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MWSUG 2021 Training Classes

Customize Your Own Training Curriculum

MWSUG offers a full menu of pre-conference training courses. These training classes are learning opportunities which allow you to delve more deeply into a topic. Classes are offered on the Friday, Saturday, and Sunday prior to the conference.

Any of our courses are open to any person who wants to take them. To help you, we have grouped the courses into “tracks” related to your interests. For example, if you are a Statistical Programmer, you might be interested in the courses in the first column. If you are, or are learning to be, a Statistician or Data Scientist, the courses in the 2nd column might be of interest.

Note that there are courses with some overlapping content. You can take all of them and get a deeper introduction into the topics. You can also combine some of these with other courses to broaden your skills.

You may mix and match courses however you like to suit your needs. Discounts are available when you take multiple classes (see below for fees). Take advantage of this opportunity to build your own custom training curriculum!

Course Schedule

Click on the course title for a short description. Click on the instructor name(s) for biographical information.

Friday, September 10, 2021

Time Room 1
Programmer
Room 2
Data Scientist
8am - Noon Introduction to SAS Macro Programming Pitfalls of Regression Analysis Part 1: Don’t let influential data observations kill your regression and your career
1pm - 5pm Intermediate SAS Macro Programming Pitfalls of Regression Analysis Part 2: The power of dummy variables in regression

Saturday, September 11, 2021

Time Room 1
Programmer
Room 2
Data Scientist
Room 3
Data Engineer
Room 4
Output Producer
8am - Noon Driving Miss Data: Data-Driven Techniques Using Python to do what YOU do in SAS Learn SAS® Analytical, Graphical and Reporting Techniques Using Public Use Data Sets (PUFs) Generating 44 Fantastic Reports with PROC REPORT
1pm - 5pm 50 Valuable Techniques to Optimize SAS® Code Performance and Processing Bayesian analysis makes total sense Working with Administrative Healthcare data sets using BASE SAS programming ODS Graphics I: Creating Quick and Easy Graphs with the Statistical Graphics (SG) Procedures

Sunday, September 12, 2021

Time Room 1
Programmer
Room 2
Data Scientist
Room 3
Data Engineer
Room 4
Output Producer
8am - Noon Introduction to PROC REPORT 1: The Basics Getting Started with Time Series Data and Analysis in SAS Introductory ADaM Dataset Development: ADSL, OCCDS and BDS ODS Excel® Programming Techniques by Example
1pm - 5pm Introduction to PROC REPORT 2: Using the Compute Block Introduction to Advanced Time Series Analysis in SAS Advanced ADaM Dataset Development: Beyond the ADaMIG Building High-Impact Dashboards Using Base SAS® Software



Course Fees and Registration

Registration Type Half Day Full Day Two Day Bundle Three Day Bundle - BEST VALUE!
Conference Attendee $175 $350 $650 $945
Without Conference $275 $550 $1,000 $1,440
Full-Time Student $100 $200 N/A N/A

To register for a training class, please select the corresponding class during the registration process. Seating for the training classes are limited and registration will be accepted on a first-come, first-served basis. The two or three day bundle price may be used for any combination of training courses equivalent to two or three full days (four or six half days, respectively).

To take advantage of the bundle pricing, you will need a coupon code. Please email the registrar at This email address is being protected from spambots. You need JavaScript enabled to view it. with a list of the courses you are registering for. After your courses are verified, you will be emailed a coupon code to be used to complete your registration.



Course Descriptions

Introduction to SAS Macro Programming
Ben Cochran
Friday, September 10, 2021, 8:00 AM - 12:00 PM, Location: TBD


Topics include:
  1. Processing the Macro Language
  2. Macro Variables, Quoting in the Macro Facility, Macro Functions
  3. SAS Macro Programs, Building SAS Macros, Passing Information To and From Macro Programs, Macro System Options, Conditional Processing, Local and Global Symbol Tables
  4. Macro Facility Interface: Creating Macro Variables in the DATA step, Table Driven Applications



Pitfalls of Regression Analysis Part 1: Don’t let influential data observations kill your regression and your career
Steven Myers
Friday, September 10, 2021, 8:00 AM - 12:00 PM, TBD


If your regression models could use a tune-up and you'd like to get better insights from your data, this class is for you! How do you know if your data is lying to you? The answer lies in following ethically applied econometric rules and being aware of and avoiding pitfalls in regression practice. The essential skills in this training are not typically taught in a single-semester econometric or regression course.

