SAS is hosting the premier analytics conference in the world September 18-20 in Washington DC, and supplementing the event with discounted training and certification exams at SAS Analytics Experience 2017. These offerings will be held before and after the event. As at other SAS events, we will be offering certification exams at
Employment - that's been a hot topic here in the US lately. Many of the manufacturing jobs we had in past decades are gone now, and it would be great if there was a crystal ball to predict which jobs might be at risk of disappearing in the future. The
This is the second post in my series of machine learning best practices. If you missed it, read the first post, Machine learning best practices: the basics. As we go along, all ten tips will be archived at this machine learning best practices page. Machine learning commonly requires the use of
Procurement fraud, often a form of internal fraud, is common fraud’s black sheep of the family...whispered about at gatherings or ignored in hopes that it will simply go away. No one wants to shine a questioning light onto trusted employees or imply wrongdoing within our purchasing departments, but the fact
If you’re familiar with the SAS story, you know that we have deep roots in academia. But our CEO Jim Goodnight has always known that roots aren’t enough to achieve incremental growth. You also have to plant seeds. Analytics alone can’t drive change, help businesses succeed and make the world
A lot of my friends seem to be getting married these days. Which got me thinking about wedding parties. Which then got me wondering what songs DJs do/don't play at weddings these days. And what was the outcome of my meandering thoughts ... a fun & interesting graph, of course! It
Skewness is a measure of the asymmetry of a univariate distribution. I have previously shown how to compute the skewness for data distributions in SAS. The previous article computes Pearson's definition of skewness, which is based on the standardized third central moment of the data. Moment-based statistics are sensitive to
Cuando AstraZeneca se propuso hacer más rentable el proceso de fabricación de su producto Turbuhaler y minimizar las variaciones, se enfrentó a un reto importante: la necesidad de construir una solución con capacidad suficiente para manejar todos los datos relacionados con la producción que se almacenaban en todos los sistemas
Nowadays, whether you write SAS programs or use point-and-click methods to get results, you have choices for how you access SAS. Currently, when you open Base SAS most people get the traditional SAS windowing environment (aka Display Manager) as their interface. But it doesn’t have to be that way. If
TwIn part one of this series, Clark Twiddy, Chief Administrative Officer of Twiddy & Company, shared some best practices from the first of three phases of Twiddy’s journey to becoming a data-driven SMB. This post focuses on phases two and three of their journey. (Read the whole series here.) Phase
Todd Wright says Hadoop can be difficult – but data management can help.
최근 양귀비 불법 재배와 연예인 마약 사건이 잇달아 발생하면서 국내에서는 멀게 느껴졌던 마약 범죄에 대한 관심이 높아지고 있습니다. 실제 관세청은 올해 상반기 마약 밀수가 전년 대비 2.6배 증가했으며, 밀수된 마약의 종류까지 다양해졌다고 발표했는데요. 국가는 이처럼 급증하는 마약 밀수를 차단하기 위해 조사 인력과 장비를 확충하고, 검색 기법을 적극 개발하겠다고 밝혔습니다. 이미
Datasets are rarely ready for analysis, and one of the most prevalent problems is missing data. This post is the first in a short series focusing on how to think about missingness, how JMP13 can help us determine the scope of missing data in a given table, and how to
St. Louis Union Station welcomed its first passenger train on Sept. 2, 1894 at 1:45 pm and became one of the largest and busiest passenger rail terminals in the world. Back in those days, the North American railroads widely used a system called Timetable and Train Order Operation to establish
In May, the New York Times published an article “The Little Known Statistician Who Taught Us to Measure Teachers” which profiled the life of Dr. Bill Sanders. The author reflected on Dr. Sanders’ life and work improving education for all students. For those of us in education, Dr. Sanders’ work
SAS power users (and actually, power users of any application) like to customize their environment for maximum productivity. Long-time SAS users remember the KEYS window in SAS display manager, which allows you to assign SAS commands to "hot keys" in your SAS session. These users will invest many hours to
My previous blog post focused on a graph, showing the % of women earning STEM degrees in various fields. While that graph was was designed to answer a very specific question, let's now look at the data from a broader perspective. Let's look at the total number of STEM degrees
An important problem in machine learning is the "classification problem." In this supervised learning problem, you build a statistical model that predicts a set of categorical outcomes (responses) based on a set of input features (explanatory variables). You do this by training the model on data for which the outcomes
Editor-in-Chief Len Tashman's Preview The forecasting field is surely cross-disciplinary, as exemplified by the diverse membership of the International Institute of Forecasters (the publisher of this journal), but it is also multidimensional, as can be clearly seen in this Summer 2017 issue. The articles you’ll read here encompass sales and
Phil Simon chimes in on the immediacy of enterprise data.
For the past several years, efforts have been under way to recruit more women into the STEM (science, technology, engineering, and math) fields. I recently saw an interesting graph showing the percentage of bachelor's degrees conferred to women in the US, and I wondered if I could tweak that graph
I started my training in machine learning at the University of Tennessee in the late 1980s. Of course, we didn’t call it machine learning then, and we didn’t call ourselves data scientists yet either. We used terms like statistics, analytics, data mining and data modeling. Regardless of what you call
Artificial intelligence promises to transform society on the scale of the industrial, technical, and digital revolutions before it. Machines that can sense, reason and act will accelerate solutions to large-scale problems in myriad of fields, including science, finance, medicine and education, augmenting human capability and helping us to go further,
I recently showed how to compute a bootstrap percentile confidence interval in SAS. The percentile interval is a simple "first-order" interval that is formed from quantiles of the bootstrap distribution. However, it has two limitations. First, it does not use the estimate for the original data; it is based only
Carbon Dioxide ... CO2. Humans breathe out 2.3 pounds of it per day. It's also produced when we burn organic materials & fossil fuels (such as coal, oil, and natural gas). Plants use it for photosynthesis, which in turn produces oxygen. It is also a greenhouse gas, which many claim
I don't have a big vacation planned this summer. Don't feel sorry for me... I am going to Germany for a week in October and on Friday I leave for my second weekend at the beach. I have recently been reading about what makes a vacation "restorative". There is some cool
Everyone is talking about artificial intelligence (AI). In fact, many SAS customers who've been using our analytics capabilities for years or even decades are asking: What can we do with AI? What exactly is AI from a software perspective? How can we infuse cognitive computing into our customer interactions and on the customer
Die (technologische) Veränderung kommt… und sie kommt schnell. Digitalisierung ist nicht mehr nur „in aller Munde“, sondern inzwischen in den Unternehmen angekommen. Das zeigte das SAS Forum Deutschland 2017, das am 29. Juni im World Conference Center in Bonn stattfand und das rund 1.200 Teilnehmer besuchten und etwa ebenso viele
Joyce Norris-Montanari explains why it's so important to pick the right tools to manage your big data.
I previously wrote about how to compute a bootstrap confidence interval in Base SAS. As a reminder, the bootstrap method consists of the following steps: Compute the statistic of interest for the original data Resample B times from the data to form B bootstrap samples. B is usually a large
