The SAS Global Forum Student Symposium is an opportunity of teams of two to four post-secondary students and a faculty advisor (professor) to showcase their skills and compete with other teams in the application of SAS Analytics in big data.    Below is a list of the top eight teams who made the finals based on their submitted paper.  These eight teams virtually presented their paper to a panel of judges, and the top three teams were recognized as winners.  I want to personally thank all the students as they didn’t hesitate to submit their virtual presentations while still completing their coursework during this pandemic.  They are truly amazing, and their papers and presentations provide a variety of interesting subjects and SAS topics.   1 st Place Winner Unsupervised Contextual Clustering of Abstracts:  Jacob Noble & Himanshu Gamit from the University of St Thomas, St Paul delves into their study that utilizes publicly available data from the National Science Foundation (NSF) Web Application Programming Interface (API). In this paper, various machine learning techniques are demonstrated to explore, analyze and recommend similar proposal abstracts to aid the NSF or Awardee with the Merit Review Process.  To perform text analysis, SAS® University Edition is used which supports SASPy, SAS® Studio and Python JupyterLab. Gensim Doc2vec is used to generate document vectors for proposal abstracts. Afterwards, document vectors were used to cluster similar abstracts using SAS® Studio KMeans Clustering Module. For visualization, the abstract embeddings were reduced to two dimensions using Principal Component Analysis (PCA) within SAS® Studio. This was then compared to a t-Distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction technique as part of the Scikit-learn machine learning toolkit for Python. SAS Users Channel  (video)   2 nd Place Winner Reducing Ecological Footprint: Examining Panel Data:  Neil Belford, Jordan Humes, Manasi Murde, Bruce Rehburg of Oklahoma State University.  In their paper, worldwide panel data is explored to develop a model that can be used to find characteristics that are associated with a nation’s ecological impact, as measured by Ecological Footprint Accounting. Through panel regression in SAS®, several factors are found that are associated with a reduction in Ecological Footprint among OECD countries after controlling for GDP and population size. These findings point to general policy objectives that can be used by industrialized and industrializing countries to reduce their ecological impact. SAS Users Channel  (video)   3 rd Place Winner The Plight of the Honeybee:  Rachael Bishop, DaMarkus Green, and Karin Kolb of Kennesaw State University explores the effects of these pesticides on honeybees by comparing annual honey production yields and honeybee colony counts in the United States from 1995 through 2015.  A decline in the honeybee population is raising concerns worldwide. Honeybees are an important part of agricultural industries. Is it possible that the neonicotinoid pesticides used to protect crops from damaging insects are also harming the insects necessary for pollinating the same crops? SAS Users Channel  (video)   Finalists   Analyzing the Factors Impacting Suicidal Behavior in American Youth:  Ashlesha Sampathi, Mounica Mandapati, Nitesh Maruthi, and Venkat Ram Reddy from Oklahoma State University closely examines the 2017 Youth Risk Behavior Survey (YRBS), in order to understand characteristics of teenagers who attempted suicide. Latent Class Analysis using SAS® Enterprise Guide® found that among teens who attempted suicide, three distinct classes were evident which were characterized mainly by sexual assault, bullying, and depression respectively. Moreover, using PROC SURVEYLOGISTIC, it was seen that the odds of attempting suicide were four times higher for teens who were sexually assaulted and three times higher for teens who were bullied or abused drugs.  The outcome of the paper highlights the importance of early intervention in preventing teenagers from slipping down a “rabbit hole” of risky behaviors that ultimately lead them to take their own lives. SAS Users Channel  (video)   Determining College Student Success:  Carl Palombaro, Kayla Barrier, and Katherine Floyd, from UNC Wilmington discusses how student loan debt is an extremely hot topic in the media and is continuously on the rise, totaling over 1.5 trillion dollars in the United States alone. The overall goal of this project was to determine a student’s best financial option based on the degree they intend to seek, the university they attend, and the average salary per degree after graduation. In order to narrow the scope, this idea was applied to schools in North Carolina within the UNC public school system. The choice to exclude private universities in North Carolina was because many tuition rates at private institutions exceed the amount of funding typically available to a student through federal loans. SAS Users Channel  (video)   Economic Impacts of Sea Level Rise on Coastal Real Estate:  Austin Willoughby, Emma Delehanty, and Victoria Mullin University of North Carolina at Wilmington.  