Larry Larusso

9月 142017
 

You might not know it by looking at me (I’m rounding up when I tell people I’m 5’8”) but I’m a huge basketball fan. I’ve been following the sport since I was 10, coaching it for the last decade and playing on teams throughout my life, still dedicating my winters [...]

The post How to learn from The Dream Team of experts at Analytics Experience 2017...even if you're not going appeared first on SAS Learning Post.

8月 282017
 

My daughter is a junior in high school, and for almost every semester she’s taken an online course as part of her studies. This semester she’s taking Spanish 3, an advanced level course where every word of instruction is spoken in Spanish. Each morning she joins the class from our [...]

The post Learn SAS from wherever you are: Live Web classes go global appeared first on SAS Learning Post.

7月 262017
 

Trivial Pursuit, Justin Bieber and Timbits. Some pretty great things have come from Canada, eh? Well, you can go ahead and add expert SAS programmers to that impressive list.

In this video, six Canadian SAS programmers, with more than 115 years of SAS programming experience between them, share some of their favorite, little-known SAS programming tips. You're sure to discover a new trick or two.

The video includes the following tips and more:

  • Standardizing and documenting your SAS program.
  • Creating parameter lookup tables.
  • Declaring your macro variables.
  • Using the Characterize Data task in SAS Enterprise Guide.
  • Data exploration best practices for SAS Enterprise Guide.

Looking for more great tips to help bring your SAS programming skills to the next level? Check out these great resources and learn even more from your SAS peers:

  • SAS Support Communities: Peer-to-peer support for SAS users about programming, data analysis, installation and deployment issues, tips and best practices and a whole lot more.
  • SAS Blogs: Connecting you to people, products and ideas from SAS with technical tips, support information and more.
  • SAS Newsletters: The latest news, tips, tricks and resources from SAS, plus advice and industry knowledge gleaned from top experts and other SAS users.

 

Programming tips from experienced SAS users was published on SAS Users.

4月 182017
 

In addition to his day job as Chief Technology Officer at SAS, Oliver Schabenberger is a committed lifelong learner. During his opening remarks for the SAS Technology Connection at SAS Global Forum 2017, Schabenberger confessed to having a persistent nervous curiosity, and insisted that he’s “learning every day.” And, he encouraged attendees to do the same, officially proclaiming lifelong learning as a primary theme of the conference and announcing a social media campaign to explore the issue with attendees.

This theme of lifelong learning served a backdrop – figuratively at first, literally once the conference began! – when Schabenberger, R&D Vice President Oita Coleman and Senior R&D Project Manager Lisa Morton sat down earlier this year to determine the focus for the Catalyst Café at SAS Global Forum 2017.

A centerpiece of SAS Global Forum’s Quad area, the Catalyst Café is an interactive space for attendees to try out new SAS technology and provide SAS R&D with insight to help guide future software development. At its core, the Catalyst Café is an incubator for innovation, making it the perfect place to highlight the power of learning.

After consulting with SAS Social Media Manager Kirsten Hamstra and her team, Schabenberger, Coleman and Morton decided to explore the theme by asking three questions related to lifelong learning, one a day during each day of the conference. Attendees, and others following the conference via social media channels, would respond using the hashtag #lifelearner. Morton then visualized the responses on a 13-foot-long by 8-foot-high wall, appropriately titled the Social Listening Mural, for all to enjoy during the event.

Questions for a #lifelearner

The opening day of the conference brought this question:

Day two featured this question:

Finally, day three, and this question:

"Committed to lifelong learning"

Hamstra said the response from the SAS community was overwhelming, with hundreds of individuals contributing.

Morton working on the Social Listening Mural at the SAS Global Forum Catalyst Café

“It was so interesting to see what people shared as their first jobs,” said Morton. “One started out as a bus boy and ended up a CEO, another went from stocking shelves to analytical consulting, and a couple said they immediately started their analytical careers by becoming data analysts right out of school.”

The “what do you want to learn next?” question brought some interesting responses as well. While many respondents cited topics you’d expect from a technically-inclined crowd – things like SAS Viya, the Go Programming Language and SASPy – others said they wanted to learn Italian, how to design websites or teach kids how to play soccer.

Morton said the connections that were made during the process was fascinating and made the creation of the mural so simple and inspiring. “The project showed me how incredibly diverse our SAS users are and what a wide variety of backgrounds and interests they have.”

