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Analytics 2012 Conference
from http://www.sas.com/events/analytics/us/index.html
Analytics 2012 Conference
SAS and more than 1,000 analytics experts gather at

Caesars Palace
Analytics 2012 Conference Details
Pre-Conference Workshops – Oct 7
Conference – Oct 8-9
Post-Conference Training – Oct 10-12
Caesars Palace, Las Vegas
Keynote Speakers
The following are confirmed keynote speakers for Analytics 2012.
Since he co-founded SAS in 1976, Jim Goodnight has served as the company’s Chief Executive Officer.
Dr. William Hakes is the CEO and co-founder of Link Analytics, an analytical technology company focused on mobile, energy and government verticals.
Tim Rey has written over 100 internal papers, published 21 external papers, and delivered numerous keynote presentations and technical talks at various quantitative methods forums. Recently he has co-chaired both forecasting and data mining conferences. He is currently in the process of co-writing a book, Applied Data Mining for Forecasting.
http://www.sas.com/events/analytics/us/train.html
Pre-Conference
Plan to come to Analytics 2012 a day early and participate in one of the pre-conference workshops or take a SAS Certification exam. Prices for all of the preconference workshops, except for SAS Sentiment Analysis Studio: Introduction to Building Models and the Business Analytics Consulting Workshops, are included in the conference package pricing. You will be prompted to select your pre-conference training options when you register.
Sunday Morning Workshop
SAS Sentiment Analysis Studio: Introduction to Building Models
This course provides an introduction to SAS Sentiment Analysis Studio. It is designed for system designers, developers, analytical consultants and managers who want to understand techniques and approaches for identifying sentiment in textual documents.
View outline
Sunday, Oct. 7, 8:30a.m.-12p.m. – $250
Sunday Afternoon Workshops
Business Analytics Consulting Workshops
This workshop is designed for the analyst, statistician, or executive who wants to discuss best-practice approaches to solving specific business problems, in the context of analytics. The two-hour workshop will be customized to discuss your specific analytical needs and will be designed as a one-on-one session for you, including up to five individuals within your company sharing your analytical goal. This workshop is specifically geared for an expert tasked with solving a critical business problem who needs consultation for developing the analytical approach required. The workshop can be customized to meet your needs, from a deep-dive into modeling methods to a strategic plan for analytic initiatives. In addition to the two hours at the conference location, this workshop includes some advanced consulting time over the phone, making it a valuable investment at a bargain price.
View outline
Sunday, Oct. 7; 1-3 p.m. or 3:30-5:30 p.m. – $200
Demand-Driven Forecasting: Sensing Demand Signals, Shaping and Predicting Demand
This half-day lecture teaches students how to integrate demand-driven forecasting into the consensus forecasting process and how to make the current demand forecasting process more demand-driven.
View outline
Sunday, Oct. 7; 1-5 p.m.
Forecast Value Added Analysis
Forecast Value Added (FVA) is the change in a forecasting performance metric (such as MAPE or bias) that can be attributed to a particular step or participant in the forecasting process. FVA analysis is used to identify those process activities that are failing to make the forecast any better (or might even be making it worse). This course provides step-by-step guidelines for conducting FVA analysis – to identify and eliminate the waste, inefficiency, and worst practices from your forecasting process. The result can be better forecasts, with fewer resources and less management time spent on forecasting.
View outline
Sunday, Oct. 7; 1-5 p.m.
SAS Enterprise Content Categorization: An Introduction
This course gives an introduction to methods of unstructured data analysis, document classification and document content identification. The course also uses examples as the basis for constructing parse expressions and resulting entities.
View outline
Sunday, Oct. 7; 1-5 p.m.
Introduction to Data Mining and SAS Enterprise Miner
This course serves as an introduction to data mining and SAS Enterprise Miner for Desktop software. It is designed for data analysts and qualitative experts as well as those with less of a technical background who want a general understanding of data mining.
View outline
Sunday, Oct. 7, 1-5 p.m.
