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Jim Harris 0
The Chicken Man versus the Data Scientist

In my previous post Sisyphus didn’t need a fitness tracker, I recommended that you only collect, measure and analyze big data if it helps you make a better decision or change your actions. Unfortunately, it’s difficult to know ahead of time which data will meet that criteria. We often, therefore, collect, measure and analyze

Rick Wicklin 0
How to create a hexagonal bin plot in SAS

While I was working on my recent blog post about two-dimensional binning, a colleague asked whether I would be discussing "the new hexagonal binning method that was added to the SURVEYREG procedure in SAS/STAT 13.2." I was intrigued: I was not aware that hexagonal binning had been added to a

Jim Harris 0
Sisyphus didn’t need a fitness tracker

In his pithy style, Seth Godin’s recent blog post Analytics without action said more in 32 words than most posts say in 320 words or most white papers say in 3200 words. (For those counting along, my opening sentence alone used 32 words). Godin’s blog post, in its entirety, stated: “Don’t measure

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Let’s chat about big data and innovation

 “The best data scientists are those that combine deep statistical / data / machine learning skills with domain knowledge.” “[Most companies] haven't properly addressed the need for cultural change!... There's still this prevailing perception that it's a technology & skills problem.” “Analytics only ever tells you one of two things—it

Leo Sadovy 0
Why analytic forecasting?

Because you are already halfway there and you should want the entire process to be data-driven, not just the historical reporting and analysis.  You are making decisions and using data to support those decisions, but you are leaving value on the table if the analytics don't carry through to forecasting.  In the

Learn SAS
Rick Wicklin 0
Choosing bins for histograms in SAS

When you create a histogram with statistical software, the software uses the data (including the sample size) to automatically choose the width and location of the histogram bins. The resulting histogram is an attempt to balance statistical considerations, such as estimating the underlying density, and "human considerations," such as choosing

Dylan Jones 0
Lack of knowledge and the root-cause myth

A lot of data quality projects kick off in the quest for root-cause discovery. Sometimes they’ll get lucky and find a coding error or some data entry ‘finger flubs’ that are the culprit. Of course, data quality tools can help a great deal in speeding up this process by automating

Arun C. Murthy 0
SAS high-performance capabilities with Hadoop YARN

For Hadoop to be successful as part of the modern data architecture, it needs to integrate with existing tools. This integration allows you to reuse existing resources (licenses and personnel) and is typically 60% of the evaluation criteria for integration of Hadoop into the data center. One of the most

Paul Kent 0
Share your cluster – How Apache Hadoop YARN helps SAS

Even though it sounds like something you hear on a Montessori school playground, this theme “Share your cluster” echoes across many modern Apache Hadoop deployments. Data architects are plotting to assemble all their big data in one system – something that is now achievable thanks to the economics of modern

Jim Harris 0
Data science versus narrative psychology

My previous post explained how confirmation bias can prevent you from behaving like the natural data scientist you like to imagine you are by driving your decision making toward data that confirms your existing beliefs. This post tells the story of another cognitive bias that works against data science. Consider the following scenario: Company-wide

Rick Wicklin 0
Creating heat maps in SAS/IML

In a previous blog post, I showed how to use the graph template language (GTL) in SAS to create heat maps with a continuous color ramp. SAS/IML 13.1 includes the HEATMAPCONT subroutine, which makes it easy to create heat maps with continuous color ramps from SAS/IML matrices. Typical usage includes

Data Visualization
Falko Schulz 0
Path analysis with SAS Visual Analytics

Introduction Understanding the behavior of your customers is key to improving and maintaining revenue streams. It is a an important part when crafting successful marketing campaigns. With SAS Visual Analytics 7.1 you can analyze, explore and visualize user behavior, click paths and other event-based scenarios. Monitoring the customer journey by visualizing

Rick Wicklin 0
Creating a basic heat map in SAS

Heat maps have many uses. In a previous article, I showed how to use heat maps with a discrete color ramp to visualize matrices that have a small number of unique values, such as certain covariance matrices and sparse matrices. You can also use heat maps with a continuous color

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