Regression analysis is like a puddle in which a novice can wade, and an expert drown.

We are going to explore the depths. For example, do you know that two regressions models can both be highly significant and produce very different conclusions? You will learn processes, a checklist of pitfalls, to help you know which model is correct and which is lying to you. This class is for statisticians and programmers who already have a basic understanding of regression models and some experience writing SAS programs to manipulate and analyze data." It will be appreciated by experienced analysts and statisticians alike. This course is based on the 10 Regression Pitfalls, developed by the author. It also leans on an ethical approach to applied analysis. You will learn what you must do to be an ethical data analyst and how to avoid regression pitfalls.

Part 1 will cover the first regression pitfalls by way of examples with data and code that you will be able to access. A focus will be on what you can learn with residual analysis. Ever rush to a regression model because it seems the right approach and is a quick way of documenting a relationship from which you will make business decisions based on the explanations (estimates) or the prediction? This paper will discuss and show how you could confirm your expectations with strong statistical indicators and still be extremely wrong (confirmation bias). Did you do any exploratory investigation before you ran the regression and perhaps more importantly did you do any exploration of the data after you ran the regression? Graphics can be used in simple regression especially when the data is ordered, but for multiple regression we need to statistically analyze the residuals. We will discuss the role of data cleaning and model specification in the case of contaminated data, non-normal errors, and influential observations. OLS parametric regression, robust parametric estimation and local nonparametric regression will be discussed.


Intermediate SAS Macro Programming
Ben Cochran
Friday, September 10, 2021, 1:00 PM - 5:00 PM, Location: TBD


Topics include:
  1. The Macro Applications Environment
  2. Creating Macro Programs that read data
  3. Developing Macros that are reusable
  4. Using Macro variables as Prompts



Pitfalls of Regression Analysis Part 2: The power of dummy variables in regression
Steven Myers
Friday, September 10, 2021, 1:00 PM - 5:00 PM, Location: TBD


If your regression models could use a tune-up and you'd like to get better insights from your data, this class is for you! The essential skills in this training are not typically taught in a single-semester econometric or regression course.

Regression analysis is like a puddle in which a novice can wade, and an expert drown.

We are going to explore the depths. For example, do you know that two regressions models can both be highly significant and produce very different conclusions? You will learn processes to help you know which model is correct and which is lying to you. This class is for statisticians and programmers who already have a basic understanding of regression models and some experience writing SAS programs to manipulate and analyze data." It will be appreciated by experienced analysts and statisticians alike. This course is based on the 10 Regression Pitfalls, developed by the author. It also leans on an ethical approach to applied analysis. You will learn what you must do to be an ethical data analyst and how to avoid regression pitfalls.

Part 2 will cover the second half of the regression pitfalls by way of examples with data and code that you will be able to access. The focus of the second course will be working with and using dummy variables. Few tools are as powerful as various regression procedures. Choosing to model with right hand side dummy variables can raise the power of the regression modeling many fold. Perhaps we desire to model the result of a treatment or highlight certain observations for unique influence, or find results across an entire class of observations. This paper will discuss the coding, specification, testing and interpretation of dummy variables as we represent binary classifications (yes/no, male/female) and multiple classifications (strongly agree, somewhat agree, neither agree nor disagree, somewhat disagree, strongly agree). Additionally, using dummy variables in log equations, for seasonality, fixed effects and structural change models will be discussed.


