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 Sunday prior to the conference. Mix and match courses however you like to suit your needs and interests! Take advantage of this opportunity to build your own custom training curriculum!
Updated August 11, 2026
Course Fees
$175 per half-day class with MWSUG 2026 Conference registration$250 per half-day class without conference registration
Sunday, November 15, 2026
| Course Title (click for description) | Instructor(s) (click for bio) | Time |
| Mastering Statistical Hypothesis Testing in the Age of AI: Comparative Analytics with Python, R, and SAS | Ryan Paul Lafler & Miguel Angel Bravo |
8:00 AM - 12:00 PM |
| SQL Joins Demystified: A Visual and Practical Tutorial to Master PROC SQL Joins Using Real-World Data | Kirk Paul Lafler | 8:00 AM - 12:00 PM |
| Introductory SAS Visual Analytics: Concepts, Reports, and Interactive Dashboards | James Blum | 8:00 AM - 12:00 PM |
| ODS Workshop: The Output Delivery System from Beginning to End | Jay Iyengar | 8:00 AM - 12:00 PM |
| Time for a Quick Start to Time Series Analysis | Danny Modlin | 1:00 PM - 5:00 PM |
| Fifty-Five Functions to Supercharge your SAS Code | Joshua Horstman | 1:00 PM - 5:00 PM |
| Creating Custom Graphs Using SAS and R | Richann Watson | 1:00 PM - 5:00 PM |
| Essential SAS Macro Language: Concepts, Tools, and Tricks for Novice Macro Users | James Blum | 1:00 PM - 5:00 PM |
Course Descriptions
Mastering Statistical Hypothesis Testing in the Age of AI: Comparative Analytics with Python, R, and SASRyan Paul Lafler, Miguel Angel Bravo
Sunday, November 15, 2026, 8:00 AM - 12:00 PM
This hands-on workshop provides a practical introduction to statistical hypothesis testing, comparative statistical programming, and reproducible analytical workflows across Python, R, and SAS . As AI-enabled analytics, automated modeling workflows, and open-source tools become more common across regulated and research environments, professionals need the statistical foundation to evaluate results, validate assumptions, interpret model behavior, and determine whether analytical conclusions are reliable.
Designed for data scientists, statisticians, statistical programmers, analysts, researchers, students, and professionals working in clinical, healthcare, pharmaceutical, policy, regulatory, operational, and applied research settings, this workshop focuses on selecting appropriate statistical tests, evaluating assumptions, interpreting results, and implementing accepted hypothesis testing techniques across multiple programming environments.
Attendees will gain practical experience applying parametric and nonparametric statistical testing methods that support well-defined Statistical Analysis Plans (SAPs), reproducible analytics, and defensible reporting. Through guided examples and hands-on exercises, attendees will compare how equivalent statistical workflows are implemented in Python, R, and SAS, including differences in syntax, output, diagnostics, assumptions, and interpretation.
Key Topics covered in this workshop include:
- Exploratory data analysis (EDA), data summarization, visualization, and preprocessing across Python, R, and SAS
- The role of hypothesis testing in SAP-driven analysis, regulated analytics, research, and AI-enabled analytical workflows
- Statistical significance, practical significance, clinical significance, and effect size interpretation
- Selecting appropriate parametric and nonparametric tests based on research questions, data structure, and model assumptions
- Comparing two groups using Welch s two-sample t-test and the Mann-Whitney U test
- Comparing multiple groups using one-way ANOVA and the Kruskal-Wallis test
- Factorial ANOVA models, interaction effects, model assumptions, and diagnostic checks
- Cross-language implementation patterns for Python, R, and SAS
All registered attendees will receive non-redistributable PDF slides, fully documented Python and R notebooks, SAS programs, and workshop datasets so they can reproduce the analyses and continue practicing after the workshop.
