Upcoming Events:
Dartmouth Area SAS Users Group Meeting (Virtual via Zoom)
Date/Time: July 13, 2023 Thursday, 11:00 AM – 12:00 PM EST.
Title: Improving Deep Learning Model Performance Using SAS Autotuning
Abstract: Deep learning is an area of machine learning that has become ubiquitous with artificial intelligence (AI) applications, including recent advancements in generative AI. One of the most significant challenges practitioners face today is finding a model structure and corresponding set of hyperparameters that perform well on unique data with deep learning applications.
This presentation will show how SAS can alleviate the neural architecture search burden through intelligent automation using SAS autotune. We’ll surface autotune using the solveBlackBox action in SAS.
Bio of Speaker: Robert Blanchard is a Principal Data Scientist at SAS where he builds end-to-end artificial intelligence applications. He also researches, consults, and teaches machine learning with an emphasis on deep learning and computer vision for SAS. Robert has authored an introductory book on computer vision and has written several professional courses on topics including neural networks, deep learning, and optimization modeling. Before joining SAS, Robert worked under the Senior Vice Provost at North Carolina State University, where he built models pertaining to student success, faculty development, and resource management. Prior to working in academia, Robert was a member of the research and development group on the Workforce Optimization team at Travelers Insurance. His models at Travelers focused on forecasting and optimizing resources. Robert graduated with a master’s degree in Business Analytics and Project Management from the University of Connecticut and a master’s degree in Applied and Resource Economics from East Carolina University.
Registration link: https://libcal.dartmouth.edu/calendar/itc/2023DSAIW2
** Registrants will receive a zoom link one day before the webinar.
Boston Area SAS Users Group (BASUG) Events
Past Events:
Dartmouth Area SAS Users Group Meeting (Virtual via Zoom)
Date/Time: Nov. 17, Thursday, 10:30 AM – 11:30 AM EST.
Abstract: Modeling time to an event poses particular challenges that are different from either logistic regression or linear regression modeling. This presentation will give you the essentials to start using this technique right away, make sense of censored data, the classes of models that are most commonly used, and how to explain your findings in a meaningful way.
Bio of Speaker: Marc Huber is Senior Analytical Training Consultant at SAS Institute, Cary, NC. BA, MA Quantitative Psychology, MSW – all from the University of North Carolina at Chapel Hill Health researcher (biostatistics and epidemiology) at Duke University Medical Center and UNC-Chapel for 14 years. SAS instructor since 2004.
Registration link: https://libcal.dartmouth.edu/calendar/itc/dasug2022fall
** A Zoom link will be emailed to all registered participants prior to the webinar.
Dartmouth Area SAS Users Group Meeting (Virtual via Zoom)
Date/Time: July 28, Thursday, 11:00 AM – 13:00 PM EST.
1. Title: Introduction to Penalized Regression and Variable Selection Methods in SAS/STAT® and SAS® Viya® Procedures
Abstract:
Variable selection is a fundamental task in high-dimensional modeling and statistical learning. A sparse model that is composed of a subset of variables is usually preferable to a full model that uses all input variables because of its better interpretability and higher prediction accuracy. Traditional approaches such as stepwise regression use sequential procedures, which are computationally intensive and unstable. Alternative selection methods use sparsity inducing penalized regression techniques to simultaneously select variables and estimate regression coefficients. In this talk, I will review the variable selection methods in linear regression, and present practical examples using SAS/STAT and SAS Viya procedures.
Bio of the speaker:
Yingwei Wang is a Sr. Research Statistician Developer at Advanced Analytics Division of SAS. He specializes in variable selection methods in linear models and support the GLMSELECT and REGSELECT procedures in SAS. Yingwei got his PhD from the Department of Mathematics at Purdue University.
2. Title: Version Control With SAS® and Git
Abstract:
Few technologies have done more to advance code collaboration and automation than Git. GitHub’s popularity has drawn the attention of all types of programmers including SAS programmers. Many SAS products have direct integration with Git –extending to GitHub. In this session we will cover the following: • What is Git and why do I care? • Using Git with SAS Enterprise Guide. • Using Git with SAS Studio. • Git functions in Base SAS. • Where to learn more.
