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About SESUG


About SESUG


 
 
  
CONFERENCE WORKSHOPS


Conference Workshops offer the bonus of additional educational experiences for SESUG Conference attendees.  We have talented, recognized, and experienced instructors prepared to share their knowledge in these 4-hour sessions.

SESUG is scheduling eight workshops this year with one on Saturday, October 19th, six on Sunday, October 20th, and one on Wednesday, October 23rd!! See the times in the table below.

The workshops cover a wide variety of topics and skill levels. These 4-hour workshops will appeal to everyone interested in exceptional in-depth training. Please note that workshops are an extra fee event at SESUG.

Saturday, October 19, 2019
50 Ways to Use SAS® ODS EXCEL Destination to enhance your Microsoft® Excel Workbooks William E Benjamin Jr 1pm-5pm
Sunday, October 20, 2019
All Together Now: Strategies for Combining Data from Multiple Sources Christianna Williams 8am-12pm
Advanced PROC SQL Concepts and Programming Techniques Using SAS® Kirk Paul Lafler 8am-12pm
Data-Driven Design in SAS® and Python: Developing More Dynamic, Flexible, Configurable, Reusable Software Troy Martin Hughes 8am-12pm
A Variety of Mixed Models David A. Dickey 1pm-5pm
ODS Graphics I: Creating Quick and Easy Graphs with the Statistical Graphics (SG) Procedures Josh Horstman 1pm-5pm
SAS Workshop (to be determined) SAS Instructor (to be determined) 1pm-5pm
Wednesday, October 23, 2019
Advanced SAS Macro Language Techniques for Building Dynamic Programs Josh Horstman 8am -12pm

Early Online Registration Fee $175 ($150 each if registering for 2+ for the same person)
Regular Onsite Registration $175
Administrative Fee for Workshop Only $25 (no conference registration)

 

For information on registering for a workshop or the conference, please see Registration Information.

 

  
Saturday, October 19, 2019



50 Ways to Use SAS® ODS EXCEL Destination to enhance your Microsoft® Excel Workbooks

Scheduled Time:  
Saturday, October 19th, 1:00 pm - 5:00 pm

Intended Audience:  Beginner to Intermediate SAS users

Instructor:  William E Benjamin Jr

Abstract:  Course Abstract: This course is intended for beginner to intermediate SAS® users. One of the first things a new SAS programmer learns is how to create output using the SAS ODS system. Many SAS programmers are also tasked with placing SAS data into Microsoft Excel workbooks. Before ODS existed, files were created using the SAS commands to place SAS data into Microsoft® Excel workbooks using the SAS DATA STEP to write CSV (Comma Separated Variable) text files that Excel could read. Then came various PROC TEMPLATE "TAGSETS" like "CSV", "HTML" and "EXCELXP" which created more text files that Microsoft Excel could also read. The newest method of creating Excel workbooks is the SAS ODS EXCEL Destination. The focus of this course is to show the students 50 ways to build and enhance Excel workbooks and worksheets using the SAS ODS EXCEL Destination. While not required, this course is based upon the book Exchanging Data From SAS® to Excel: The ODS Excel Destination. The course materials will be provided along with code examples. The topics listed below identify the major topics of the class.

  • ODS Tagset versus Destination
  • ODS Excel Destination Actions
  • Setting Excel Document Property Values
  • Options That Affect the Workbook
  • Arguments that Affect Output Features
  • Options That Affect Worksheet Features
  • Options That Affect Print Features
  • Column, Row, and Cell Features
  • Instructor Bio:   William's expertise includes Base SAS® Software, and SAS Macros. He has a BSCS degree from Arizona State University and an MBA from Western International University. He has used SAS software since 1983 and programmed computers since 1973. His experience spans from vacuum tube mainframes, to current PC computers. His consulting company is called OWL Computer Consultancy, LLC. His first SAS Press book "Exchanging Data between SAS and Microsoft Excel: Tips and Techniques to Transfer and Manage Data More Efficientl" was released in April 2015. and his new book "Exchanging Data From SAS to Excel: The ODS Excel Destination" was published Aug 30, 2017.

     

      
    Sunday, October 20, 2019



    All Together Now: Strategies for Combining Data from Multiple Sources

    Scheduled Time:  
    Sunday, October 20th, 8:00 am - 12:00 pm

    Intended Audience:  Beginning/Intermediate programmer; moderate pace

    Instructors:  Christianna Williams

    Abstract:  Problem 1: you have data and "metadata" that need to be combined to produce a user-friendly report.