Driving Miss Data: Data-Driven Techniques
Richann Watson
Saturday, September 11, 2021, 8:00 AM - 12:00 PM, Location: TBD


We have all been there. We write a program based on the data we have. Then, we get new data and we must update the program. Making these updates can be time consuming. Not only must you update the production version of the program, but someone must also update any associated validation or QC programs. Wouldn’t it be nice if there were ways around this? This is where data-driven techniques come in handy. Using detailed examples, you will learn how to write robust code that is ready to handle an unexpected bend in the road! This half-day course will cover advanced techniques such as: discovering and using information about data sets and variables even if it's not known in advance; generating dynamic formats that are based on the data instead of hard-coded into your program; using complex looping structures to control your program flow based on the data; building code on the fly, even from within a DATA step; and much more!


Using Python to do what YOU do in SAS
Russ Lavery
Saturday, September 11, 2021, 8:00 AM - 12:00 PM, Location: TBD


The Python module in python is the module that manages dataframes (tabular data like and XLS sheet or a SAS data set) and this 4 hour seminar concentrates on using Pandas to do data manipulations done in SAS (read data, append, merge, subset rows and columns, create a new variable, making table summaries (like a proc freq), and make charts. Python is a big object oriented language and has offer 80 different types of objects and this concentrates on the one data type most used by analysts. This seminar will teach to do useful thangs and a bit of data cleaning. However, fixing coding errors often involves knowing how to manage the other data types. It is hoped that MWSUG will offer follow-up up classes to broaden attendee skill sets.


Learn SAS® Analytical, Graphical and Reporting Techniques Using Public Use Data Sets (PUFs)
Louise Hadden
Saturday, September 11, 2021, 8:00 AM - 12:00 PM, Location: TBD


Interest in data sources useful for demonstrating statistical, graphical and reporting techniques has increased with the exponential growth of interest and activity in the fields of Data Science, Machine Learning, and Natural Language Processing. Thus, freely available and reliable banks of data have become highly sought after. This tutorial will introduce three high quality and robust data sources for analytic work suitable for journal submissions, and explore in depth public use data sets and BASE SAS tools that can be used to analyze and graphically represent measures and trends. These data sets include USAID’s Demographic and Health Surveys which include health survey data from Afghanistan to Zimbabwe; the Centers for Medicare and Medicaid Services' Care Compare Tool (data.medicare.gov and data.cms.gov) focusing on nursing homes; and CDC’s National Health and Nutrition Examination Survey (NHANES) which will demonstrate how to work with a complex sampling design. Exploration of the NHANES survey will also include the use of National Cancer Institute (NCI) macros to analyze usual daily intake. The tutorial will prepare attendees to construct an analysis plan (AP) and standard operating procedures (SOPs) for researching, analyzing and documenting PUFs. SAS tools used will be standard statistical and reporting tools available in BASE SAS, as well as geographic tools including PROC GEOCODE.


Generating 44 Fantastic Reports with PROC REPORT
Ben Cochran
Saturday, September 11, 2021, 8:00 AM - 12:00 PM, Location: TBD


Topics include:
  1. Basic reports
  2. Using the Compute Block
  3. Using ODS to control ALL aspects of the report



50 Valuable Techniques to Optimize SAS® Code Performance and Processing
Kirk Paul Lafler
Saturday, September 11, 2021, 1:00 PM - 5:00 PM, Location: TBD


In an era of big data and rapidly changing technologies, programmers and developers design software adhering to a specified set of functionalities and the assumption that their program code will run on processors with limited computational power. Adding to these issues, performance and/or efficiency aspects are often given little attention or simply ignored to ensure that program code uses the fewest resources (i.e., CPU, I/O, storage, and memory) possible. This course presents 50 valuable techniques to control, manage and optimize program execution, I/O, memory, disk space, and program maintenance activities to deliver faster results while reducing the demand for limited resources. Topics include SAS® optimization techniques to enable your code to run faster, reduce input/output (I/O) operations, and affect storage demands by utilizing efficient coding constructs and functions, removing redundant code, eliminating dead code, leveraging memory speeds, avoiding type conversions, assigning system options to optimize processing efficiencies, using arrays and user-defined formats for table lookup operations, organizing conditional statements by probability of occurrence, optimizing looping scenarios, accessing data subsets sequentially or with indexes, and the application of other scalable techniques.