Their study models the potential loss of value coastal properties in New Hanover County, North Carolina will experience due to sea level rise. The methods used were linear regression to predict the future value of properties in addition to methods used in ArcGIS in order to find the parcels affected by different increments of sea level rise. This analysis could be used to inform coastal residents on the potential loss in value of their property and what to expect in future years. SAS Users Channel  (video)   Hate Speech Classification of social media posts using Text Analysis and Machine Learning:  Venkateshwarlu Konduri, Sarada Padathula, Asish Pamu and Sravani Sigadam of Oklahoma State University focuses on how SAS® Enterprise Miner’s Text Analytics was used to develop a model that categorizes tweets based on their content, specifically hateful vs normal. After sampling and cleaning of the data and breaking the tweets down into quantifiable components, different models were built and compared. The best performing model was used to score unseen data, achieving reasonable accuracy in classification. Their paper touches upon how text analytics could be harnessed by organizations like Twitter for encouraging civic responsibility in its users. By providing a feature at the user-level which allows tweets to be labelled as a particular category as they are typed, the users might be given an opportunity to review and possibly modify any hateful tweets before posting them. SAS Users Channel  (video)   What Happens After Police Shootings?  Aravind Dhanabal, Mason Kopasz, and Alex Lindsay of Oklahoma State University explores the possibility of underlying biases and prejudices that are leading to officers not being held accountable. To accomplish this, police shooting data and the results of the follow-on investigations were examined. The goal was to identify if there is a predictive model to evaluate if a police officer will face Grand Jury Indictment based on the attributes within the dataset. The police disposition (which specifies if an officer faced a Grand Jury Indictment or not) was used as the target variable. SAS Users Channel  (video) ... View more

I'll be sharing the sample agendas that the conference team built for different topic areas to help you get started regarding which SAS Global Forum 2020 proceedings might interest you.  Here are some Statistics papers, good for those that are getting started using SAS for Statistics.    Statistics:  Getting Started   Post-9/11 GI Bill: Teasing Out Insights about Veterans’:  Walter Ochinko, Research Director, Veterans Education Success, examines the challenges researchers face in using publicly available databases to track veterans use of GI Bill benefits and their success in earning postsecondary credentials, and presents the methodology and findings of several Veterans Education Success reports that, despite these challenges, teased out important insights on veteran outcomes.   Examining the Impact of Discussion Activities on Student Understanding in Introductory Statistics:  Rachael N. Becker of Southern Methodist University, explains how her study aims to examine the impact that voluntary participation in online discussion activities has on students’ understanding of statistical concepts in an undergraduate statistics course. A study of 90 undergraduate students enrolled in an introductory statistics course was conducted. The Levels of Conceptual Understanding in Statistics (LOCUS) assessment was utilized to measure students’ conceptual understanding in statistics. Read her e-Poster to learn how a statistical analysis of the difference between pre- and post-test data was completed in SAS® using propensity score matching techniques.   A Doctor's Dilemma: How Propensity Scores Can Help Control for Selection Bias in Medical Education:  Deanna Schreiber-Gregory, from the Henry M Jackson Foundation for the Advancement of Military Medicine, explores an example of how to use propensity score analyses. To demonstrate this technique, she seeks to explore whether clerkship order influences National Board of Medical Examiners (NBME) and United States Medical Licensing Examination (USMLE) exam scores for 3rd year military medical students. In order to conduct this analysis, a selection bias was identified, and adjustment was sought through three common forms of propensity scoring: stratification, matching, and regression adjustment.   Dealing with Missing Data in Epidemiological and Clinical Research:  Andrew T. Kuligowski, Independent Consultant, and Lida Gharibvand, from Loma Linda University, presents an introduction to categories of missing data and demonstrates some techniques that researchers can use to deal with missing data.  Missing values can have a surprising impact on the way data is analyzed and processed. Epidemiological and clinical research typically involve complex data and large databases that frequently contain missing data. The impact of missing data on data analysis and research findings can be significant, so it is important to develop a sound methodology to deal with it. Fortunately, there are powerful tools to represent and reference the missing data in SAS® analytics. There are several SAS functions and procedures that enable differentiated approaches for handling missing data. However, dealing with missing data can still be a bit of a minefield.   Introducing the GAMSELECT Procedure for Generalized Additive Model Selection:  Michael Lamm and Weijie Cai from SAS Institute Inc. explains how model selection is an important area in statistical learning. Both SAS/STAT® software and SAS® Visual Statistics software provide a rich set of tools for performing model selection over linear models, generalized linear models, and Cox proportional hazards models. With these tools, you can build parsimonious predictive models by constructing linear or fixed nonlinear effects to describe the dependency structure. But what if the dependency structure is nonlinear and the nonlinearity is unknown? And what if covariates are nonlinearly correlated? The new GAMSELECT procedure, available in SAS Visual Statistics, addresses these questions by using spline terms to approximate the nonlinear dependency, then selecting important variables in appropriate nonlinear transformations by using the boosting method or the shrinkage method. The procedure builds models for response variables in the exponential family, so that you can use it for continuous, count, or binary responses. This paper introduces the GAMSELECT procedure and provides a brief comparison to related SAS® procedures. ... View more