Modeling Trend, Cycles, and Seasonality in Time Series Data Using PROC UCM
This half-day lecture teaches students how to model, interpret, and predict time series data using UCMs. The UCM procedure analyzes and forecasts equally spaced univariate time series data using the unobserved components models (UCM). This course is designed for business analysts who want to analyze time series data to uncover patterns such as trend, seasonal effects, and cycles using the latest techniques.
View outline
Sunday, Oct. 7, 1-5 p.m.
SAS Rapid Predictive Modeler
This seminar will provide a brief introduction to the use of SAS Enterprise Guide for graphical and data analysis. However, the focus will be on using SAS Enterprise Guide and SAS Enterprise Miner along with the Rapid Predictive Modeling component to build predictive models. Predictive modeling will be introduced using the SEMMA process developed with the introduction of SAS Enterprise Miner. Several examples will be used to illustrate the use of the Rapid Predictive Modeling component, and interpretations of the model results will be provided.
View outline
Sunday, Oct. 7, 1-5 p.m
Analytics 2012 Conference
A nice conference from the grand old institution of Analytics, SAS Institute’s annual analytic pow-wow.
I especially like some of the trainings- and wonder if they could be stored as e-learning modules for students/academics to review
in SAS’s extensive and generous Online Education Program.
Sunday Morning Workshop
SAS Sentiment Analysis Studio: Introduction to Building Models
This course provides an introduction to SAS Sentiment Analysis Studio. It is designed for system designers, developers, analytical consultants and managers who want to understand techniques and approaches for identifying sentiment in textual documents.
View outline
Sunday, Oct. 7, 8:30a.m.-12p.m. – $250
Sunday Afternoon Workshops
Business Analytics Consulting Workshops
This workshop is designed for the analyst, statistician, or executive who wants to discuss best-practice approaches to solving specific business problems, in the context of analytics. The two-hour workshop will be customized to discuss your specific analytical needs and will be designed as a one-on-one session for you, including up to five individuals within your company sharing your analytical goal. This workshop is specifically geared for an expert tasked with solving a critical business problem who needs consultation for developing the analytical approach required. The workshop can be customized to meet your needs, from a deep-dive into modeling methods to a strategic plan for analytic initiatives. In addition to the two hours at the conference location, this workshop includes some advanced consulting time over the phone, making it a valuable investment at a bargain price.
View outline
Sunday, Oct. 7; 1-3 p.m. or 3:30-5:30 p.m. – $200
Demand-Driven Forecasting: Sensing Demand Signals, Shaping and Predicting Demand
This half-day lecture teaches students how to integrate demand-driven forecasting into the consensus forecasting process and how to make the current demand forecasting process more demand-driven.
View outline
Sunday, Oct. 7; 1-5 p.m.
Forecast Value Added Analysis
Forecast Value Added (FVA) is the change in a forecasting performance metric (such as MAPE or bias) that can be attributed to a particular step or participant in the forecasting process. FVA analysis is used to identify those process activities that are failing to make the forecast any better (or might even be making it worse). This course provides step-by-step guidelines for conducting FVA analysis – to identify and eliminate the waste, inefficiency, and worst practices from your forecasting process. The result can be better forecasts, with fewer resources and less management time spent on forecasting.
View outline
Sunday, Oct. 7; 1-5 p.m.
SAS Enterprise Content Categorization: An Introduction
This course gives an introduction to methods of unstructured data analysis, document classification and document content identification. The course also uses examples as the basis for constructing parse expressions and resulting entities.
View outline
Sunday, Oct. 7; 1-5 p.m.
You can see more on this yourself at -
http://www.sas.com/events/analytics/us/
SAS Institute Financials 2011
SAS Institute has release it’s financials for 2011 at http://www.sas.com/news/preleases/2011financials.html,
Revenue surged across all solution and industry categories. Software to detect fraud saw a triple-digit jump. Revenue from on-demand solutions grew almost 50 percent. Growth from analytics and information management solutions were double digit, as were gains from customer intelligence, retail, risk and supply chain solutions
AJAY- and as a private company it is quite nice that they are willing to share so much information every year.
