Google Plus Gaming vs Facebook Gaming

After a few hiccups, Facebook has gotten the notifications scrolling back and much better than Google Plus. This gives it a cleaner advantage in social gaming interface – even for the same game. and of course many more gamers!

Clearly the games stream is much more efficiently designed in FB, probably because they need to earn some ad revenue- that forces you to think more optimally for space. FB interface is also bug free compared to the constant error in G+ (error changing circle membership– ideally I wanted to create one gaming circle for all gaming friends)

See this  – not just compare the games stream/notifications only

vs

How to make an analytics project?

Some of the process methodologies I have used and been exposed to while making analytics projects are-1) DMAIC/Six Sigma

While Six Sigma was initially a quality control system, it has also been very succesful in managing projects. The various stages of an analytical project can be divided using the DMAIC methodology.

DMAIC stands for

  • Define
  • Measure
  • Analyze
  • Improve
  • Control

Related to this is DMADV, ( “Design For Six Sigma”)

  • Define
  • Measure and identify CTQs
  • Analyze
  • Design
  • Verify

2) CRISP
CRISP-DM stands for Cross Industry Standard Process for Data Mining

CRISP-DM breaks the process of data mining into six major phases- and these can be used for business analytics projects as well.

  • Business Understanding
  • Data Understanding
  • Data Preparation
  • Modeling
  • Evaluation
  • Deployment

3) SEMMA
SEMMA  stands for

  • sample
  • explore
  • modify
  • model
  • assess

4) ISO 9001

ISO 9001 is a certification as well as a philosophy for making a Quality Management System to measure , reduce and eliminate error and customer complaints. Any customer complaint or followup has to be treated as an error, logged, and investigated for control.

5) LEAN
LEAN is a philosophy to eliminate Wastage in a process. Applying LEAN principles to analytics projects helps a lot in eliminating project bottlenecks, technology compatibility issues and data quality resolution. I think LEAN would be great in data quality issues, and IT infrastructure design because that is where the maximum waste is observed in analytics projects.

6) Demings Plan Do Check Act cycle.

Use R for Business- Competition worth $ 20,000 #rstats

All you contest junkies, R lovers and general change the world people, here’s a new contest to use R in a business application

http://www.revolutionanalytics.com/news-events/news-room/2011/revolution-analytics-launches-applications-of-r-in-business-contest.php

REVOLUTION ANALYTICS LAUNCHES “APPLICATIONS OF R IN BUSINESS” CONTEST

$20,000 in Prizes for Users Solving Business Problems with R

 

PALO ALTO, Calif. – September 1, 2011 – Revolution Analytics, the leading commercial provider of R software, services and support, today announced the launch of its “Applications of R in Business” contest to demonstrate real-world uses of applying R to business problems. The competition is open to all R users worldwide and submissions will be accepted through October 31. The Grand Prize winner for the best application using R or Revolution R will receive $10,000.

The bonus-prize winner for the best application using features unique to Revolution R Enterprise – such as itsbig-data analytics capabilities or its Web Services API for R – will receive $5,000. A panel of independent judges drawn from the R and business community will select the grand and bonus prize winners. Revolution Analytics will present five honorable mention prize winners each with $1,000.

“We’ve designed this contest to highlight the most interesting use cases of applying R and Revolution R to solving key business problems, such as Big Data,” said Jeff Erhardt, COO of Revolution Analytics. “The ability to process higher-volume datasets will continue to be a critical need and we encourage the submission of applications using large datasets. Our goal is to grow the collection of online materials describing how to use R for business applications so our customers can better leverage Big Analytics to meet their analytical and organizational needs.”