In the end, Morton said she learned one thing for sure about SAS users: “It’s clear our users are just as committed to lifelong learning as we are here at SAS!”

My guess is that wherever you’ll find Schabenberger at this moment – writing code in his office, behind a book at the campus library, or discussing AI with Dr. Goodnight – he’s nodding in agreement.

The final product

Nurturing the #lifelearner in all of us was published on SAS Users.

3月 222017
 

Editor's note: The following post is from Scott Leslie, PhD, Manager of Advanced Analytics for MedImpact Healthcare Systems, Inc. Scott will be one of the Code Doctors at SAS Global Forum 2017.

Learn more about Scott.

VISIT THE CODE CLINIC AT SASGF 2017

$0 copay, no deductible.  No waiting rooms, no outdated magazines. What kind of doctor’s office is this? While we might not be able to help with that nasty cough, SAS Code Doctors are here to help – when it comes to your SAS code, that is.

Yes, the Code Doctors return to SAS Global Forum 2017! This year the Code Clinic will have over 20 SAS experts on-call to answer your questions on syntax, SAS Solutions, best practices and concepts across a broad range of SAS topics/applications, including Base SAS, macros, report writing, ODS, SQL, SAS Enterprise Guide, statistics, and more. It’s a fantastic opportunity to review code, ask questions, develop and brainstorm with peers who have decades of experience using SAS. Bring your code on paper, a flash drive, or a laptop. We’ll have 3-4 laptops with several versions of SAS software installed: 9.1.3 to 9.4 and EG 4.1 to 7.1. And if we can’t answer your coding question at the clinic, we can easily refer you to a specialist, namely the SAS R&D section of the Quad.

So, take advantage of this personalized learning experience in the Lower Quad area of the conference. Clinic office hours are:

  • Monday 4/3, 10:00 am - 3:30 pm
  • Tuesday 4/3, 9:30 am – 2:00 pm and 3:30 pm – 6:00 pm

Here’s the detailed schedule of our all-star code doctor lineup. If you haven’t heard of these names yet, you have now...

/*Just by reading this blog…*/.

 

About Scott Leslie

Scott Leslie, PhD, is Manager of Advanced Analytics for MedImpact Healthcare Systems, Inc. with over15 years of SAS® experience in the pharmacy benefits and medical management field. His SAS knowledge areas include SAS/STAT, Enterprise Guide, and Visual Analytics. Scott presents at local, regional and international SAS user group conferences as well at various clinical and scientific conferences. He is a former executive committee member of the Western Users of SAS Software (WUSS) and contributes to the San Diego SAS Users’ Group (SANDS).

Visit the code clinic at SAS Global Forum was published on SAS Users.

3月 092017
 

Editor’s note: This is the first in a series of posts to help current SAS programmers add SAS Viya to their analytics skillset. In this post, SAS instructors Stacey Syphus and Marc Huber introduce you to the new Transitioning from Programming in SAS 9 to SAS Viya video library, designed to show SAS programmers [...]

The post Transitioning from programming in SAS 9 to SAS Viya appeared first on SAS Learning Post.

3月 032017
 

Editor's note: The following post is from Emma Warrillow, Chief DiGGer at Data Insight Group Inc. (DiG). Emma is a featured speaker at SAS Global Forum 2017 and recently named as one of the Top Women in Direct Marketing by Direct Marketing News. Learn more about Emma.   “I need [...]

The post That analyst is certifiable! appeared first on SAS Learning Post.

2月 282017
 

Did you know that… Scientists have concluded that the chicken came first, not the egg, because the protein which makes the egg shells is only produced by hens. Source A toaster uses almost half as much energy as a full-sized oven. Source The London Eye in England is the largest [...]

The post Are you learning what future employers are really looking for? appeared first on SAS Analytics U Blog.

2月 142017
 

Editor's note: This following post is from Shara Evans, CEO of Market Clarity Pty Ltd. Shara is a featured speaker at SAS Global Forum 2017 and a globally acknowledged Keynote Speaker and widely regarded as one of the world’s Top Female Futurists.

Learn more about Shara.


In the movie Minority Report lead character John Anderton, played by Tom Cruise, has an eye transplant in order to avoid being recognized by ubiquitous iris scanning identification systems.

Such surgical procedures still face some fairly significant challenges, in particular connecting the optic nerve of the transplanted eye to that of the recipient. However the concept of pervasive individual identification systems is now very close to reality and although the surgical solution is already available, it’s seriously drastic!