SQL Joins Demystified: A Visual and Practical Tutorial to Master PROC SQL Joins Using Real-World Data
Kirk Paul Lafler
Sunday, November 15, 2026, 8:00 AM - 12:00 PM
Have you ever wondered why SQL joins seem confusing or why seemingly simple joins sometimes produce unexpected results, duplicate observations, missing records, or even thousands of extra rows? You're not alone. SQL joins are among the most powerful and frequently misunderstood features of SQL. Understanding how they work is essential for anyone who combines data from multiple tables to produce accurate reports, dashboards, analytical datasets, or regulatory submissions.
This engaging, instructor-led tutorial removes the mystery surrounding SQL joins by building a solid conceptual foundation before writing a single line of code. Through easy-to-understand visualizations, Venn diagrams, relational database concepts, and carefully designed examples, attendees will learn exactly how each join works, why different joins return different results, and when each join should be used.
Attendees will explore the most used SQL joins, including INNER, LEFT, RIGHT, FULL, CROSS, and SELF joins, along with multi-table joins, non-equijoins, and common join strategies used in production environments. Each join type is presented using a consistent learning framework that includes an introduction, business use cases, advantages and disadvantages, visual Venn diagrams, source tables, fully documented SAS PROC SQL code, annotated results, performance considerations, common programming mistakes, troubleshooting techniques, and key takeaways.
By the end of this half-day course, attendees will confidently understand how SQL joins work and why they produce the results they do. Attendees will be equipped to select the appropriate join for virtually any data integration task, interpret join results with confidence, debug problematic queries, and write production-quality PROC SQL programs that are both accurate and efficient. Whether you are new to SQL joins or looking to strengthen your PROC SQL skills, this tutorial provides a clear, visual, and practical roadmap to mastering one of the most essential techniques in data management and analytics.
Introductory SAS Visual Analytics: Concepts, Reports, and Interactive Dashboards
James Blum
Sunday, November 15, 2026, 8:00 AM - 12:00 PM
SAS Visual Analytics provides an accessible environment for exploring data, creating visual summaries, and building interactive reports and dashboards. This course introduces essential concepts and practical skills for new users who want to move from static tables and graphs toward dynamic, shareable analytics products. The emphasis is on developing effective visual reports that support exploration, communication, and decision-making.
Topics include navigating the SAS Visual Analytics interface, accessing and preparing data for reporting, creating and modifying data items, building common visualizations, applying filters, designing dashboards, working with calculated fields, and using data item customizations. Participants will also encounter tools for developing more interactive reports, including controls, parameters, display rules, hierarchies, geographic maps, and report-level filtering. Attention is given to report organization, effective layout, performance considerations, and practical techniques for making reports easier for an audience to interpret.
The course is intended for analysts, researchers, programmers, and other SAS users who want to use SAS Visual Analytics to explore data and communicate results more effectively. Examples will emphasize common reporting and dashboard tasks, with attention to both useful features and common novice pitfalls.
ODS Workshop: The Output Delivery System from Beginning to End
Jay Iyengar
Sunday, November 15, 2026, 8:00 AM - 12:00 PM
This course will give attendees an introduction to the Output Delivery System component of BASE SAS as a reporting tool. It will cover both basic and intermediate/advanced topics in ODS. Specific topics include ODS destinations, ODS Objects, ODS Statements, Formatting with Styles, ODS graphics, Creating Styles using PROC TEMPLATE, and more. Examples will be provided utilizing several SAS reporting procedures, such as PROC PRINT, PROC TABULATE, PROC REPORT and others. Demonstrations in SAS will be run to illustrate ODS concepts and applications for attendees. Attendees should have basic experience with BASE SAS programming, including SAS reporting procedures.
Time for a Quick Start to Time Series Analysis
Danny Modlin
Sunday, November 15, 2026, 1:00 PM - 5:00 PM
Never had the TIME to look into time series analysis? This is the quick start talk for you! This presentation will allow participants to start with a software agnostic introduction to time series where we will answer "What is a Time Series?" and "What are components of a Time Series?". We will then move into a discussion of the three main classes of time series models (ESM, UCM, and ARIMAX). Remaining time will allow the participant to see modeling of time series structures in both SAS 9.4 and in SAS Viya Visual Forecasting. Within SAS 9.4, emphasis will be placed on utilizing the SAS Studio tasks that will prompt the user for choices in their analysis. Time will be taken to view the associated code that is generated in SAS 9.4. This presentation will also take time to show SAS Viya Model Studio in which users can perform time series analysis in another GUI aspect. Discussion will be had concerning differences and similarities between the SAS 9.4 options and the SAS Visual Forecasting options. Do you have the TIME for this Quick Start?