To enter Revolution Analytics’ “Applications of R in Business” competition Continue reading “Use R for Business- Competition worth $ 20,000 #rstats”

The Top Statisticians in the World

 

 

 

 

 

 

http://en.wikipedia.org/wiki/John_Tukey

 

John Tukey

From Wikipedia, the free encyclopedia
John Tukey

John Wilder Tukey
Born June 16, 1915
New Bedford, Massachusetts, USA
Died July 26, 2000 (aged 85)
New Brunswick, New Jersey
Residence United States
Nationality American
Fields Mathematician
Institutions Bell Labs
Princeton University
Alma mater Brown University
Princeton University
Doctoral advisor Solomon Lefschetz
Doctoral students Frederick Mosteller
Kai Lai Chung
Known for FFT algorithm
Box plot
Coining the term ‘bit’
Notable awards Samuel S. Wilks Award (1965)
National Medal of Science (USA) in Mathematical, Statistical, and Computational Sciences (1973)
Shewhart Medal (1976)
IEEE Medal of Honor (1982)
Deming Medal (1982)
James Madison Medal (1984)
Foreign Member of the Royal Society(1991)

John Wilder Tukey ForMemRS[1] (June 16, 1915 – July 26, 2000) was an American statistician.

Contents

[hide]

[edit]Biography

Tukey was born in New Bedford, Massachusetts in 1915, and obtained a B.A. in 1936 and M.Sc.in 1937, in chemistry, from Brown University, before moving to Princeton University where he received a Ph.D. in mathematics.[2]

During World War II, Tukey worked at the Fire Control Research Office and collaborated withSamuel Wilks and William Cochran. After the war, he returned to Princeton, dividing his time between the university and AT&T Bell Laboratories.

Among many contributions to civil society, Tukey served on a committee of the American Statistical Association that produced a report challenging the conclusions of the Kinsey Report,Statistical Problems of the Kinsey Report on Sexual Behavior in the Human Male.

He was awarded the IEEE Medal of Honor in 1982 “For his contributions to the spectral analysis of random processes and the fast Fourier transform (FFT) algorithm.”

Tukey retired in 1985. He died in New Brunswick, New Jersey on July 26, 2000.

[edit]Scientific contributions

His statistical interests were many and varied. He is particularly remembered for his development with James Cooley of the Cooley–Tukey FFT algorithm. In 1970, he contributed significantly to what is today known as the jackknife estimation—also termed Quenouille-Tukey jackknife. He introduced the box plot in his 1977 book,”Exploratory Data Analysis“.

Tukey’s range test, the Tukey lambda distributionTukey’s test of additivity and Tukey’s lemma all bear his name. He is also the creator of several little-known methods such as the trimean andmedian-median line, an easier alternative to linear regression.

In 1974, he developed, with Jerome H. Friedman, the concept of the projection pursuit.[3]

http://en.wikipedia.org/wiki/Ronald_Fisher

Sir Ronald Aylmer Fisher FRS (17 February 1890 – 29 July 1962) was an English statistician,evolutionary biologisteugenicist and geneticist. Among other things, Fisher is well known for his contributions to statistics by creating Fisher’s exact test and Fisher’s equationAnders Hald called him “a genius who almost single-handedly created the foundations for modern statistical science”[1] while Richard Dawkins named him “the greatest biologist since Darwin“.[2]

 

contacts.xls

http://en.wikipedia.org/wiki/William_Sealy_Gosset

William Sealy Gosset (June 13, 1876–October 16, 1937) is famous as a statistician, best known by his pen name Student and for his work on Student’s t-distribution.

Born in CanterburyEngland to Agnes Sealy Vidal and Colonel Frederic Gosset, Gosset attendedWinchester College before reading chemistry and mathematics at New College, Oxford. On graduating in 1899, he joined the Dublin brewery of Arthur Guinness & Son.

Guinness was a progressive agro-chemical business and Gosset would apply his statistical knowledge both in the brewery and on the farm—to the selection of the best yielding varieties ofbarley. Gosset acquired that knowledge by study, trial and error and by spending two terms in 1906–7 in the biometric laboratory of Karl Pearson. Gosset and Pearson had a good relationship and Pearson helped Gosset with the mathematics of his papers. Pearson helped with the 1908 papers but he had little appreciation of their importance. The papers addressed the brewer’s concern with small samples, while the biometrician typically had hundreds of observations and saw no urgency in developing small-sample methods.