    Problem 2: you have data in several different data sources each at different levels of aggregation (such as person-level, site-level, and event-level) and you need to combine it into a single data set for analysis or generating a report.

    Problem 3: You need to join data from two sources based on a range of values rather than an exact match. What is the best SAS strategy to solve each of these problems? When should you use a MERGE (or UPDATE)? When should you use an INNER, OUTER, or LEFT join? When should you use DATA Step SET or SQL UNION or PROC APPEND? Or when would thoughtful use of SAS Formats allow you to combine the data in an efficient way? This workshop will begin by presenting basic methods for combining data sets (both concatenation and joins) to set the stage for a series of examples addressing each of these problems and more. We will discuss strategies and solutions for each in order to help you choose the best approach for the data combination challenges you face, and emphasis will be placed on making a plan for your target data set before you start to code. We will use DATA Step, PROC SQL, PROC FORMAT and other strategies to get our data act together! I encourage you to bring examples of the types of data combination problems you have struggled with.

    Instructor Bio:  
    Christianna Williams, PhD has been a Senior Associate at Abt Associates Inc. since 2008. Although Abt is based in Cambridge, MA, Christianna primarily telecommutes from her home in Chapel Hill, North Carolina. An epidemiologist by training and disposition, she has worked in a variety of subject areas from the association of birth trauma with left-handedness to the quality of end-of-life care in nursing homes. Arguably, she spent way too much time in school and holds degrees from Duke University, Yale University and the University of California at Berkeley. Christianna started using SAS as a graduate student in population biology in 1985 and is still learning! She has been a frequent presenter at local and regional user group conferences as well as SAS Global Forum, and has been sharing her geeky love for SAS programming through teaching for more 20 years. She also devotes as much time as possible to her other passions: running, vegetarian cooking and reading novels.

     


    Advanced PROC SQL Concepts and Programming Techniques Using SAS®

    Scheduled Time:  
    Sunday, October 20th, 8:00 am - 12:00 pm

    Intended Audience:  Intermediate and Advanced SAS users. This is an Instructor-led Workshop with many examples.

    Instructor:  Kirk Paul Lafler

    Abstract:   Structured Query Language (SQL) is a universal language used in data science, data analytics, statistics, data management, and other disciplines to access, transform, manipulate and output data stored in SAS data sets, relational databases and tables. Based on Kirk's new Third Edition PROC SQL: Beyond the Basics Using SAS®, this half-day course presents core concepts and programming techniques to help leverage PROC SQL as a programming and database language.
    Attendees learn how to construct powerful and scalable queries; construct real-world queries including nearest neighbor and first, last and between By-group processing; apply rule-based and cost-based optimization strategies - influencing the SQL optimizer to choose from the available join algorithms; apply effective "fuzzy" matching programming techniques when a table's key(s) is (are) inconsistent or less than reliable; use the SQL-macro interface to create single-value (or aggregate) and value-list macro variables; construct effective simple and composite indexes to dynamically access a table's data; construct table validation rules using table integrity constraints; and explore “select” query performance tuning techniques for big data environments.

    Instructor Bio:   Kirk Paul Lafler is an entrepreneur and founder at Software Intelligence Corporation, and has used SAS software since 1979 as a consultant, application developer, programmer, SAS solutions provider, data analyst, data manager, infrastructure specialist, performance tuner, educator and author. As a SAS Certified professional, mentor, and educator at Software Intelligence Corporation, and an advisor and adjunct professor at the University of California San Diego Extension, Kirk has taught SAS courses, seminars, workshops, and webinars to thousands of users around the world. Kirk is also the author of several books including PROC SQL: Beyond the Basics Using SAS, Third Edition (SAS Press. 2019) along with hundreds of papers and articles on a variety of SAS topics; has been selected as an Invited speaker, educator, keynote and section leader at SAS conferences and meetings worldwide; and is the recipient of 25 "Best" contributed paper, hands-on workshop (HOW), and poster awards.

     


    Data-Driven Design in SAS® and Python: Developing More Dynamic, Flexible, Configurable, Reusable Software

    Scheduled Time:  
    Sunday, October 20th, 8:00 am - 12:00 pm

    Intended Audience:  Data-driven design and development are relevant to all levels of expertise across all industries.