Bayesian analysis makes total sense
Russ Lavery
Saturday, September 11, 2021, 1:00 PM - 5:00 PM, Location: TBD


Bayesian statistics is growing in popularity every day and should be a tool understood by all modelers. SAS has some excellent PROCS for Bayesian analysis. This introductory cartoon format, example rich and interactive seminar is a very easiest way to learn logistic regression. This is an introduction to Bayesian analysis and assumes an understanding of basic statistics and regression…and that the attendee has not forgotten all of high school algebra. Topics covered will be: Why Bayes’ law (formula) makes perfect sense to a frequentist (two examples) Why Bayes’ law (formula) makes perfect sense to a bayesian (two examples) Why we all think like Bayesians (updating beliefs in the presence of new information) A review of some important distributions (beta, bernouli, binomial, gamma) Examples of three closed form solutions: (conjugate priors)
  • updating a percentage with new information
  • updating a Poisson lambda with new information
  • updating a normal distribution with new information
The Metropolis Hastings algorithm ad Burn-in Examples of Bayesian regression using SAS Examples of PROC MCMC


Working with Administrative Healthcare data sets using BASE SAS programming
Jayanth Iyengar
Saturday, September 11, 2021, 1:00 PM - 5:00 PM, Location: TBD


The course is geared towards data analysts, programmer\analysts, and SAS programmers within the healthcare industry who need to understand the nuances and complexities of healthcare data structures to perform their responsibilities. This training seminar will give attendees an overview and detailed explanation of the different types of healthcare data, and the SAS programming constructs to work with them. This includes different types of healthcare claims data, such as facility claims, professional claims, pharmacy claims, and Medicare claims. In addition, attendees will receive a background and in-depth explanation of healthcare systems, and the U.S. Medicare System. The course features demonstrations using SAS to perform analytic and reporting tasks with healthcare data sets.


ODS Graphics I: Creating Quick and Easy Graphs with the Statistical Graphics (SG) Procedures
Josh Horstman
Saturday, September 11, 2021, 1:00 PM - 5:00 PM, Location: TBD


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.


Introduction to PROC REPORT 1: The Basics
Josh Horstman
Sunday, September 12, 2021, 8:00 AM - 12:00 PM, Location: TBD


The REPORT procedure is one of the most commonly-used methods for generating reports in SAS. This workshop will cover the basic building blocks through a series of increasingly complex examples. Topics include the basic syntax of PROC REPORT, the COLUMN and DEFINE statements, basic customization of the layout and headers, and the use of groups and breaks.


Getting Started with Time Series Data and Analysis in SAS
David Corliss
Sunday, September 12, 2021, 8:00 AM - 12:00 PM, Location: TBD


Time series data management
  • Time Series Data
  • PROC TIMEDATA
  • Imputation of Missing Data

Basic analytic methods
  • Time Series Regression
  • Lag Models
  • Seasonality



Introductory ADaM Dataset Development: ADSL, OCCDS and BDS
Nancy Brucken
Sunday, September 12, 2021, 8:00 AM - 12:00 PM, Location: TBD


This half-day seminar introduces attendees to CDISC ADaM and the ADaM documents. We will discuss how ADaM fits into the clinical process, and describe the key principles of ADaM. We will cover how to apply the basic ADaM concepts, rules, recommended best practices, and the four types of ADaM metadata. The seminar then explains the ADSL, OCCDS and BDS models. Submission deliverables like ADRG and ADaM define.xml will be discussed as well. A basic understanding of SDTM and regulatory submission needs is expected.


ODS Excel® Programming Techniques by Example
Kirk Paul Lafler
Sunday, September 12, 2021, 8:00 AM - 12:00 PM, Location: TBD


SAS® software is the “gold” standard for robust and reliable data access, manipulation, analytics, analysis, reporting, and data discovery. Microsoft Excel® is the most widely used software in the world. This popular course illustrates how to leverage the Output Delivery System (ODS) Excel destination to create customized, and exciting, output and results in Microsoft Excel; apply ODS Excel options; customize output and results with SAS-supplied styles; add background images and traffic lighting to Excel workbooks; build interactive drill-down Excel workbooks; and construct SAS and ODS Excel code to share data, tables, statistics, images, reports, and results to create robust Microsoft Excel files and workbooks.