Another researcher at Guinness had previously published a paper containing trade secrets of the Guinness brewery. To prevent further disclosure of confidential information, Guinness prohibited its employees from publishing any papers regardless of the contained information. However, after pleading with the brewery and explaining that his mathematical and philosophical conclusions were of no possible practical use to competing brewers, he was allowed to publish them, but under a pseudonym (“Student”), to avoid difficulties with the rest of the staff.[1] Thus his most famous achievement is now referred to as Student’s t-distribution, which might otherwise have been Gosset’s t-distribution.

Top 25 Errors in Programming that lead to hacker attacks

I am elaborating an earlier article on https://decisionstats.com/top-25-most-dangerous-software-errors/ based on my continued research into cyber conflict and strategy. My inputs are in italics – the rest is a condensed article for further thought.

This is thus a very useful initiative for the world to follow and upgrade their cyber security.

It is in accordance with the US policy to secure its cyber infrastructure (http://www.whitehouse.gov/the-press-office/remarks-president-securing-our-nations-cyber-infrastructure)  and countries like India, and even Europe as well as other nations could do well to atleast benchmark their own security practices in software and digital infrastructure with it. There seems to much better technical coordination between rogue hackers than patriotic hackers imho 😉


The Department of Homeland Security of the United States of America has just launched a list of top 25 errors in programming or creating software that increase vulnerability to hacking attacks. The list which is available at http://cwe.mitre.org/top25/index.html lists down a methodology fo measuring vulnerability called Common Weakness Scoring System (CWSS) and uses that score to rank the various errors as well as suggestions to eliminate these weaknesses or errors.
Measuring Weaknesses

The importance of a weakness (that arises due to software bugs) may vary depending on business usage or project implementation, the technologies , operating systems and computing environments in use, and the risk or threat perception.The Common Weakness Scoring System (CWSS) provides a mechanism for scoring weaknesses. and provides a framework for prioritizing security errors (“weaknesses”) that are discovered in software applications.
Identifying Weaknesses
For example the number 1 weakness is shown with
1CWE-89: Improper Neutralization of Special Elements used in an SQL Command (‘SQL Injection’).
The rest of the weaknesses are

RANK SCORE ID NAME
[1] 93.8 CWE-89 Improper Neutralization of Special Elements used in an SQL Command (‘SQL Injection’)
[2] 83.3 CWE-78 Improper Neutralization of Special Elements used in an OS Command (‘OS Command Injection’)
[3] 79.0 CWE-120 Buffer Copy without Checking Size of Input (‘Classic Buffer Overflow’)
[4] 77.7 CWE-79 Improper Neutralization of Input During Web Page Generation (‘Cross-site Scripting’)
[5] 76.9 CWE-306 Missing Authentication for Critical Function
[6] 76.8 CWE-862 Missing Authorization
[7] 75.0 CWE-798 Use of Hard-coded Credentials
[8] 75.0 CWE-311 Missing Encryption of Sensitive Data
[9] 74.0 CWE-434 Unrestricted Upload of File with Dangerous Type
[10] 73.8 CWE-807 Reliance on Untrusted Inputs in a Security Decision
[11] 73.1 CWE-250 Execution with Unnecessary Privileges
[12] 70.1 CWE-352 Cross-Site Request Forgery (CSRF)
[13] 69.3 CWE-22 Improper Limitation of a Pathname to a Restricted Directory (‘Path Traversal’)
[14] 68.5 CWE-494 Download of Code Without Integrity Check
[15] 67.8 CWE-863 Incorrect Authorization
[16] 66.0 CWE-829 Inclusion of Functionality from Untrusted Control Sphere
[17] 65.5 CWE-732 Incorrect Permission Assignment for Critical Resource
[18] 64.6 CWE-676 Use of Potentially Dangerous Function
[19] 64.1 CWE-327 Use of a Broken or Risky Cryptographic Algorithm
[20] 62.4 CWE-131 Incorrect Calculation of Buffer Size
[21] 61.5 CWE-307 Improper Restriction of Excessive Authentication Attempts
[22] 61.1 CWE-601 URL Redirection to Untrusted Site (‘Open Redirect’)
[23] 61.0 CWE-134 Uncontrolled Format String
[24] 60.3 CWE-190 Integer Overflow or Wraparound
[25] 59.9 CWE-759 Use of a One-Way Hash without a Salt
Details of each weakness is given by http://cwe.mitre.org/top25/index.html#Details
It includes Summary , Weakness Prevalence, Consequences, Remediation Cost, Ease of Detection ,Attacker Awareness and Attack Frequency .In addition the following sections describe each software vulnerability in detail- Technical Details ,Code Examples ,Detection Methods ,References,Prevention and Mitigation, Related CWEs and Related Attack Patterns.
Other important software weaknesses are –

