy to use. See for yourself and check out the free trial at www.jmp.com.

Download a free 30-day trial of JMP.

Ajay- What are the challenges and opportunities of expanding JMP’s market share? Do you see JMP expanding its conferences globally to engage global audiences?

Anne-

We realized solid global growth in 2010. The release of JMP Pro and JMP Clinical last year along with continuing enhancements to the rest of the JMP family of products (JMP and JMP Genomics) should position us well for another good year.

With the growing interest in analytics as a means to sustained value creation, we have the opportunity to help people along their analytic journey – to get started, take the next step, or adopt new paradigms speeding their time to value. The challenge is doing that as fast as we would like.

We are hiring internationally to offer even more events, training and academic programs globally.

Ajay- What are the current and proposed educational and global academic initiatives of JMP? How can we see more JMP in universities across the world (say India- China etc)?

Anne-

We view colleges and universities both as critical incubators of future JMP users and as places where attitudes about data analysis and statistics are formed. We believe that a positive experience in learning statistics makes a person more likely to eventually want and need a product like JMP.

For most students – and particularly for those in applied disciplines of business, engineering and the sciences – the ability to make a statistics course relevant to their primary area of study fosters a positive experience. Fortunately, there is a trend in statistical education toward a more applied, data-driven approach, and JMP provides a very natural environment for both students and researchers.

Its user-friendly navigation, emphasis on data visualization and easy access to the analytics behind the graphics make JMP a compelling alternative to some of our more traditional competitors.

We’ve seen strong growth in the education markets in the last few years, and JMP is now used in nearly half of the top 200 universities in the US.

Internationally, we are at an earlier stage of market development, but we are currently working with both JMP and SAS country offices and their local academic programs to promote JMP. For example, we are working with members of the JMP China office and faculty at several universities in China to support the use of JMP in the development of a master’s curriculum in Applied Statistics there, touched on in this AMSTAT News article.

Ajay- What future trends do you see for 2011 in this market (say top 5)?

Anne-

Growing complexity of data (text, image, audio…) drives the need for more and better visualization and analysis capabilities to make sense of it all.

More “chief analytics officers” are making better use of analytic talent – people are the most important ingredient for success!

JMP has been on the vanguard of 64-bit development, and users are now catching up with us as 64-bit machines become more common.

Users should demand easy-to-use, exploratory and predictive modeling tools as well as robust tools to experiment and learn to help them make the best decisions on an ongoing basis.

All these factors and more fuel the need for the integration of flexible, extensible tools with popular analytic platforms.

Ajay-You enjoy organic gardening as a hobby. How do you think hobbies and unwind time help people be better professionals?

Anne-

I am lucky to work with so many people who view their work as a hobby. They have other interests too, though, some of which are work-related (statistics is relevant everywhere!). Organic gardening helps me put things in perspective and be present in the moment. More than work defines who you are. You can be passionate about your work as well as passionate about other things. I think it’s important to spend some leisure time in ways that bring you joy and contribute to your overall wellbeing and outlook.

Btw, nice interviews over the past several months—I hadn’t kept up, but will check it out more often!

Biography-  Source- http://www.sas.com/knowledge-exchange/business-analytics/biographies.html

  • Anne Milley

    Anne Milley

    Anne Milley is Senior Director of Analytics Strategy at JMP Product Marketing at SAS. Her ties to SAS began with bank failure prediction at Federal Home Loan Bank Dallas and continued at 7-Eleven Inc. She has authored papers and served on committees for F2006, KDD, SIAM, A2010 and several years of SAS’ annual data mining conference. Milley is a contributing faculty member for the International Institute of Analytics. anne.milley@jmp.com

Computer Education grants from Google

Image representing Google as depicted in Crunc...

Image via CrunchBase

message from the official google blog-

http://googleblog.blogspot.com/2011/01/supporting-computer-science-education.html

With programs like Computer Science for High School (CS4HS), we hope to increase the number of CS majors —and therefore the number of people entering into careers in CS—by promoting computer science curriculum at the high school level.

For the fourth consecutive year, we’re funding CS4HS to invest in the next generation of computer scientists and engineers. CS4HS is a workshop for high school and middle school computer science teachers that introduces new and emerging concepts in computing and provides tips, tools and guidance on how to teach them. The ultimate goals are to “train the trainer,” develop a thriving community of high school CS teachers and spread the word about the awe and beauty of computing.

If you’re a university, community college, or technical School in the U.S., Canada, Europe, Middle East or Africa and are interested in hosting a workshop at your institution, please visit www.cs4hs.com to submit an application for grant funding.Applications will be accepted between January 18, 2011 and February 18, 2011.

In addition to submitting your application, on the CS4HS website you’ll find info on how to organize a workshop, as well as websites and agendas from last year’s participants to give you an idea of how the workshops were structured in the past. There’s also a collection ofCS4HS curriculum modules that previous participating schools have shared for future organizers to use in their own program.

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