    Instructor:  Troy Martin Hughes

    Abstract:  Students will receive a complimentary copy of the author's 2019 book SAS® Data-Driven Development: From Abstract Design to Dynamic Functionality, a $40 value! The course follows the book's outline and teaches data-driven techniques in which software customization, configuration, business rules, data models, data cleaning/validation, report style, and other dynamic elements are maintained in external data structures – NOT in the underlying code. Data-driven development techniques allow software to adapt flexibly to various organizations, environments, and objectives. This design facilitates highly configurable (i.e., "codeless") software whose functionality can be modified by changing only the underlying control data - the control tables, configuration files, parameters, and user-specified options rather than the code itself. All examples are demonstrated in both Base SAS 9.4 and Python 3.7, so the course is ideal for either SAS or Python developers seeking to expand their skills. All students will walk away with an understanding of how data-driven design minimizes software maintenance and modification, as well as proven data-driven development techniques that can be immediately implemented. In the first half, students will learn the basics of data-driven design and data structures (i.e., control data):

  • Compare data-driven software design with functionally equivalent code-driven design.
  • Identify dynamic elements within software and learn the benefits of controlling them remotely.
  • Create and read various file types that contain dynamic data elements, including batch files, configuration files, control files/tables, decision tables, business rule repositories, hierarchical taxonomies, and other data models.
  • Create and read various control data file formats, such as Excel spreadsheets, SAS data sets, XML files, CSS files, custom-formatted text files, and directory/folder contents.


  • In the second half, students will use data-driven methods to solve real-world problems:
  • Learn SAS-specific components that support data-driven development, such as the CALL EXECUTE statement, CNTLIN statement in PROC FORMAT, SYSPARM option, SAS dictionary tables, CSSSTYLE option in PROC REPORT, and SYMGET and SYMPUT functions.
  • Learn Python-specific components that support data-driven development, including NumPy and Pandas.
  • Write batch files that parameterize dynamic elements to initiate and execute software using customized user specifications.
  • Clean, standardize, and categorize data using dynamic data formats and dynamic data models.
  • Create quality control exception reports that use dynamic data dictionaries to identify erroneous data.
  • Transform data using dynamic business rules and conditional logic maintained outside of software.
  • Create “checkpoint” control tables that validate program/process success or indicate program/process failure.
  • Customize the style (e.g., format, font, color scheme, graphics, etc.) and content of data products.
  • Instructor Bio:   Troy has more than 20 years of experience leading SAS teams and projects in support of federal, state, and local government initiatives. Since 2013, he has given more than 90 presentations, trainings, and hands-on workshops at SAS conferences, including at SAS Global Forum, SAS Analytics Experience, WUSS, SCSUG, SESUG, MWSUG, and PharmaSUG. Additionally, he has authored two books that model design and development best practices: - SAS Data Analytic Development: Dimensions of Software Quality (2016) - SAS Data-Driven Development: From Abstract Design to Dynamic Functionality (2019) Troy has an MBA in information systems management and numerous certifications including SAS Base, SAS Advanced, SAS Clinical Trials, PMP, PMI-RMP, PMI-PBA, PMI-ACP, CISSP, CSSLP, ITIL, CSM, CSD, CSPO, CSP-SM, and CSP-PO. He is a US Navy veteran with two tours of duty in Afghanistan.

     


    A Variety of Mixed Models

    Scheduled Time:  
    Sunday, October 20th, 1:00 pm - 5:00 pm

    Intended Audience:  Statisticians, analysts, banking and medical statistics researchers

    Instructor:  David A. Dickey

    Abstract:   Mixed models are those with fixed and random effects. In ordinary mixed models, one estimates the fixed effects using estimated generalized least squares where the variance-covariance matrix of the data is estimated as part of maximum likelihood or REML (Restricted, or Residual, Maximum Likelihood) algorithm. After reviewing how to distinguish random from fixed effects, this course will describe the overall methodology and show several examples of its application including random coefficient models, repeated measures and hierarchical models. A review of nonlinear models is included and the additional complexities arising from the inclusion of random effects illustrated. A third type of model, the generalized linear mixed model, is discussed with examples. Such a model arises when the response is not normally distributed but rather is in the exponential family of distributions. Outstanding examples of the exponential family are the binomial and Poisson distributions. Emphasis is on concepts, examples, when to apply each type of model, and how to interpret each.