Introduction to PROC REPORT 2: Using the Compute Block
Josh Horstman
Sunday, September 12, 2021, 1:00 PM - 5:00 PM, Location: TBD


Building on the basic concepts learned in part 1, this workshop will cover the use of the COMPUTE block in PROC REPORT. The COMPUTE block allows for the addition of complex logic to create highly customized reports. Examples will show the basic functionality of the COMPUTE block, how it interacts with report items, the use of temporary variables, and some timing issues related to the COMPUTE block.


Introduction to Advanced Time Series Analysis in SAS
David Corliss
Sunday, September 12, 2021, 1:00 PM - 5:00 PM, Location: TBD


Predicting the time to an event
  • Survival
  • Survival on Survey Data
  • Cox Proportional Hazards

Introduction to Forecasting Methods
  • Moving Average
  • Autoregressive
  • ARCH Models



Advanced ADaM Dataset Development: Beyond the ADaMIG
Nancy Brucken
Sunday, September 12, 2021, 1:00 PM - 5:00 PM, Location: TBD


This half-day course takes you beyond the examples in the ADaM Implementation Guide (ADaM IG), and shows you how to create analysis-ready datasets to meet more complex analysis requirements. Among the topics to be discussed are the addition of columns versus rows, approaches for handling multiple baseline definitions, creation of intermediate datasets while maintaining traceability back to SDTM, and avoidance of circular processing logic in ADSL. A working knowledge of basic ADaM structures and principles is expected.


Building High-Impact Dashboards Using Base SAS® Software
Kirk Paul Lafler
Sunday, September 12, 2021, 1:00 PM - 5:00 PM, Location: TBD


Organizations around the world develop static and interactive reports, spreadsheets and dashboards for the purpose of displaying the current status of “point-in-time” data, charts, tables, reports, statistics, scorecards, metrics and key performance indicators. Effectively designed dashboards, along with the code behind them, involves the extraction of data from a variety of sources, the performance of a series of data cleaning steps, restructuring and reformatting data, and the production of charts, tables and reports with the purpose of highlighting important information, numbers, tables, statistics, metrics, performance information and other essential content on a single screen. This popular half-day course explores the best practice programming techniques to build static and interactive drill-down dashboards (containing hyperlinks) using Base-SAS® software to drive awareness and understanding of summary and detail results. Attendees learn how to create high-impact dashboards with a purpose not in weeks or months, but in hours, using the DATA step; PROC FORMAT; PROC PRINT; PROC REPORT; PROC MEANS; PROC SQL; Output Delivery System (ODS); the macro language; Statistical Graphic procedures: PROC SGPLOT, PROC SGSCATTER, PROC SGPANEL, and PROC SGRENDER; Graphics Template Language (GTL); and PROC TEMPLATE.





Instructor Biographies


Nancy Brucken

Nancy Brucken is currently a Standards Engineer at Clinical Solutions Group, a division of IQVIA. She has over 30 years of experience as a SAS programmer, and has been a member of the CDISC ADaM team since 2011, working on the OCCDS v1.0, ADaMIG v1.2, Traceability and ADaM Library sub-teams, and co-leading the ADQRS sub-team.

Ben Cochran

After more than 11 years with SAS in the Professional Services (as an Instructor) and Marketing Departments (as Marketing Manager for the SAS/EIS product), Ben Cochran left to start his own consulting and SAS Training business in the fall of 1996 – The Bedford Group. As a Silver member of SAS Institute’s Alliance Partner Program, Ben has been involved in many consulting projects over the last 20 years and has been teaching SAS courses since 1985. Ben has authored and presented dozens of papers as well as being an invited speaker at SUGI/SGF, regional and local user groups on a variety of topics since 1988.

