SAS Press gets in the Valentine's Day spirit.
SAS for Mixed Models: Introduction to Basic Applications takes you step-by-step through the journey of creating models, enabling you to transform scores of data into actionable insights.
The post The Model Gift – Transforming Data into Actionable Insights! appeared first on SAS Learning Post.
Old and new SAS users alike learned the tricks of the data trade from our Little SAS Book! We hope these fun tips from our exercise and project book teach you even more about how to master the data analytics game!
From Rebecca Ottesen:
Tip #1: Grouping Quantitative Variables
My favorite tip to share with students and SAS users is how to use PROC FORMAT to group quantitative variables into categories. A format can be created with a VALUE statement that specifies the ranges relevant to the category groupings. Then, this format can be applied with a FORMAT statement during an analysis to group the variable accordingly (don't forget the CLASS statement when applicable). You can also create categorical variables in the DATA step by applying the format in an assignment statement with a PUT function.
From Lora Delwiche:
Tip #2: Commenting Blocks of Code
This tip I learned from fellow SAS Press author Alan Wilson at SAS Global Forum 2008 in San Antonio. It might be a bit overly dramatic to say that this tip changed my life, but that’s not far from the truth! So, I am paying this tip forward. Thank you, Alan!
To comment out a whole block of code, simply highlight the lines of code, hold down the control key, and press the forward slash ( /). SAS will take those lines of code and turn them into comments by adding a /* to the beginning of each line and an */ at the end of each line.
To convert the commented lines back to code, highlight the lines again, hold down the control and shift keys, and press the forward slash ( /). This works in both the SAS Windowing environment (Display Manager) and SAS Enterprise Guide.
If you are using SAS Studio as your programming interface, you comment the same way, but to uncomment, just hold down the control key and then press the forward slash.
From Susan J. Slaughter:
Tip #3: Susan's Macro Mottos
There is no question that writing and debugging SAS macros can be a challenge. So I have two "macro mottos" that I use to help keep me on track.
“Remember, you are writing a program that writes a program.”
This is the most important concept to keep in mind whenever you work with SAS macros. If you feel the least bit confused by a macro, repeating this motto can help you to see what is going on. I speak from personal experience here. This is my macro mantra.
“To avoid mangling your macros, always write them one piece at a time.”
This means, write your program in standard SAS code first. When that is working and bug-free, then add your %MACRO and %MEND statements. When they are working, then add your parameters, if any, one at a time. If you make sure that each macro feature you add is working before you add another one, then debugging will be vastly simplified.
And, this is the best time ever to learn SAS! When I first encountered SAS, there were only two ways that I could get help. I could either ask another graduate student who might or might not know the answer, or I could go to the computer center and borrow the SAS manual. (There was only one.) Today it's totally different.
I am continually AMAZED by the resources that are available now—many for FREE. Here are four resources that every new SAS user should know about:
1. SAS Studio
This is a wonderful new interface for SAS that runs in a browser and has both programming and point-and-click features. SAS Studio is free for students, professors, and independent learners. You can download the SAS University Edition to run SAS Studio on your own computer, or use SAS OnDemand for Academics via the Internet.
2. Online classes
Two of the most popular self-paced e-learning classes are available for free: SAS Programming 1: Essentials, and Statistics 1. These are real classes which in the past people paid hundreds of dollars to take.
4. Community of SAS users
If you encounter a problem, it is likely that someone else faced a similar situation and figured out how to solve it. On communities.sas.com you can post questions and get answers from SAS users and developers. On the site, www.lexjansen.com, you can find virtually every paper ever presented at a SAS users group conference.
If you want even more tips and tricks, check out our Exercises and Projects for The Little SAS Book, Fifth Edition! Let us know if enjoyed these tips in the comment boxes below.
Deep learning (DL) is a subset of neural networks, which have been around since the 1960’s. Computing resources and the need for a lot of data during training were the crippling factor for neural networks. But with the growing availability of computing resources such as multi-core machines, graphics processing units (GPUs) accelerators and hardware specialized, DL is becoming much more practical for business problems.
Financial institutions use a large number of computations to evaluate portfolios, price securities, and financial derivatives. For example, every cell in a spreadsheet potentially implements a different formula. Time is also usually of the essence so having the fastest possible technology to perform financial calculations with acceptable accuracy is paramount.