[26] CWE-770: Allocation of Resources Without Limits or Throttling
[27] CWE-129: Improper Validation of Array Index
[28] CWE-754: Improper Check for Unusual or Exceptional Conditions
[29] CWE-805: Buffer Access with Incorrect Length Value
[30] CWE-838: Inappropriate Encoding for Output Context
[31] CWE-330: Use of Insufficiently Random Values
[32] CWE-822: Untrusted Pointer Dereference
[33] CWE-362: Concurrent Execution using Shared Resource with Improper Synchronization (‘Race Condition’)
[34] CWE-212: Improper Cross-boundary Removal of Sensitive Data
[35] CWE-681: Incorrect Conversion between Numeric Types
[36] CWE-476: NULL Pointer Dereference
[37] CWE-841: Improper Enforcement of Behavioral Workflow
[38] CWE-772: Missing Release of Resource after Effective Lifetime
[39] CWE-209: Information Exposure Through an Error Message
[40] CWE-825: Expired Pointer Dereference
[41] CWE-456: Missing Initialization
Mitigating Weaknesses
Here is an example of the new matrix for migrations that also list the top 25 errors . This thus shows a way to fix the weaknesses and relative impact on each weakness by the following mitigations.
http://cwe.mitre.org/top25/mitigations.html#MitigationMatrix

Effectiveness ratings include:

  • High: The mitigation has well-known, well-understood strengths and limitations; there is good coverage with respect to variations of the weakness.
  • Moderate: The mitigation will prevent the weakness in multiple forms, but it does not have complete coverage of the weakness.
  • Limited: The mitigation may be useful in limited circumstances, only be applicable to a subset of this weakness type, require extensive training/customization, or give limited visibility.
  • Defense in Depth (DiD): The mitigation may not necessarily prevent the weakness, but it may help to minimize the potential impact when an attacker exploits the weakness.

Within the matrix, the following mitigations are identified:

 

  • M1: Establish and maintain control over all of your inputs.
  • M2: Establish and maintain control over all of your outputs.
  • M3: Lock down your environment.
  • M4: Assume that external components can be subverted, and your code can be read by anyone.
  • M5: Use industry-accepted security features instead of inventing your own.

The following general practices are omitted from the matrix:

  • GP1: Use libraries and frameworks that make it easier to avoid introducing weaknesses.
  • GP2: Integrate security into the entire software development lifecycle.
  • GP3: Use a broad mix of methods to comprehensively find and prevent weaknesses.
  • GP4: Allow locked-down clients to interact with your software.