Fifty-Five Functions to Supercharge your SAS Code
Joshua Horstman
Sunday, November 15, 2026, 1:00 PM - 5:00 PM
The SAS System includes an extensive collection of DATA step functions that can provide great utility and convenience for the programmer. Many of these functions are relatively new and unknown. In this half-day course, we ll look at some SAS functions that should be in every programmer s toolbox. Each function will be presented with concrete examples so you ll be able to take what you ve learned and put it to use right away. We will cover functions from a broad range of categories such as string manipulation, logic and program control, dates and times, metadata, and much more. This course is suitable for beginning SAS programmers, but even seasoned veterans will probably find something new!
Creating Custom Graphs Using SAS and R
Richann Watson
Sunday, November 15, 2026, 1:00 PM - 5:00 PM
Creating custom graphs has always been a challenge regardless of what software you are using. Most software has tools that can help you create a basic graph such as a simple bar chart, box plot, series plot or scatter plot. These basic graphs use the tools default values for things such as background color, line color, marker color and font. Although most of the graphs produced within a procedure or function are adequate for most situations, they sometimes lack those one or two extra features you need to really make your graphs stand out and impress your clients or customers. In this class, we start with the basics and build on to what we know to modify these different aspects to make a graph that is desired. We walk through several examples, showing how to achieve the desired graph using both SAS and R.
Essential SAS Macro Language: Concepts, Tools, and Tricks for Novice Macro Users
James Blum
Sunday, November 15, 2026, 1:00 PM - 5:00 PM
The SAS macro facility is one of the most powerful tools for making SAS programs more flexible, reusable, and efficient. This course introduces essential concepts, tools, and practical habits for novice macro users who want to move beyond copying and modifying code by hand. The emphasis is on understanding the macro facility as a code-generation system: macro variables, macro triggers, the word scanner, symbol tables, and the interaction between the macro processor and the SAS language processor.
Topics include creating and resolving macro variables, combining macro variables with text, using delimiters and indirect references, constructing macro variables from DATA step execution and PROC SQL, and managing large families of macro variables. Participants will learn how to define macros, pass parameters using positional and keyword methods, apply conditional logic and macro loops, and use diagnostic options such as SYMBOLGEN, MPRINT, and MLOGIC to understand and debug generated code.
The course also addresses practical development habits: building working SAS code before generalizing it, avoiding common quoting and delimiter mistakes, managing local versus global scope, storing compiled macros, using permanent macro libraries, and calling reusable code through stored macros or %include. Examples emphasize common reporting, data management, and automation tasks, with attention to both effective technique and common novice pitfalls.
Instructor Biographies
Danny Modlin
Danny Modlin has been a Training Consultant at SAS since April 2011. Before SAS, he was a teacher in middle school and high school, as well as a Teaching Assistant at the University of North Carolina at Wilmington and North Carolina State University. He holds a Bachelors of Science in Mathematics from Elon College, a Masters of Mathematics from UNCW, and a Masters of Statistics from NCSU.
James Blum
Jim Blum is a co-author of Fundamentals of Programming in SAS: A Case Studies Approach, published in 2019. Since August of 2000, he has been a Professor of Statistics at the University of North Carolina Wilmington where he has developed and taught original courses in SAS programming for the university. These courses cover topics in Base SAS, SAS/SQL, SAS/STAT, and SAS Macros. He also regularly teaches courses in regression, experimental design, categorical data analysis, and mathematical statistics; and he is a primary instructor in the Master of Data Science program at UNC Wilmington which debuted in the fall of 2017. He has experience as a consultant on data analysis projects in clinical trials, finance, public policy and government, and marine science and ecology, and is a member of the committee that designed the Clinical Trials Programming Using SAS 9.4 Certification Exam. He earned his MS in Applied Mathematics and PhD in Statistics from Oklahoma State University.