Bio of the speaker:
Chris Hemedinger leads SAS Communities & User Groups, where SAS users gather to share questions, answers, and best practices. Since 1993, Chris has worked for SAS as an author, a software developer, an R&D manager, and a consultant. Inexplicably, Chris is still coasting on the limited fame he earned as an author of SAS For Dummies. You can follow Chris at The SAS Dummy blog and on Twitter as @cjdinger
Registration link: https://libcal.dartmouth.edu/calendar/itc/2022DSAIW5
** Registrants will receive a zoom link prior to the webinar.
Summer Webinar
- Date: August 19, 2021
- Time: 11:00 a.m. – 1:00 p.m. EST
- Registration Link: https://libcal.dartmouth.edu/calendar/itc/2021DSAIW7
Causal Analysis Using SAS Statistics Procedures
This talk overviews some recently developed SAS procedures that provide statistical tools for causal analysis, including causal effect estimation, causal mediation analysis, and causal graph theory for establishing valid estimation strategies. After basic principles of causal analysis are summarized, important features of the causal procedures are demonstrated through examples.
Presented by Clay Thompson. Clay Thompson is a senior research statistician developer in Advanced Analytics R&D at SAS Institute, where he supports methods for causal analysis and sequential trials. He has taught workshops and CE courses on causal analysis at JSM and the International Chinese Statistical Association. Prior to joining SAS, he worked as a quantitative systems pharmacologist at Pfizer Inc. He received his PhD in applied mathematics from NCSU.
Analyzing observational data with the PSMATCH procedure
Learn about how you can use the PSMATCH procedure and propensity-score-based methods to support the estimation of causal effects from nonrandomized data. This talk reviews the definition of causal effects in a counter factual framework and provides examples of how you can use propensity score based matching and inverse probability weighting to adjust for confounding variables. No prior experience with the material is required.
Presented by Michael Lamm, a senior research statistician developer in the Advanced Analytics R&D at SAS Institute. Among his responsibilities are developing software in causal analysis and statistical learning. He has taught courses on causal analysis at conferences including JSM, and the ENAR Spring Meetings. He received his PhD in statistics and operations research from UNC Chapel Hill.
Winter Webinar
- Date: December 17, 2020
- Time: 10:00 a.m. – 12:00 p.m. EST
- Location/Format: Webinar link
1. Causal Effect Estimands: Interpretation, Identification, and Computation (slides)
In modern statistics and data science, there is growing attention on estimating causal effects by using data from nonrandomized or imperfectly randomized studies. This task arises in applications such as post-approval analysis of medical treatments, evaluation of public policies, and assessment of marketing campaign efficacy. One challenge of these applications is the variety of causal effects that you can estimate. For example, you might need to determine whether to estimate the average treatment effect (ATE), the average treatment effect for the treated (ATT), a mediated effect, or other conditional effects. Identifying the causal effect most relevant to your application can have important implications for determining what approach to causal inference is most appropriate. This presentation provides an overview of different types of causal estimands, a comparison of how the different estimands are interpreted, and guidance on how identifying an appropriate estimand can help you determine an appropriate causal analysis workflow. The CAUSALGRAPH, CAUSALMED, CAUSALTRT, and PSMATCH procedures in SAS/STAT® software are used to demonstrate the workflow. The presentation also includes a review of the assumptions that are required for identifying and estimating causal effects.
Presented by Clay Thompson. Clay Thompson is a Senior Research Statistician Developer in the Multivariate Models Research Department at the SAS Institute, where (among other things) he develops algorithms and software for the analysis of causal effects using graphical models. His career has focused on research problems at the intersection of computational science, mathematics, and health sciences. Prior to SAS, he worked as a quantitative systems pharmacologist in the pharmaceutical industry. He received a PhD in Applied Mathematics from North Carolina State University.
2. PROC SQL vs. DATA Step Programming (slides)
Everyone wants to know: Should I use the DATA step or PROC SQL to join this data? Take a behind the scenes look at how the DATA step and SQL procedure process data by comparing all types of joins (inner, left/right, outer) with multiple types of data (one-to-one, one-to-many, many-to-many).
Presented by Mary-Elizabeth Eddlestone, Analytics Technical Advisor, SAS Customer Success Organization
Demystifying analytics has been a career-long quest for Mary-Elizabeth (“M-E”) Eddlestone, an Analytics Technical Advisor on the SAS Customer Success team. Having studied Economics and Quantitative Methods at Mount Holyoke College and Cornell University, M-E has used SAS analytics to study, model, forecast, and predict a wide range of subjects in a variety of industries. M-E began programming in SAS as an undergraduate and has used SAS in every job since. She has spent the last several years at SAS helping customers discover the power of SAS analytics and has presented at, and served as section chair for SUGI/SAS Global Forum, Analytics, as well as several regional, local and in-house SAS user groups.