 

M1 M2 M3 M4 M5 CWE
High DiD Mod CWE-22: Improper Limitation of a Pathname to a Restricted Directory (‘Path Traversal’)
Mod High DiD Ltd CWE-78: Improper Neutralization of Special Elements used in an OS Command (‘OS Command Injection’)
Mod High Ltd CWE-79: Improper Neutralization of Input During Web Page Generation (‘Cross-site Scripting’)
Mod High DiD Ltd CWE-89: Improper Neutralization of Special Elements used in an SQL Command (‘SQL Injection’)
Mod DiD Ltd CWE-120: Buffer Copy without Checking Size of Input (‘Classic Buffer Overflow’)
Mod DiD Ltd CWE-131: Incorrect Calculation of Buffer Size
High DiD Mod CWE-134: Uncontrolled Format String
Mod DiD Ltd CWE-190: Integer Overflow or Wraparound
High CWE-250: Execution with Unnecessary Privileges
Mod Mod CWE-306: Missing Authentication for Critical Function
Mod CWE-307: Improper Restriction of Excessive Authentication Attempts
DiD CWE-311: Missing Encryption of Sensitive Data
High CWE-327: Use of a Broken or Risky Cryptographic Algorithm
Ltd CWE-352: Cross-Site Request Forgery (CSRF)
Mod DiD Mod CWE-434: Unrestricted Upload of File with Dangerous Type
DiD CWE-494: Download of Code Without Integrity Check
Mod Mod Ltd CWE-601: URL Redirection to Untrusted Site (‘Open Redirect’)
Mod High DiD CWE-676: Use of Potentially Dangerous Function
Ltd DiD Mod CWE-732: Incorrect Permission Assignment for Critical Resource
High CWE-759: Use of a One-Way Hash without a Salt
DiD High Mod CWE-798: Use of Hard-coded Credentials
Mod DiD Mod Mod CWE-807: Reliance on Untrusted Inputs in a Security Decision
High High High CWE-829: Inclusion of Functionality from Untrusted Control Sphere
DiD Mod Mod CWE-862: Missing Authorization
DiD Mod CWE-863: Incorrect Authorization

Analytics 2011 Conference

From http://www.sas.com/events/analytics/us/

The Analytics 2011 Conference Series combines the power of SAS’s M2010 Data Mining Conference and F2010 Business Forecasting Conference into one conference covering the latest trends and techniques in the field of analytics. Analytics 2011 Conference Series brings the brightest minds in the field of analytics together with hundreds of analytics practitioners. Join us as these leading conferences change names and locations. At Analytics 2011, you’ll learn through a series of case studies, technical presentations and hands-on training. If you are in the field of analytics, this is one conference you can’t afford to miss.

Conference Details

October 24-25, 2011
Grande Lakes Resort
Orlando, FL

Analytics 2011 topic areas include:

RapidMiner launches extensions marketplace

For some time now, I had been hoping for a place where new package or algorithm developers get at least a fraction of the money that iPad or iPhone application developers get. Rapid Miner has taken the lead in establishing a marketplace for extensions. Is there going to be paid extensions as well- I hope so!!

This probably makes it the first “app” marketplace in open source and the second app marketplace in analytics after salesforce.com

It is hard work to think of new algols, and some of them can really be usefull.

Can we hope for #rstats marketplace where people downloading say ggplot3.0 atleast get a prompt to donate 99 cents per download to Hadley Wickham’s Amazon wishlist. http://www.amazon.com/gp/registry/1Y65N3VFA613B

Do you think it is okay to pay 99 cents per iTunes song, but not pay a cent for open source software.

I dont know- but I am just a capitalist born in a country that was socialist for the first 13 years of my life. Congratulations once again to Rapid Miner for innovating and leading the way.

http://rapid-i.com/component/option,com_myblog/show,Rapid-I-Marketplace-Launched.html/Itemid,172

RapidMinerMarketplaceExtensions 30 May 2011
Rapid-I Marketplace Launched by Simon Fischer

Over the years, many of you have been developing new RapidMiner Extensions dedicated to a broad set of topics. Whereas these extensions are easy to install in RapidMiner – just download and place them in the plugins folder – the hard part is to find them in the vastness that is the Internet. Extensions made by ourselves at Rapid-I, on the other hand,  are distributed by the update server making them searchable and installable directly inside RapidMiner.