We’re talking face recognition here.

Many facial recognition systems are built on the concept of “cooperative systems,” where you look directly at the camera from a pre-determined distance and you are well lit, and your photo is compared against a verified image stored in a database. This type of system is used extensively for border control and physical security systems.

Facial recognition

Face in the Crowd Recognition (Crowd walking towards camera in corridor) Source: Imagus

Where it gets really interesting is with “non-cooperative systems,” which aim to recognize faces in a crowd: in non-optimal lighting situations and from a variety of angles. These systems aim to recognize people who could be wearing spectacles, scarves or hats, and who might be on the move. An Australian company, Imagus Technology has designed a system that is capable of doing just that — recognizing faces in a crowd.

To do this, the facial recognition system compiles a statistical model of a face by looking at low-frequency textures such as bone structure. While some systems may use very high-frequency features such as moles on the skin, eyelashes, wrinkles, or crow’s feet at the edges of the eyes — this requires a very high-quality image. Whereas, with people walking past, there’s motion blur, non-optimal camera angles, etcetera, so in this case using low-frequency information gets very good matches.

Biometrics are also gaining rapid acceptance for both convenience and fraud prevention in payment systems. The two most popular biometric markers are fingerprints and facial recognition, and are generally deployed as part of a two-factor authentication system. For example, MasterCard’s “Selfie Pay” app was launched in Europe in late 2016, and is now being rolled out to other global locations. This application was designed to speed-up and secure online purchases.

Facial recognition is particularly interesting, because while not every mobile phone in the world will be equipped with a fingerprint reader, virtually every device has a camera on it. We’re all suffering from password overload, and biometrics - if properly secured, and rolled out as part of a multi-factor authentication process - can provide a solution to coming up with, and remembering, complex passwords for the many apps and websites that we frequent.

Its not just about recognizing individuals

Facial recognition systems are also being used for marketing and demographics. In a store, for example, you might want to count the number of people looking at your billboard or your display. You'd like to see a breakdown of how many males and females there are, age demographics, time spent in front of the ad, and other relevant parameters.

Can you imagine a digital advertising sign equipped with facial recognition? In Australia, Digital Out-of-Home (DOOH) devices are already being used to choose the right time to display a client’s advertising. To minimize wastage in ad spend, ads are displayed only to a relevant audience demographic; for instance, playing an ad for a family pie only when it sees a mum approaching.

What if you could go beyond recognizing demographics to analyzing people’s emotions? Advances in artificial intelligence are turning this science fiction concept into reality. Robots such as “Pepper” are equipped with specialized emotion recognition software that allows it to adapt to human emotions. Again, in an advertising context, this could prove to be marketing gold.

Privacy Considerations

Of course new technologies is always a double-edged sword, and biometrics and advanced emotion detection certainly fall into this category.

For example, customers typically register for a biometric payment system in order to realize a benefit such as faster or more secure e-commerce checkouts or being fast-tracked through security checks at airports. However, the enterprise collecting and using this data must in turn satisfy the customer that their biometric reference data will be kept and managed securely, and used only for the stated purpose.

The advent of advanced facial recognition technologies provides new mechanisms for retailers and enterprises to identify customers, for example from CCTV cameras as they enter shops or as they view public advertising displays. It is when these activities are performed without the individual’s knowledge or consent that concerns arise.

Perhaps most worrisome is that emotion recognition technology would be impossible to control. For example, anyone would be able to take footage of world leaders fronting the press in apparent agreement after the outcome of major negotiations and perhaps reveal their real emotions!

From a truth perspective, maybe this would be a good thing.

But, imagine that you’re involved in intense business negotiations. In the not too distant future advanced augmented reality glasses or contacts could be used to record and analyze the emotions of everyone in the room in real time. Or, maybe you’re having a heart-to-heart talk with a family member or friend. Is there such a thing as too much information?

Most of the technology for widespread exploitation of face recognition is already in place: pervasive security cameras connected over broadband networks to vast resources of cloud computing power. The only piece missing is the software. Once that becomes reliable and readily available, hiding in plain sight will no longer be an option.