Joshua Horstman
Josh Horstman is a statistical programming consultant, trainer, and SAS Certified Advanced Programmer based in Indianapolis. With more than 28 years of hands-on SAS experience, he brings both technical depth and a genuine passion for teaching to his work. Through his firm, PharmaStat LLC, Josh supports pharmaceutical clients navigating the complexities of clinical trial programming. A frequent and enthusiastic presenter at SAS user group conferences and industry events, he is known for making complex topics accessible and engaging. Outside of work, Josh channels that same adventurous spirit into travel and hiking he and his family have explored 48 states and 32 national parks.
Jay Iyengar
Jay Iyengar is Director of Data Systems Consultants LLC. He’s a SAS consultant, trainer, and SAS Certified Advanced Programmer. He’s been an invited speaker at several SAS user group conferences (WIILSU, WCSUG, SESUG) and has presented papers and training seminars at SAS Global Forum, Pharmaceutical SAS Users Group (PharmaSUG), and other regional and local SAS User Group conferences (MWSUG, NESUG, WUSS, MISUG). He received the Best Paper Award at WUSS 2025 for the Basics and Beyond Section. He was co-leader and organizer of the Chicago SAS Users Group (WCSUG) from 2015-19. He received his bachelor's degree from Syracuse University in Public Policy and Economics, and his master's degree from the American University.
Kirk Paul Lafler
Kirk Paul Lafler is a data scientist, developer, programmer, educator, consultant, and author who teaches dozens of in-person and virtual SAS, SQL, Python, R, Analytics, Excel, and cloud-based technology courses, workshops, and seminars to users around the world. Kirk is also a lecturer and adjunct professor at San Diego State University and is a Western Users of SAS Software (WUSS) Executive Committee (EC) Board Member serving as the Open Source Advocate and Coordinator. As 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; Kirk has been selected as an Invited speaker, educator, keynote, and mentor at SAS conferences and meetings worldwide; and is the recipient of 29 “Best” contributed paper, hands-on workshop (HOW), and poster awards.
Miguel Angel Bravo
Miguel Angel Bravo is a Consultant and Data Scientist for Premier Analytics Consulting, LLC, where he develops appliechine learning systems, AI integrations, big data pipelines, and data-driven full-stack systems for research and enterprise analytics. His work spans production-ready ML workflows, containerized AI systems, open-source GIS workflows, and real-time analytics using Python, FastAPI, Docker, AWS, and modern MLOps practices. Miguel holds a Master of Science in Big Data Analytics from San Diego State University and a Bachelor of Science in Electronics, Robotics, and Mechatronics Engineering from the University of Malaga, with research experience in environmental modeling, geospatial analytics, systems architecture, and AI-driven decision support systems.
Ryan Paul Lafler
Ryan Paul Lafler is the Founder, CEO, Chief Data Scientist, and Lead Consultant at Premier Analytics Consulting, LLC, a data science consulting firm based in San Diego, California. He’s also Adjunct Faculty at San Diego State University for the Big Data Analytics Graduate Program and the Department of Mathematics and Statistics.
Ryan’s multilingual experience in Python, R, SAS, JavaScript (React.js & API frameworks), and SQL has contributed to his success as a Big Data Scientist; Consultant; Machine Learning Engineer; Statistician; and Application Developer.
He received his Master of Science in Big Data Analytics from San Diego State University in May 2023 following the successful defense and publication of his Thesis. He holds a Bachelor of Science in Statistics and minored in Quantitative Economics from San Diego State University after graduating Magna cum Laude.
His passions include Machine Learning, Deep Learning, Artificial Intelligence, statistics, web application and interactive dashboard development, data visualization, and open-source programming languages.
Richann Watson
Richann Jean Watson is an independent statistical programmer based in Ohio who loves to code and is very active in the SAS User Group community. 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. When Richann is not busy coding or volunteering in the SAS User Group community, she is spending time with her family and cute but psycho puppy, Loki, or doing some of her favorite crafts such as crocheting or sewing.