We thought that this was a bit unfair, so we decieded to open up the update server to the public, and not only this, we even gave it a new look and name. The Rapid-I Marketplace is available in beta mode at http://rapidupdate.de:8180/ . You can use the Web interface to browse, comment, and rate the extensions, and you can use the update functionality in RapidMiner by going to the preferences and entering http://rapidupdate.de:8180/UpdateServer/ as the update server URL. (Once the beta test is complete, we will change the port back to 80 so we won’t have any firewall problems.)

As an Extension developer, just register with the Marketplace and drop me an email (fischer at rapid-i dot com) so I can give you permissions to upload your own extension. Upload is simple provided you use the standard RapidMiner Extension build process and will boost visibility of your extension.

Looking forward to see many new extensions there soon!

Disclaimer- Decisionstats is a partner of Rapid Miner. I have been liking the software for a long long time, and recently agreed to partner with them just like I did with KXEN some years back, and with Predictive AnalyticsConference, and Aster Data until last year.

I still think Rapid Miner is a very very good software,and a globally created software after SAP.

Here is the actual marketplace

http://rapidupdate.de:8180/UpdateServer/faces/index.xhtml

Welcome to the Rapid-I Marketplace Public Beta Test

The Rapid-I Marketplace will soon replace the RapidMiner update server. Using this marketplace, you can share your RapidMiner extensions and make them available for download by the community of RapidMiner users. Currently, we are beta testing this server. If you want to use this server in RapidMiner, you must go to the preferences and enter http://rapidupdate.de:8180/UpdateServer for the update url. After the beta test, we will change the port back to 80, which is currently occupied by the old update server. You can test the marketplace as a user (downloading extensions) and as an Extension developer. If you want to publish your extension here, please let us know via the contact form.

Hot Downloads
«« « 1 2 3 » »»
[Icon]The Image Processing Extension provides operators for handling image data. You can extract attributes describing colour and texture in the image, you can make several transformation of a image data which allows you to perform segmentation and detection of suspicious areas in image data.The extension provides many of image transformation and extraction operators ranging from Wavelet Decomposition, Hough Circle to Block Difference of Inverse probabilities.

[Icon]RapidMiner is unquestionably the world-leading open-source system for data mining. It is available as a stand-alone application for data analysis and as a data mining engine for the integration into own products. Thousands of applications of RapidMiner in more than 40 countries give their users a competitive edge.

  • Data IntegrationAnalytical ETLData Analysis, and Reporting in one single suite
  • Powerful but intuitive graphical user interface for the design of analysis processes
  • Repositories for process, data and meta data handling
  • Only solution with meta data transformation: forget trial and error and inspect results already during design time
  • Only solution which supports on-the-fly error recognition and quick fixes
  • Complete and flexible: Hundreds of data loading, data transformation, data modeling, and data visualization methods
[Icon]All modeling methods and attribute evaluation methods from the Weka machine learning library are available within RapidMiner. After installing this extension you will get access to about 100 additional modelling schemes including additional decision trees, rule learners and regression estimators.This extension combines two of the most widely used open source data mining solutions. By installing it, you can extend RapidMiner to everything what is possible with Weka while keeping the full analysis, preprocessing, and visualization power of RapidMiner.

[Icon]Finally, the two most widely used data analysis solutions – RapidMiner and R – are connected. Arbitrary R models and scripts can now be directly integrated into the RapidMiner analysis processes. The new R perspective offers the known R console together with the great plotting facilities of R. All variables and R scripts can be organized in the RapidMiner Repository.A directly included online help and multi-line editing makes the creation of R scripts much more comfortable.