Certification: Predictive Modeler Using SAS® Enterprise Miner™
Handling Missing Data in SAS and Proven Practices for Predictive Modeling
- Date: August 6, 2020
- Time: 11:00 a.m. – 1:00 p.m. EST
- Location/Format: Webinar link
1. Handling Missing Data in SAS (slides/video)
What do you do when you have missing values in your data? In SAS we have many ways to manage missing values. In this session we cover what are missing values, why and when missing values occur and how to manage missing values. We discuss functions, procedures and how different products deal with missing values.
2. Proven Practices for Predictive Modeling(slides/video)
In our ongoing quest for “analytics excellence,” what are some of the strategies and tactics that we, as analytics practitioners, can consider not only for individual predictive modeling projects, but for increasing the value and importance of analytics in our organizations? This presentation will share some of the common strategies, attributes, processes and best practices of the most successful organizations. Best practices will include considerations for an overall analytics process as well as the discrete steps of building a predictive model, such as data preparation and sampling; input (variable) examination, selection and transformation; model selection and validation; and more.
Presenter:
Melodie Rush is a Principal Data Scientist for the SAS Global Customer Success Technical Team. Melodie received both her B.S. in Statistics and her Masters in Science of Management from North Carolina State University. Before joining SAS, Melodie worked for Research Triangle Institute as a Statistician. Her responsibilities included implementing national and local surveys of various topics, such as health care, employee benefits, and drug abuse. As part of her research, she has published work for both the American Statistical Association and the American Public Health Association. After joining SAS, Melodie has developed presentations and methodology for doing many types of analysis, including data mining, machine learning, forecasting, data exploration and visualization, quality control and marketing. She has spent the last 20+ years helping companies identify and solve problems in each of these analytical areas.
Date & Time: Thursday, December 5, 2019 8:15 AM-12:30 PM
- Presentation 1: “Free Online SAS® Resources”
- Presentation 2: “A Tour of the SAS University Edition (AKA “Free SAS”)”
- Presentation 3: “A Survey of Some of the Most Useful SAS Functions”
- Presentation 4: “Panel Discussion: Advice for Students and Early Career SAS Professionals”
BASUG Training Session
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- Date & Time: Thursday, December 5, 2019 – 1:30 PM-5 PM
- Data Cleaning 101 by Ron Cody
- Location: The Link, 255 Main Street, 8th Floor, Cambridge, MA 02142
- Contact: 617-945-2562
Using JupyterLab with SAS and Tips for SAS EG
- Date: Thursday, November 14, 2019
- Time: 11:00 a.m. – 12:00 p.m.
- Location/Format: Webinar
1. Learning Data Science with SAS® University Edition and JupyterLab (Brian Gaines)
One of the interfaces included with SAS® University Edition is the popular JupyterLab interface. You can use this open-source interface to generate dynamic notebooks that easily incorporate SAS® code and results into documents such as course materials and analytical reports. The ability to seamlessly interweave code, results, narrative text, and mathematical formulas all into one document provides students with practical experience in creating reports and effectively communicating results. In addition, the use of an executable document facilitates collaboration and promotes reproducible research and analyses. After a brief overview of SAS University Edition, this paper describes JupyterLab, discusses examples of using it to learn data science with SAS, and provides tips. SAS University Edition is available at no charge to educators and learners for academic, noncommercial use and includes SAS® Studio, Base SAS®, SAS/STAT®, SAS/IML®, and some other analytical capabilities.
2. 20 in 20: Quick Tips for SAS® Enterprise Guide® Users (Kelly Gray)
There are many time-saving and headache-saving tips and tricks you can use to make working in SAS® Enterprise Guide® a breeze. Did you know that you can change your layout so that you can see your code and your results at the same time? You will learn 20 tips and tricks for working in SAS Enterprise Guide in 20 minutes. One tip per minute, and out of the twenty you are guaranteed to find at least one nugget that will making your life easier.
SAS Blowout Event
Co-hosted by Boston Area SAS® Users Group and SAS Institute Inc.
- Wednesday, September 18, 2019
- 8:15 a.m. – 4:30 p.m.
- The NonProfit Center, developed by TSNE MissionWorks
- 89 South Street, Boston, MA 02111
BASUG invites you to join us for our 6th Annual SAS Blowout, featuring presentations by three senior SAS Institute developers. Come for all or part of the day, and be sure to stay for our make- your-own sundae social event, where you can schmooze with your colleagues and this incredible team of presenters from SAS.