I'll be sharing the sample agendas that the conference team built for different topic areas to help you get started on which SAS Global Forum 2020 proceedings might interest you.  Here are some SAS Programming papers, good for those that are getting started with SAS programming.      Programming: Getting Started   The Essentials of SAS Dates and Times: Derek Morgan of PAREXEL International gives you a solid base for working with dates and times in SAS. It introduces you to functions and features that enable you to manipulate your dates and times with surprising flexibility.   Dynamic Programming: Using Files to Change the Behavior of Your Program:  Bryan K. Merrell of Merrick Bank provides a pro-active solution to a dilemma dealing with documentation of over a thousand pages of data transforms and usage and could change at any time, which is controlled by a third party.  He shows you how to create a file that lists out all field transforms to be able to change those values as they change from the provider. Best Practices in SAS® Programming to Support Ever-Increasing Data Loads.:  John Schmitz from Luminare Data, LLC., presents some well-known and lesser-known SAS methods for efficient data handling. The focal point of the paper is more geared towards what is available and the use cases for each technique, rather than a detailed how-to on a specific solution.   Do More with Less: Programming Efficiently with Keyboard Macros: Breanne Young from Life Time Inc. shows you how to limit your time searching through previous programs for recycled code by repurposing through the utilization of keyboard abbreviations. Keyboard abbreviations are a means of creating keystroke shortcuts or abbreviations that tie directly to a curated library of code. This library can then be distributed using keyboard macro files, enabling an increase in coding efficiency among teams.   How a SAS® Function and the ODS PACKAGE Statement Will Help You Save Money and Space: Ricardo Rosales from Westat Inc. demonstrates how to unzip files into the work library and how to create new zip files using the ODS Package Destination. This will keep your network space in a good shape.   Using SAS® for Generating and Sending Certificates by Email:  Alan Ricardo da Silva of Universidade de Brasília, shows a %certificate macro to create a basic structure of a certificate and then send it by email in a pdf format. The only requirement before you can use the macro is that a list (or a file) with the names and emails of the participants is created.   The Curious CAT (Q, S, T, X) Functions:  Jinson J. Erinjeri, an independent SAS programmer and Saritha Bathi of Bristol-Myers Squibb presents various CAT functions with relevant examples of their application. The main objective of this paper is to remove the curiosity of the CAT family and encourage the programmers to add these functions as a must in their toolbox.   Step-by-Step SQL Procedure: Charu Shankar of SAS Institute Inc. show how PROC SQL is a powerful query language that can sort, summarize, subset, join, and print results all in one step.  She provides elements to master PROC SQL, such as understanding the syntax order in which to submit queries to PROC SQL, joining tables using join conditions like inner join and reflexive join, and much more.   Think Globally, Act Locally: Understanding the Global and Local Macro Symbol Tables:  In this paper, authored by Michelle Buchecker, an independent consultant,  you learn when a local symbol table gets created, when a macro variable gets placed in the local table, what happens if you have a macro variable by the same name, and why does any of this matter. %LET, CALL SYMPUT, CALL SYMPUTX, and the INTO clause in SQL each have their own rules for addressing how they interact with the local and global tables. Techniques for debugging and displaying symbol tables are also discussed.   Work Smarter, Not Harder: Learning to Live without Your X:  Amit Patel and Lewis Mitchell discusses how to replace the following: getting file listings; making and deleting directories and files; changing file permissions; Microsoft Windows compression, by harnessing the power of built-in SAS functions as well as other functionality.  They are able to replicate much of what users have lost out on when faced with X-commands being disabled, enabling users to finally move on from their X.     ... View more