The graphics are nice ( and the colors much better than in 2010) , but pie-charts- seriously dude there is no way to compare how much SAS revenue is shifting across geographies or even across industries. So my two cents is – lose the pie charts, and stick to line graphs please for the share of revenue by country /industry.
In 2011, SAS grew staff 9.2 percent and reinvested 24 percent of revenue into research and development
AJAY- So that means 654 million dollars spent in Research and Development. I wonder if SAS has considered investing in much smaller startups (than it’s traditional strategy of doing all research in-house and completely acquiring a smaller company)
Even a small investment of say 5-10 million USD in open source , or even Phd level research projects could greatly increase the ROI on that.
That means
Analyzing a private company’s financials are much more fun than a public company, and I remember the words of my finance professor ( “dig , dig”) to compare 2011 results with 2010 results.
http://www.sas.com/news/preleases/2010financials.html
The percentage invested in R and D is exactly the same (24%) and the percentages of revenue earned from each geography is exactly the same . So even though revenue growth increased from 5.2 % to 9% in 2011, both the geographic spread of revenues and share R&D costs remained EXACTLY the same.
The Americas accounted for 46 percent of total revenue; Europe, Middle East and Africa (EMEA) 42 percent; and Asia Pacific 12 percent.
Overall, I think SAS remains a 35% market share (despite all that noise from IBM, SAS clones, open source) because they are good at providing solutions customized for industries (instead of just software products), the market for analytics is not saturated (it seems to be growing faster than 12% or is it) , and its ability to attract and retain the best analytical talent (which in a non -American tradition for a software company means no stock options, job security, and great benefits- SAS remains almost Japanese in HR practices).
In 2010, SAS grew staff by 2.4 percent, in 2011 SAS grew staff by 9 percent.
But I liked the directional statement made here-and I think that design interfaces, algorithmic and computational efficiencies should increase analytical time, time to think on business and reduce data management time further!
“What would you do with the extra time if your code ran in two minutes instead of five hours?” Goodnight challenged.
Faster Distinct Values using Proc Freq in SAS
I recently stumbled upon the nlevels function in SAS. It is awesome in terms of processing speed, given that the alternative is PROC SQL, COUNT(DISTINCT) etc etc
Truly the fastest way to find uniqueness in vars is use the nlevels in PROC FREQ – and why do we need to find levels in character variables- well to check for binary variables (2 values), constants (just 1 level), and simple data analysis stuff.
See this extract from-
ods output nlevels=levels;
proc freq data=good.sas nlevels;
tables _char_ /noprint;
quit;
SAS Blogs gets a makeover
SAS blogs gets a much needed makeover. Now if only they share some of the social media analytics
some more rather than just social media on analytics
One of the more professionally designed , managed and
passionate corporate blog series in my opinion
Seriously 27 blogs and not one blog on social media analytics despite
being the leading maker of such software!
http://www.sas.com/software/customer-intelligence/social-media-analytics/
New Community for Analytics- All Analytics
Here is a brand new community for analytics called allanalytics.com or http://www.allanalytics.com/
It seems to be managed by http://www.ubmtechweb.com/about/ who have a lot of other internet properties as well.One feature I disliked was the lengthy registration form which seems anarchic in these days of OAuth
Currently the community seems to be sponsored by SAS Institute, and it is basically a curated blog aggregator, like TeraData has SmartDataCollective , but much different than SAS Institutes’s affiliated sascommunity.org which is more for coders and consultants in SAS Language.
The community talks about analytics as a paradigm and way of getting business down. Have a look, notable names include Thomas C Redman and SAS’s very own Gary Cokins at http://www.allanalytics.com/bloggers.asp. The list of bloggers seems to be much lesser here, a kind of trade-off for high profile bloggers and thinkers. Worth a dekko or RSS subscription at least IMHO
Also see-
http://decisionstats.com/interview-thomas-c-redman-author-data-driven/
http://decisionstats.com/interview-gary-cokins-sas-institute/
#SAS 9.3 and #Rstats 2.13.1 Released
A bit early but the latest editions of both SAS and R were released last week.