Event agenda
DASUG Annual General Meeting
- Thursday, July 11, 2019
- Noon – 1 p.m., Lunch
- 1 – 5 p.m., Presentations
- Dartmouth College
The Class of 1978 Life Sciences Center 201
78 College Street, Hanover, NH
(Very close to Dewey Field Parking Lot)
Join fellow SAS users and SAS experts for the DASUG meeting on Thursday, July 11, at Dartmouth College. You can network with local SAS users, boost your skills, and learn about some of the latest SAS tools and technologies.
Presentations by SAS
- It’s All About the Base – Procedures
Jane Eslinger, Principal Technical Support Analyst - Power Up Your Reporting Using the SAS® Output Delivery® System
Chevell Parker, Senior Principal Technical Support Analyst - Celebrity Makeover: A Fresh and Modern Look for SAS® Enterprise Guide®
Chevell Parker, Senior Principal Technical Support Analyst
DASUG Webinar Information:
Lisa Horwitz from SAS will present:
Change is Good, or at Least Expected: Techniques for Visualizing Categorical Values Over Time
Date and time: Friday, December 7, 2018 2:00 pm (Eastern Standard Time)
Duration: 0.5 hour
Description:
Many techniques exist for showing how numeric values change over time. Bar charts, line charts, plots and many other graph types are all excellent ways to demonstrate how temperature, expenses and other measures increase and decrease over minutes, months or decades. On the other hand, such graphs don’t lend themselves to showing how and when categorical values such as grade, rating, score and status change over time. A simple combination of data manipulation, file merging, custom formatting and the Output Delivery System (ODS) can produce a wide range of useful, easy-to-interpret and effective reports. By using color, fonts, custom messages and other features to indicate a change in a data value, these reports make it easy to monitor progress or to detect when things are going in the wrong direction. SASGF18
And,
Ray Wright from SAS will present:
Date and time: Friday, December 7, 2018 2:30 pm (Eastern Standard Time)
Duration: 0.5 hour
Description:
One of the key questions a data scientist asks when interpreting a predictive model is “How do the model inputs work?” Variable importance rankings are helpful for identifying the strongest drivers, but these rankings provide no insight into the functional relationship between the drivers and the model’s predictions.
Partial dependence (PD) and individual conditional expectation (ICE) plots are visual, model-agnostic techniques that depict the functional relationships between one or more input variables and the predictions of a black-box model. For example, a PD plot can show whether estimated car price increases linearly with horsepower or whether the relationship is another type, such as a step function, curvilinear, and so on. ICE plots enable data scientists to drill much deeper to explore individual differences and identify subgroups and interactions between model inputs.
This paper shows how PD and ICE plots can be used to gain insight from and compare machine learning models, particularly so-called “black-box” algorithms such as random forest, neural network, and gradient boosting. It also discusses limitations of PD plots and offers recommendations about how to generate scalable plots for big data. The paper includes SAS® code for both types of plots. SASGF18
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Boston Area SAS Users Group Quarterly Meeting – SAS Blowout Event & Trainings!
Meeting information
Featured presentations
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Webinar informationLisa Horwitz from SAS will present: A Long-Time SAS® Programmer Learns New Tricks Date and time: Friday, October 20, 2017 2:00 pm (Eastern Standard Time) Duration:1 hour
Meeting number: 595 279 717 Meeting password: GMmSr382 Join by phone +1 866 282 7366 US Toll Free +1 210 606 9466 US Toll Description: When a large and important project with a strict deadline hits your desk, it’s easy to revert to those tried-and-true SAS programming techniques that have been successful for you in the past. In fact, trying to learn new techniques at such a time may prove to be distracting and a waste of precious time. However, the lull after a project’s completion is the perfect time to reassess your approach and see if there are any new features added to the SAS arsenal since the last time you looked that could be of great use the next time around. Such a post-project post-mortem has provided me with the opportunity to learn about several new features which will prove to be hugely valuable as I rework this project for Round 2:
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Meeting information
Featured presentations
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Friday, November 18, 2016 1:00 pm Central Standard Time (Chicago, GMT-06:00) |
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Friday, November 18, 2016 11:00 am Pacific Standard Time (San Francisco, GMT-08:00) |
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Analyzing Multilevel Models with the GLIMMIX Procedure by Phil Gibbs. Thursday, July. 16, 2015 1:00 – 2:00 p.m.Learn how to use the GLIMMIX procedure in SAS/STAT to analyze hierarchical data that have a wide variety of distributions. Examples are included to illustrate the flexibility that PROC GLIMMIX offers for modeling within-unit correlation, disentangling explanatory variables at different levels, and handling unbalanced data.