In this blog, we talk to Henry Bequet, Director of High-Performance Computing and Machine Learning in the Finance Risk division of SAS, about how he uses DL as a technology to maximize performance.
Henry discusses how the performance of numerical applications can be greatly improved by using DL. Once a DL network is trained to compute analytics, using that DL network becomes drastically faster than more classic methodologies like Monte Carlo simulations.
We asked him to explain deep learning for numerical analysis (DL4NA) and the most common questions he gets asked.
Can you describe the deep learning methodology proposed in DL4NA?
Yes, it starts with writing your analytics in a transparent and scalable way. All content that is released as a solution by the SAS financial risk division uses the "many task computing" (MTC) paradigm. Simply put, when writing your analytics using the many task computing paradigm, you organize code in SAS programs that define task inputs and outputs. A job flow is a set of tasks that will run in parallel, and the job flow will also handle synchronization.
The job flow in Figure 1.1 visually gives you a hint that the two tasks can be executed in parallel. The addition of the task into the job flow is what defines the potential parallelism, not the task itself. The task designer or implementer doesn’t need to know that the task is being executed at the same time as other tasks. It is not uncommon to have hundreds of tasks in a job flow.
Using that information, the SAS platform, and the Infrastructure for Risk Management (IRM) is able to automatically infer the parallelization in your analytics. This allows your analytics to run on tens or hundreds of cores. (Most SAS customers run out of cores before they run out of tasks to run in parallel.) By running SAS code in parallel, on a single machine or on a grid, you gain orders of magnitude of performance improvements.
This methodology also has the benefit of expressing your analytics in the form of Y= f(x), which is precisely what you feed a deep neural network (DNN) to learn. That organization of your analytics allows you to train a DNN to reproduce the results of your analytics originally written in SAS. Once you have the trained DNN, you can use it to score tremendously faster than the original SAS code. You can also use your DNN to push your analytics to the edge. I believe that this is a powerful methodology that offers a wide spectrum of applicability. It is also a good example of deep learning helping data scientists build better and faster models.
The number of neurons of the input layer is driven by the number of features. The number of neurons of the output layer is driven by the number of classes that we want to recognize, in this case, three. The number of neurons in the hidden layers as well as the number of hidden layers is up to us: those two parameters are model hyper-parameters.
How do I run my SAS program faster using deep learning?
In the financial risk division, I work with banks and insurance companies all over the world that are faced with increasing regulatory requirements like CCAR and IFRS17. Those problems are particularly challenging because they involve big data and big compute.
The good news is that new hardware architectures are emerging with the rise of hybrid computing. Computers are increasing built as a combination of traditional CPUs and innovative devices like GPUs, TPUs, FPGAs, ASICs. Those hybrid machines can run significantly faster than legacy computers.
The bad news is that hybrid computers are hard to program and each of them is specific: you write code for GPU, it won’t run on an FPGA, it won’t even run on different generations of the same device. Consequently, software developers and software vendors are reluctant to jump into the fray and data scientist and statisticians are left out of the performance gains. So there is a gap, a big gap in fact.
To fill that gap is the raison d’être of my new book, Deep Learning for Numerical Applications with SAS. Check it out and visit the SAS Risk Management Community to share your thoughts and concerns on this cross-industry topic.
SAS Press author Kirk Paul Lafler's favorite tips using PROC SQL.
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This blog post highlights more SAS Global Forum papers chosen by SAS Press authors.
Three bestselling SAS Press authors feature their favorite papers from SAS Global Forum 2018.
The post What your favorite authors are reading: SAS Press authors highlight SAS Global Forum papers appeared first on SAS Learning Post.
The Base SAS DATA step has been a powerful tool for many years for SAS programmers. But as data sets grow and programmers work with massively parallel processing (MPP) computing environments such as Teradata, Hadoop or the SAS High-Performance Analytics grid, the data step remains stubbornly single-threaded. Welcome DS2 – [...]
In this blog post you'll find out the top 10 bestselling titles at SAS Global Forum 2018.
The post Top 10 bestselling titles at SAS Global Forum 2018 appeared first on SAS Learning Post.