SAS 9.3 is clearly a major release with multiple enhancements to make SAS both relevant and pertinent in enterprise software in the age of big data. Also many more R specific, JMP specific and partners like Teradata specific enhancements.
http://support.sas.com/software/93/index.html
Features
Data management
- Enhanced manageability for improved performance
- In-database processing (EL-T pushdown)
- Enhanced performance for loading oracle data
- New ET-L transforms
- Data access
Data quality
- SAS® Data Integration Server includes DataFlux® Data Management Platform for enhanced data quality
- Master Data Management (DataFlux® qMDM)
- Provides support for master hub of trusted entity data.
Analytics
- SAS® Enterprise Miner™
- New survival analysis predicts when an event will happen, not just if it will happen.
- New rate making capability for insurance predicts optimal insurance premium for individuals based on attributes known at application time.
- Time Series Data Mining node (experimental) applies data mining techniques to transactional, time-stamped data.
- Support Vector Machines node (experimental) provides a supervised machine learning method for prediction and classification.
- SAS® Forecast Server
- SAS Forecast Server is integrated with the SAP APO Demand Planning module to provide SAP users with access to a superior forecasting engine and automatic forecasting capabilities.
- SAS® Model Manager
- Seamless integration of R models with the ability to register and manage R models in SAS Model Manager.
- Ability to perform champion/challenger side-by-side comparisons between SAS and R models to see which model performs best for a specific need.
- SAS/OR® and SAS® Simulation Studio
- Optimization
- Simulation
- Automatic input distribution fitting using JMP with SAS Simulation Studio.
Text analytics
- SAS® Text Miner
- SAS® Enterprise Content Categorization
- SAS® Sentiment Analysis
Scalability and high-performance
- SAS® Analytics Accelerator for Teradata (new product)
- SAS® Grid Manager
LICENCE:
• No parts of R are now licensed solely under GPL-2. The licences for packages rpart and survival have been changed, which means that the licence terms for R as distributed are GPL-2 | GPL-3.
This is a maintenance release to consolidate various minor fixes to 2.13.0.
CHANGES IN R VERSION 2.13.1:
NEW FEATURES:
• iconv() no longer translates NA strings as "NA".
• persp(box = TRUE) now warns if the surface extends outside the
box (since occlusion for the box and axes is computed assuming
the box is a bounding box). (PR#202.)
• RShowDoc() can now display the licences shipped with R, e.g.
RShowDoc("GPL-3").
• New wrapper function showNonASCIIfile() in package tools.
• nobs() now has a "mle" method in package stats4.
• trace() now deals correctly with S4 reference classes and
corresponding reference methods (e.g., $trace()) have been added.
• xz has been updated to 5.0.3 (very minor bugfix release).
• tools::compactPDF() gets more compression (usually a little,
sometimes a lot) by using the compressed object streams of PDF
1.5.
• cairo_ps(onefile = TRUE) generates encapsulated EPS on platforms
with cairo >= 1.6.
• Binary reads (e.g. by readChar() and readBin()) are now supported
on clipboard connections. (Wish of PR#14593.)
• as.POSIXlt.factor() now passes ... to the character method
(suggestion of Joshua Ulrich). [Intended for R 2.13.0 but
accidentally removed before release.]
• vector() and its wrappers such as integer() and double() now warn
if called with a length argument of more than one element. This
helps track down user errors such as calling double(x) instead of
as.double(x).
INSTALLATION:
• Building the vignette PDFs in packages grid and utils is now part
of running make from an SVN checkout on a Unix-alike: a separate
make vignettes step is no longer required.
These vignettes are now made with keep.source = TRUE and hence
will be laid out differently.
• make install-strip failed under some configuration options.
• Packages can customize non-standard installation of compiled code
via a src/install.libs.R script. This allows packages that have
architecture-specific binaries (beyond the package's shared
objects/DLLs) to be installed in a multi-architecture setting.
SWEAVE & VIGNETTES:
• Sweave() and Stangle() gain an encoding argument to specify the
encoding of the vignette sources if the latter do not contain a
\usepackage[]{inputenc} statement specifying a single input
encoding.