Find out more at the SAS Global User Forum

This is a preview of some of the concepts that Shara will explore in her speech on “Emerging Technologies: New Data Sets to Interpret and Monetize” at the SAS Global User Forum:

  • Emerging technologies such as advanced wearables, augmented and virtual reality, and biometrics — all of which will generate massive amounts of data.
  • Smart Cities — Bringing infrastructure to life with sensors, IoT connections and robots
  • Self Driving Cars + Cars of the Future — Exploring the latest in automotive technologies, robot vision, vehicle sensors, V2V comms + more
  • The Drone Revolution — looking at both the incredible benefits and challenges we face as drones take to the skies with high definition cameras and sensors.
  • The Next Wave of Big Data — How AI will transform information silos, perform advanced voice recognition, facial recognition and emotion detection
  • A Look Into the Future — How the convergence of biotech, ICT, nanotechnologies and augmentation of our bodies may change what it means to be human.

Join Shara for a ride into the future where humans are increasingly integrated with the ‘net!

About Shara Evans

Technology Futurist Shara Evans is a globally acknowledged Keynote Speaker and widely regarded as one of the world’s Top Female Futurists. Highly sought after and in demand by conference producers and media, Shara provides the latest insights and thought provoking ideas on a broad spectrum of issues. Shara can be reached via her website: www.sharaevans.com

(Note: My new website will be launching in a few weeks. In the meantime, the URL automatically redirects to my company website – www.marketclarity.com.au )

tags: analytics, SAS Global Forum

Facial recognition: Monetizing faces in the crowd was published on SAS Users.

1月 212017
 

zhangEditor's note: This following post is from Xiaoyuan Zhang, presenter at an upcoming Insurance and Finance User Group (IFSUG) webinar.

Learn more about Xiaoyuan Zhang.


As a business user with limited statistical skills, I don’t think I could build a credit scorecard without the help of SAS Enterprise Miner. As you can see from the flow chart, SAS Enterprise Miner, a descriptive and predictive modeling software, does an amazing job in model developing and streamlining.

credit_score_modeling-in-sas-enterprise-minerThe flow chart presents my whole credit score modeling process, which is divided into three parts: creating the preliminary scorecard, performing reject inference, and building the final scorecard. I will cover the whole process in the Insurance and Finance Users Group (IFSUG) virtual session on Feb 3, 2017. In this blog I wanted to emphasize the second part, which is sometimes easy to ignore.

The data for preliminary scorecard is from only accepted loan applications. However, the scorecard modeler needs to apply the scorecard to all applicants, both accepted and rejected. To solve the sample bias problem reject inference is performed.

Before inferring the behavior (good or bad) of the rejected applicants, data examination is needed. I used StatExplore node to explore the data and found out that there were a significant number of missing values, which is problematic. Because in SAS Enterprise Miner regression model, the model that is used here for scorecard creation and reject inference, ignores observations that contain missing values, which reduces the size of the training data set. Less training data can substantially weaken the predictive power of the model.

To help with this problem, Impute Node is used to impute the missing values. In the Properties Panel of the node, there are a variety of choices from which the modeler could choose for the imputation. In this model, Tree surrogate is selected for class variables and Median is selected for interval variables.

However, in Impute Node data role is set as Train. In order to use the data in Reject Inference Node, data role needs to be changed into Score. A SAS Code node is put in between for this purpose, which writes as:

data &em_export_score;
      set &em_import_data;   
   run;

Last but not least, Reject Inference Node is used to infer the performance of the rejected loan applicant data. SAS Enterprise Miner offers three standard, industry-accepted methods for inferring the performance of the rejected applicant data by the use of a model that is built on the accepted applicants. We won’t explore the three methods in detail here, as the emphasis of the blog is on the process.

To hear more on this topic, please register for the IFSUG virtual session, Credit Score Modeling in SAS Enterprise Miner on February 3rd from 11am-12pm ET.


About Xiaoyuan Zhang

Xiaoyuan Zhang grew up in Zhaoyuan China on the coast of the Bohai sea. Her town is famous for its ancient gold mine, hot springs and its unusual and tasty seafood. Her undergraduate degree is from China Agricultural University in Bejing, where she majored in Marketing Intelligence and graduated with honors. She graduated, with honors, from Drexel University with a Master Degree in Finance. She has passed two CFA exams and learned Enterprise Miner in one of her courses. She specializes in efficient credit score modeling with unutilized SAS Enterprise Minor. She is using some of her post-graduation free time to study "regular SAS", to tutor and to volunteer.

 

tags: IFSUG, SAS Enterprise Miner

Credit score modeling in SAS Enterprise Miner: Reject inference to solve sample bias problem was published on SAS Users.