A_First_Look_at_the_ODS_Destination_for_PowerPoint by Tim Hunter. Thursday, July. 16, 2015 2:15 – 3:15 p.m.This presentation introduces the ODS destination for PowerPoint, one of the next generation of ODS destinations.
Introduction to the SAS® Macro Facility by T. Winand. Friday, Jan. 30, 2015 2:30 – 3:30 p.m.Learn about the macro facility in an introduction to its purpose and functionality. You will learn the basics of creating and using both macro variables and macro programs.
Top 10 Ways to Optimize Your SAS code by T. Winand. Friday, Jan. 30, 2015 3:40 – 4:40 p.m.Learn tips and tricks to make your SAS code run more efficiently. There are at least six ways to do most things in SAS, so understanding some coding guidelines can help to guide efficient decisions. Some tips include: limiting the amount of data read, index usage, and efficient conditional processing.
STDRATE procedure and the EFFECT statement in SAS/STAT® by Phil Gibbs. Friday, Jan. 31 1:30 – 4 p.m.
Thursday, June 27 9:30 – 12:30
- An Introduction to Creating Multisheet Microsoft Excel Workbooks the Easy Way with SAS” with Vince DelGobbo
Vince DelGobbo’s ExcelXP Tagset Paper IndexWednesday, March 27 2013 12:00pm
- Executing a PROC from a DATA Step by Jason Secosky, a Software Development Manager at SAS
Tuesday, October 9, 2012 4:00pm
- Up Close and Personal with SAS® Enterprise Guide® 5.1 by I-kong Fu (a product manager at SAS in the business intelligence area). Click here to view the presentation slides.
Wednesday, March 21, 2012 4:00pm
- Programming Techniques for Optimizing SAS Throughput by Ruegsegger of IBM Microelectronics in Essex Junction, VT
Wednesday Oct. 12, 2011 12-1:30pm
- the Basics SAS Options – Versatile Players in the Game of SAS by Denise Poll of the SAS® Institute
June 8th, 2011 12:00pm-1:30pm Webinar:
March 17th, 2011 4:00-6:00pm @ 35 Centerra Parkway, Lebanon, NH (TDI, Dartmouth)
Oct. 7th, 2010 @ Lebanon, NH (TDI, Dartmouth)
- Creating Presentation-Quality ODS Graphics Output by SAS expert Dan Heath
June 23 DASUG: Health Care Claims Data Symposium 9am to 5pm @ Lebanon, NH
- Details and slides please see here
April 29 from 4-6pm@ Lebanon, NH
- SAS macro best practice and Dictionary Table by Frank DiIorio
November 5, 2009 @ Lebanon, NH
- Improve SAS I/O Throughput by Avoiding the Operating System File Cache by Leigh Ihnen of SAS Institute
June 11th, 2009 DASUG Officer’s meeting
April 9th, 2009 @ Lebanon, NH (TDI, Dartmouth)
- SAS Integration Technology by Robert Burnham from The Amos Tuck School of Business
Oct. 21, 2008 @ Dartmouth College
- SAS Visual Data Discovery by: Chuck Pirrello
- Meeting minutes
July 10, 2008
- Vanessa Hayden Presented Unit of Analysis Programming
- Meeting minutes
April 10, 2008
- Robert Rosofsky Presented “Make Your Match and Capture the Data, which is on the SAS rx() functions”
- Meeting Minutes
September 14, 2007
- Maura Stokes (SAS Institute) presented “Applications of GEE Methodology Using the SAS System “.
- Meeting Minutes
June 12, 2007
- Michael J. A. Berry presented “Business Applications of Time to Event Analysis”. Click here to download the presentation slides.
- Meeting Minutes
March 15, 2007
- Foster Kerrison presented “Hey, let SAS do the work!”. Click here to download the sample SAS code and presentation slides.
- Meeting Minutes
October 31, 2006
- Rebecca Symes, Maine Health Information Center, talked about “The Use of DDE in Exporting Data to Excel”. Click here to download the sample SAS code, SAS data and Excel File.