• There is a new Sweave option figs.only = TRUE to run each figure
chunk only for each selected graphics device, and not first using
the default graphics device. This will become the default in R
2.14.0.
• Sweave custom graphics devices can have a custom function
foo.off() to shut them down.
• Warnings are issued when non-portable filenames are found for
graphics files (and chunks if split = TRUE). Portable names are
regarded as alphanumeric plus hyphen, underscore, plus and hash
(periods cause problems with recognizing file extensions).
• The Rtangle() driver has a new option show.line.nos which is by
default false; if true it annotates code chunks with a comment
giving the line number of the first line in the sources (the
behaviour of R >= 2.12.0).
• Package installation tangles the vignette sources: this step now
converts the vignette sources from the vignette/package encoding
to the current encoding, and records the encoding (if not ASCII)
in a comment line at the top of the installed .R file.
DEPRECATED AND DEFUNCT:
• The internal functions .readRDS() and .saveRDS() are now
deprecated in favour of the public functions readRDS() and
saveRDS() introduced in R 2.13.0.
• Switching off lazy-loading of code _via_ the LazyLoad field of
the DESCRIPTION file is now deprecated. In future all packages
will be lazy-loaded.
• The off-line help() types "postscript" and "ps" are deprecated.
UTILITIES:
• R CMD check on a multi-architecture installation now skips the
user's .Renviron file for the architecture-specific tests (which
do read the architecture-specific Renviron.site files). This is
consistent with single-architecture checks, which use
--no-environ.
• R CMD build now looks for DESCRIPTION fields BuildResaveData and
BuildKeepEmpty for per-package overrides. See ‘Writing R
Extensions’.
BUG FIXES:
• plot.lm(which = 5) was intended to order factor levels in
increasing order of mean standardized residual. It ordered the
factor labels correctly, but could plot the wrong group of
residuals against the label. (PR#14545)
• mosaicplot() could clip the factor labels, and could overlap them
with the cells if a non-default value of cex.axis was used.
(Related to PR#14550.)
• dataframe[[row,col]] now dispatches on [[ methods for the
selected column (spotted by Bill Dunlap).
• sort.int() would strip the class of an object, but leave its
object bit set. (Reported by Bill Dunlap.)
• pbirthday() and qbirthday() did not implement the algorithm
exactly as given in their reference and so were unnecessarily
inaccurate.
pbirthday() now solves the approximate formula analytically
rather than using uniroot() on a discontinuous function.
The description of the problem was inaccurate: the probability is
a tail probablity (‘2 _or more_ people share a birthday’)
• Complex arithmetic sometimes warned incorrectly about producing
NAs when there were NaNs in the input.
• seek(origin = "current") incorrectly reported it was not
implemented for a gzfile() connection.
• c(), unlist(), cbind() and rbind() could silently overflow the
maximum vector length and cause a segfault. (PR#14571)
• The fonts argument to X11(type = "Xlib") was being ignored.
• Reading (e.g. with readBin()) from a raw connection was not
advancing the pointer, so successive reads would read the same
value. (Spotted by Bill Dunlap.)
• Parsed text containing embedded newlines was printed incorrectly
by as.character.srcref(). (Reported by Hadley Wickham.)
• decompose() used with a series of a non-integer number of periods
returned a seasonal component shorter than the original series.
(Reported by Rob Hyndman.)
• fields = list() failed for setRefClass(). (Reported by Michael
Lawrence.)
• Reference classes could not redefine an inherited field which had
class "ANY". (Reported by Janko Thyson.)
• Methods that override previously loaded versions will now be
installed and called. (Reported by Iago Mosqueira.)
• addmargins() called numeric(apos) rather than
numeric(length(apos)).
• The HTML help search sometimes produced bad links. (PR#14608)
• Command completion will no longer be broken if tail.default() is
redefined by the user. (Problem reported by Henrik Bengtsson.)
• LaTeX rendering of markup in titles of help pages has been
improved; in particular, \eqn{} may be used there.
• isClass() used its own namespace as the default of the where
argument inadvertently.
• Rd conversion to latex mis-handled multi-line titles (including
cases where there was a blank line in the \title section).