When the conference team was planning for the in-person event, we created many sample agendas for the different topic areas to help attendees with a starting point of which presentations to attend. We had lots of great feedback about how they help attendees to navigate the incredible amount of content that is available at the conference.  As you know, the on-going situation with COVID-19 means that SAS Global Forum 2020 became a virtual event and conference attendees didn’t get to view the sample agendas; however, the conference proceedings are still available!     I'll be sharing the sample agendas that the conference team built for different topic areas to help you get started on which proceedings might interest you.  The first one is Business Analytics, and there are links for a few papers, including a Forecasting and Econometrics paper, and a few Machine Learning/Predictive Modeling papers.  Happy reading!   Business Analytics   Classification of regional characteristics using population composition data and POS data:  In this e-Poster, Kazuma Bannai , explains how using population data from Tsukuba, Ibaraki Prefecture, a Huff-model will be created to identify the characteristics of each store in the city. In this city, the area where young people live and the area where elderly people live are relatively separated. Using SAS, the company will analyze whether there is a difference in sales between stores categorized into different categories based on store characteristics and POS data.   Using Analytics to Predict Tax Recovery and Prioritize Audits and Investigations in Canada: Jason Oliver, from Canada Revenue Agency (CRA), leverages the capabilities of SAS® Enterprise Guide® and SAS® Enterprise Miner™ in unearthing predictive patterns of interest with the clear objective of strengthening a feedback loop between tax risk assessment and the corresponding accrual of tax via audit. He examines powerful data learning techniques, as they apply to tax-based analytics, such as neural networks, decision trees, and regression analysis.   Deploying Computer Vision by Combining Deep Learning Action Sets with Open-Source Technology:  Jonny McElhinney and Duncan Bain, ScottishPower Energy Retail Ltd; Haidar Altaie, SAS Institute Inc., UK, focuses on doing Automatic Meter Reading (AMR) using customer-submitted photos of their meters. The challenges in this context are two-fold. First, we need to localise the box containing the digits, and then we must classify each digit with the correct label, 0-9. Many approaches are available, but both of these challenges can be addressed by combining the Deep Learning action set in SAS® Visual Data Mining and Machine Learning (VDMML) with DLPy and Keras. Read about how when applying these models in a real-world business context, an additional challenge arises, which is how to deploy and keep track of these models in a consistent way. This paper shows how SAS® and open source tools can be used together to provide a consistent approach both to creating as well as managing and deploying models.   Empower and Inspire-Designing Reports for Mobile Experiences.docx:  Khaliah H. Cothran, Ph.D., from SAS Institute Inc., presents tips for how to reduce information density with space saving-features, as well as do’s and don’ts for selecting objects, fonts, and colors that enable complex analytics to be processed quickly. Come learn how to create effective reports that scale from your desktop to your mobile device screen.   Smarter and Faster with SAS® Visual Analytics:  Rick Styll, from SAS Institute Inc., writes that SAS® Visual Analytics is the smartest business intelligence tool available. Automated Explanation, the new name for automated analysis, has been rewritten and redesigned to give you smarter and clearer insights, more interactivity, and easier-to-read explanations. In seconds, you can get the analytical story for the business intelligence hidden in your data that would take you hours to do manually. On top of that, you can automatically see suggested visualizations and identify related measures. For more advanced analytical visualizations, like decision trees, you get human-friendly natural language descriptions, drawing out insights that are easy to digest.   Forecasting and Econometrics   Forecasting Hourly Electricity Prices:  Joseph Perez from American Electric Power states that forecasting power prices can be more of an art than it is a science. Locational Marginal Prices (LMPs) exhibit high volatility—a phenomenon known as volatility clustering as with many financial time series data. His paper does not discuss the mean reverting properties of power prices, and assumes the reader has a basic understanding of the autoregressive nature of electricity prices. This method forecasts LMPs by preserving past volatility in the form of ratios and assigning them to corresponding load in the forecast. The purpose is to “shape” the forecasted series by utilizing historical profiles while holding to supply and demand fundamentals—high electricity usage tends to be associated with higher energy prices.   Machine Learning (Data mining/Predictive Modeling)   How to Explain Your Black-Box Models in SAS® Viya®:  Funda Güneş, Ricky Tharrington, Ralph Abbey, and Xin Hunt, all from SAS Institute Inc., introduces partial dependency (PD) plots, independent conditional expectation (ICE) plots, local interpretable model-agnostic explanations (LIME), and Shapley values, and demonstrates their use in two scenarios: a business-centered modeling task and a health-care modeling task. Also shown are the two different interfaces to these methods in SAS Viya: Model Studio and the SAS Viya programming interface.   Optimizing Supply Chain Robustness through Simulation and Machine Learning:  Bahar Biller and Jinxin Yi, SAS Institute Inc., introduces a supply chain simulator that has been built using SAS® Simulation Studio. The key features of the SAS® simulation technology, which enable the development of digital supply chains and the analysis of thousands of scenarios to perform risk-and-return tradeoff, are discussed. The paper concludes with a description of how computational efficiencies can be achieved through an integrated use of SAS Simulation Studio and SAS® Visual Data Mining and Machine Learning.   