David Corliss

Dr. David J Corliss is a statistical astrophysicist specializing in the dynamics of evolving stellar and cosmic populations. He was worked in the automotive industry for more than 20 years, with extensive work in dynamics of evolving populations of car buyers, reporting and visualization, operations research, big data methods, analytic platform design, and statistical methodology. Continuing astrophysics research part-time, an important focus of his work has been to bring new developments in academic research to industrial and private sector research. He presents regularly at local and national SAS events and other conferences, and is active in the American Statistical Association, serving as President of the Detroit Chapter and on the steering committee of the Conference on Statistical Practice. David Corliss is the Founder and Director of Peace-Work, a volunteer cooperative of statisticians and data scientists providing analytic support for charitable groups and applying statistical methods to issue-driven advocacy in Data For Good projects.

Louise Hadden

Ms. Hadden is a Lead Programmer Analyst at Abt Associates Inc., a social science research firm. She is an expert SAS programmer, focusing on health and environmental issues. She works on numerous government contracts, including with CMS, CDC and state governments. She is co-author on multiple peer reviewed journal articles, as well as more than one hundred SAS papers, a number of which earned best contributed paper. Several journal articles were published using public use data sets. She has presented, volunteered and participated on conference teams at multiple SAS user group conferences. She is also the girl with the SAS tattoo.

Josh Horstman

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 Advanced Programmer, Josh loves coding and is a frequent presenter and trainer 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.

Jayanth Iyengar

Jay Iyengar is Principal of Data Systems Consultants LLC. He is a SAS Consultant, Trainer, and SAS Certified Advanced Programmer. He was co-leader of the Chicago SAS Users Group, WCSUG from 2015-19. He’s presented papers at SAS Global Forum (SGF), Midwest SAS Users Group (MWSUG), Wisconsin Illinois SAS Users Group (WIILSU), Northeast SAS Users Group (NESUG), and Southeast SAS Users Group (SESUG) conferences. He has been using SAS since 1997. His industry experience includes Healthcare, Pharmaceutical, Public Health, Direct Marketing and Educational Testing.

Kirk Paul Lafler

Kirk Paul Lafler is a lecturer and adjunct professor at San Diego State University; an advisor and adjunct professor at the University of California San Diego Extension; and teaches SAS, SQL, Python, R and Excel courses, seminars, workshops, and webinars to students, professionals and users around the world. Kirk has been a SAS user since 1979 and is the author of several books including, PROC SQL: Beyond the Basics Using SAS, Third Edition (SAS Press. 2019) along with papers and articles on a variety of SAS topics. Kirk has also been selected as an Invited speaker, educator, keynote and section leader at SAS conferences; and is the recipient of 25 “Best” contributed paper, hands-on workshop (HOW), and poster awards.

Russ Lavery

Russ is a frequent and multiple award winning presenter at SAS conferences. He has reviewed five books by SAS press and presented at conferences all over the US, in Europe and in Asia. He is suave and witty and beloved by dogs and small children. He lives outside of Philadelphia.

Steven Myers

Steven Myers is an educator in applied econometrics and an evangelist for economists in data science. He is the 2019 SAS Distinguished Educator. He specializes in combining economic and business acumen with rigorous statistical and programming techniques to solve problems and relate solutions to business leaders. He taught and mentored SAS programming, data handling, statistics and applied econometrics for over 40 years. He is a recovering CIO and is Associate Professor Emeritus at The University of Akron and on their adjust faculty. He has presented at MWSUG, SCSUG, and SAS Global Forum. For more information visit https://econdatascience.com/about/current-bio-and-pictures/ or https://www.linkedin.com/in/stevencmyers/

Richann Watson

Richann Watson is an independent statistical programmer and CDISC consultant based in Ohio. She has been using SAS since 1996 with most of her experience being in the life sciences industry. She specializes in analyzing clinical trial data and implementing CDISC standards. Additionally, she is a member of the CDISC ADaM team and various sub-teams. Richann loves to code and is an active participant and leader in the SAS User Group community. She has presented numerous papers, posters, and training seminars at SAS Global Forum, PharmaSUG, and various regional and local SAS user group meetings. Richann holds a bachelor’s degree in mathematics and computer science from Northern Kentucky University and master’s degree in statistics from Miami University.
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