    Instructor Bio:   David A. Dickey is W. N. Reynolds Professor (emeritus) of Statistics at NC State University. He is known for the Dickey-Fuller test for unit roots in time series. He is a Fellow of the American Statistical Association. He has spoken at the ASA's JSM, ASQ, and CSP meetings and many times at SAS Global Forum and regional SAS Users' Group meetings. Dickey has co-authored several books and dozens of papers. He was major advisor to 16 PhD students at NCSU and served on hundreds of graduate student committees across campus. Dickey is a member of NCSU's Academy of Outstanding Teachers and Academy of Outstanding Faculty Engaged in Extension. He received the D.D. Mason Faculty Award in 1986 and 2018 and the Outstanding Extension Service Award in 2007. Dickey was a founding faculty member of NCSU's Institute for Advanced Analytics, holds an associate appointment in Economics, and is a member of the Financial Math faculty. He taught at Randolph Macon College and The College of William and Mary in Virginia for 3 years before earning his PhD in 1976 under Wayne Fuller and spending the next 43 year at NC State.

     


    ODS Graphics I: Creating Quick and Easy Graphs with the Statistical Graphics (SG) Procedures

    Scheduled Time:  
    Sunday, October 20th, 1:00 pm - 5:00 pm

    Intended Audience:  Novice to intermediate SAS programmers. Pace will be moderate, allowing plenty of time for Q&A as we go. I like to keep my classes as interactive as possible.

    Instructor:  Josh Horstman

    Abstract:   The ODS Statistical Graphics (SG) Procedures represent a complete paradigm shift for the creation of high-quality graphics using the SAS system. Legacy SAS/GRAPH functions produce crude graphics that frequently do not meet today's standards of presentation. While customization is possible, it can require extensive coding and several tricks to achieve desirable results. With the introduction of the SG procedures, all of that changed. This course will provide an overview of the major procedures such as SGPLOT, SGPANEL, and SGSCATTER as well as related statements and common options using numerous examples. Upon completion of the course, students will have the tools they need to start producing high-quality graphics and performing basic customization using the options available.

    Instructor Bio:   Josh Horstman is an independent statistical programmer based in Indianapolis with over 20 years' experience using SAS in the life sciences industry. He specializes in analyzing clinical trial data, and his clients have included major pharmaceutical corporations, biotech companies, and research organizations. A SAS certified programmer, Josh loves coding and is a frequent presenter at SAS Global Forum and various regional and local SAS users group. Josh holds a bachelor's degree in mathematics and computer science, and a master's degree in statistics from Colorado State University.

     


    Topic To be Determined

    Scheduled Time:  
    Sunday, October 14th, 1:00 pm - 5:00 pm

    Intended Audience:  To be Determined

    Instructor:  SAS Speaker To be Determined

    Abstract:  To be Determined

    Instructor Bio:   SAS Speaker To be Determined

     


    Advanced SAS Macro Language Techniques for Building Dynamic Programs

    Scheduled Time:  
    Wednesday, October 23, 2019 8:00 am - 12:00 pm

    Intended Audience:  Early intermediate to advanced SAS programmers who are familiar with the basics of the SAS macro language. Pace will be moderate, allowing plenty of time for Q&A as we go. I like to keep my classes as interactive as possible.

    Instructor:  Josh Horstman

    Abstract:   This seminar shows you how to take advantage of SAS Macro Language capabilities that enable you to write dynamic programs and applications. By mastering the concepts and techniques presented in this class your programs will become free of hard-coded data dependencies, thus eliminating the need to re-write the code every time a data set name, variable name, or other data attribute changes. Topics will include how to build and process macro variable lists, using the macro language to control the data environment, using control files, working with datasets and libraries in the macro language, accessing the SAS data dictionaries, and other miscellaneous macro topics that will help you create dynamic code. (course licensed from Art Carpenter)

    Instructor Bio:   Josh Horstman is an independent statistical programmer based in Indianapolis with over 20 years' experience using SAS in the life sciences industry. He specializes in analyzing clinical trial data, and his clients have included major pharmaceutical corporations, biotech companies, and research organizations. A SAS certified programmer, Josh loves coding and is a frequent presenter at SAS Global Forum and various regional and local SAS users group. Josh holds a bachelor's degree in mathematics and computer science, and a master's degree in statistics from Colorado State University.

     

     

     
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