- Meeting Minutes
January 26, 2006
- Louise Hadden, Abt Associates, Inc., presented “ZIPCODE 411: One of SAS’s Best Kept (and Best) Secrets” and “Data Ferrett:The Most Efficient (and cutest) Data Source on the WWW – and it’s Free!“.
- Meeting Minutes N/A
December 10, 2004
- David Shamlin, SAS Institute, presented “Practical Perl Regular Expressions in SAS 9.1” and “ODS Tricks – Advanced Usage“.
- Meeting Minutes
September 17, 2004
- Louise Hadden, Abt Associates, Inc., presented “Whats In A Map? A Macro-Driven Drill-Down Geo-Graphical Representation System“.
- Meeting Minutes
June 18, 2004
- Steve Kearing, Dartmouth College, presented “SAS Application Development with Frames“.
- The sample application presented is available by emailing the web administrator.
- Meeting Minutes N/A
March 19, 2004
- Rick Langston, SAS Institute, presented “What’s New in SAS 9“.
- Meeting Minutes
December 19, 2003
- Charles Patridge, The Hartford, “Using AutoCall Macro Libraries and Changing Paths”.
- Michael L. Davis, Vice President, Bassett Consulting Services, Inc., “You Could Look It Up: An Introduction to SASHELP Dictionary Views“.
- Meeting Minutes
September 19, 2003
- Matt Grover, S-Street Consulting “Understanding the PROC TEMPLATE Procedure“.
- Meeting Minutes
June 20, 2003
- Foster Kerrison presented “Loops, do loops, and loops can lie” and “Sysfunc, my functional assistant”.
- Meeting Minutes
March 21, 2003
- Daniel Gottlieb of Dartmouth College presented “SQL: No it’s not short for Squeal“.
- Stephanie Raymond won the NESUG registration. Congratulations Stephanie!
- Meeting Minutes
December 13, 2002
- Stephanie Raymond of CECS Dartmouth Medical School presented “Unraveling Macro Mysteries, Tips and Techiques“.
- Meeting Minutes
September 26, 2002
- Chevell Parker of SAS Institute presented “Top Ten Tips and Techniques to Effectively Use ODS“.
- Meeting Minutes
June 27, 2002
- Bob Virgile of Robert Virgile Associates, Inc. presented “Introduction to Arrays” and “How MERGE Really Works“. Bob is the author of “An Array of Challenges Test Your SAS® Skills” and “Efficiency: Improving the Performance of Your SAS® Applications“.
- Meeting Minutes
March 18, 2002
- Craig Dickstein of Tamarack Professional Services and co-author of “Health Care Data and the SAS® System” presented “Data Step vs. PROC SQL: What’s a Neophyte to do?“.
- Meeting Minutes
December 14, 2001
- Paul Grant, from the SAS Institute, lead a very informative discussion on accessing data through various methods. (ODBC, OLE DB, DDE, Proc Import, Proc Export, etc.)
- Meeting Minutes
September 20, 2001
- Judy Loren from LLBean presented a talk on SQL
- Foster Kerrison presented a problem regarding a delimited file with missing values, along with several other SAS coding questions
- Meeting Minutes
June 01, 2001
- Mike Zdeb from New York presented “Creating Maps with SAS/GRAPH” and “Beyond Format Basics”.
- Meeting Minutes
January 22, 2001
- Jim Conley, from Elliot Hospital , discussed the SAS Certification Exam Process
- Coding Workshop covered topics on merging data sets, LIBNAME, FILENAME, and FILEVAR
- Meeting Minutes
November 10, 2000
- Liza Horwitz of the SAS Institute presented a paper titled “Getting Started with SAS or What the Data Step Can Do For You!”
- Candy Riel and Pat Kelly gave a presentation from the users perspective of the enhancements within Version 8.0
- Meeting Minutes
April 27, 2000
- Foster Kerrison opened the session for the Inaugural Meeting of our new Local SAS User Group.
- Bob Burnham from the Amos Tuck School of Business at Dartmouth College presented his paper from SUGI 25: GenHTML: A Library for Converting SAS® Datasets to HTML Tables
- Bruce Thomas from Dartmouth Medical School presented a paper on Proc SQL Tables
- Meeting Minutes
Wednesday, March 21, 2012 4:00pm
Programming Techniques for Optimizing SAS Throughput by Ruegsegger of IBM Microelectronics in Essex Junction, VT
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An Insider’s Guide to ODS LAYOUT Using SAS® 9.4 | |
Friday, November 20, 2015 | |
2:00 pm | Eastern Standard Time (New York, GMT-05:00) | 1 hour |