Human Bias in Machine Learning: How Well Do You Really Know Your Model?:  Jim Box, Elena Snavely, and Hiwot Tesfaye, all from SAS Institute, explores the history of bias in models, discusses how to use SAS® Viya® to check for bias, and examines different ways of eliminating bias from our models. Furthermore, they look at how advanced model interpretability available in SAS Viya can help end users to better understand model output. Ultimately, the promise of AI and machine learning is still a reality, but human oversight of the modeling process is vital.   Exploring Online Drug Reviews using Text Analytics, Sentiment Analysis, and Data Mining Models:  Thu Dinh, Goutam Chakraborty, and Miriam McGaugh, all from Oklahoma State University, aims to classify the side effect level and effectiveness level of prescribed drugs by using text analytics and predictive models within SAS® Enterprise Miner™. Additionally, the paper explores specific effectiveness and potential side effects of each prescription drug through sentiment analysis and text mining within SAS® Visual Text Analytics.   Using Machine Learning and Demand Sensing to Enhance Short-Term Forecasting for CPGs:  Kedar Prabhudesai, Varunraj Valsaraj, Dan Woo, Jinxin Yi and Roger Baldridge, all from SAS Institute Inc., explains that their paper uses machine learning along with traditional time-series forecasting models to generate enhanced weekly and daily forecasts by using historical-demand signal data and point-of-sale data. The model first creates enhanced weekly forecasts, and then breaks down enhanced weekly forecasts into daily forecasts. ... View more

Many of you have tuned in to watch the first release of virtual SAS Global Forum sessions on the SAS Users YouTube channel. We are doing it again! Mark your calendars for May 19 when even more sessions will be made available as part of the Users Connection for viewing on demand to fit your schedule.     There will be SAS presentations, users presentations, tutorials, sessions from SAS partners, the Student Symposium analytics competition, and a session for analytics leaders with insights on how industries are responding to the COVID-19 pandemic.   In addition, on June 16 there will be another release.  Be sure to check the website for more information. The June release will include live sessions and updates from SAS executives. There will even be a virtual edition of The Quad, where you can meet with SAS experts and learn more about SAS technologies in an interactive, virtual exhibit experience.   Again, thank you to everyone who has worked hard to get these sessions completed.  Until then, many blessings to you all and I hope you and your families are safe and healthy.   Lisa ... View more

I recently wrote a LinkedIn article about my thoughts regarding the cancellation of SAS Global Forum.  I'd thought I'd share it here in case you haven't read it.   I was looking to find some meaningful quote about the disappointment I feel about the cancellation of SASGF in Washington DC. I didn’t find anything that I felt fit the bill. It sucks. It flat out sucks. However, as a friend of mine used to say, “It is what it is”. I still feel terrible that so many people invested their time, energy, knowledge, money, and heart into SAS Global Forum, and it is now cancelled. Whether they wrote a paper and were going to present, worked over a year to organize the content and logistics, managed to get the trip paid for by their employer, or even sacrificing and investing in themselves and paying out of pocket to attend for the first time, no matter what, it's disappointing.   For many of us, it’s a huge blow; however, I’m not going down like that.    As the SAS Global Forum Conference Chair, I feel obligated to do whatever I can to ensure that the knowledge that would have been shared in D.C. will still be shared virtually. How? I’m not sure yet; those details are still being worked out. I will share all of the details as soon as I can. The conference team and the SGUG Executive Board have sent me messages about how committed they are to help us do whatever we can for the SAS User Community to benefit from this situation. They are truly an amazing group of people. Let’s not forget everyone at SAS who are working feverishly dealing with this turn of events. They are also committed to making the most of these circumstances.   I know there are a lot of questions and we are working to get all of them answered. Be sure to monitor the FAQs on the website. I’ll share whatever information I can, when I can. Please, bear with us as we work to create a great virtual event. I still have a few conference team members that I will be highlighting over the next week or so, as they deserve the spotlight, so be on the lookout for my last few articles.   Thank you for your support of SAS Global Forum 2020, and I hope to share more information soon. Sincerely, Lisa ... View more

SAS is actively monitoring the coronavirus outbreak and will continue to follow the guidance of the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC). At this time, SAS Global Forum is moving forward as planned. Attendees should monitor the situation reports from the World Health Organization and the latest travel advisories from International SOS. In addition to adhering to government and airline travel policies and recommendations, SAS is in direct conversation with attendees and exhibitors from affected regions to accommodate their cancellations and refunds.   Stay up-to-date by visiting https://www.sas.com/en_us/events/sas-global-forum/faq.html ... View more