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MWSUG 2024 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 Saturday and Sunday prior to the conference.
Any of our courses are open to any person who wants to take them. 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 06-Nov-2024
Course Fees
Half-Day / Full-Day$200 / $400 with MWSUG 2024 Conference registration
$300 / $600 without conference registration
Course Schedule
Click on the course title for a short description. Click on the instructor name(s) for biographical information.
Saturday, November 16, 2024
| Course Title (click for description) | Instructor(s) (click for bio) | Time |
| From %MACRO to %MEND: Getting Started with SAS Macro Language Basics | Josh Horstman | 8:00 AM - 5:00 PM |
| Programming by Example: Applied Statistical Modeling for Hypothesis Testing Using R | Ryan Paul Lafler | 8:00 AM - 12:00 PM |
| Joinless Join: The Impossible Dream Come True Using SAS® Enterprise Guide®, PROC SQL, and DATA Step | Kent Phelps | 8:00 AM - 12:00 PM |
| Mastering the Machine Learning (ML) Toolkit: A Python Workshop for Training, Optimizing, and Deploying Supervised ML Algorithms | Ryan Paul Lafler | 1:00 PM - 5:00 PM |
| Create Charts and Plots That Are Quickly Easily Understood | LeRoy Bessler | 1:00 PM - 5:00 PM |
Sunday, November 17, 2024
| Course Title (click for description) | Instructor(s) (click for bio) | Time |
| Regression Expression! 24 Regression Methods in SAS | David Corliss | 8:00 AM - 5:00 PM |
| PROC SQL Programming Masterclass Using SAS® | Kirk Paul Lafler | 8:00 AM - 12:00 PM |
| Advanced DATA Step Programming Techniques | Josh Horstman | 8:00 AM - 12:00 PM |
| Working with Medicare and Medicaid administrative data sets using BASE SAS programming | Jay Iyengar | 1:00 PM - 5:00 PM |
| SAS vs Python : To Bee or Not to Bee? | Charu Shankar | 1:00 PM - 5:00 PM |
Course Descriptions
From %MACRO to %MEND: Getting Started with SAS Macro Language BasicsJosh Horstman
Saturday, November 16, 2024, 8:00 AM - 5:00 PM
This full-day seminar is designed for the SAS programmer who is new to the Macro Language. We will start with the basics and cover the fundamentals necessary to start applying SAS macros in your programs. By the end of the course you will understand how the Macro Language works, what the Macro Symbol Table is and how to store values in it, how the SAS System uses Macro Variables, key Macro Language concepts, important SAS Macro Language statements, and how to invoke Macros in your programs. The examples shown in the course materials demonstrate the power and flexibility of this part of the SAS System and will enable you to apply its functionalities to your own programs right away.
Programming by Example: Applied Statistical Modeling for Hypothesis Testing Using R
Ryan Paul Lafler
Saturday, November 16, 2024, 8:00 AM - 12:00 PM
This half-day R programming workshop is open to all data scientists, decision scientists, statisticians, researchers, students, educators, clinical trials associates, and programmers interested in conducting applied statistical analysis using the R programming language to answer fundamental questions about populations of interest through parametric and nonparametric statistical models.
Attendees will gain valuable skills processing, cleaning, visualizing, and generating descriptive statistics for datasets using the well-supported `tidyverse` ecosystem of packages in R. Statistical models including t-tests; One-Way ANOVA (Analysis of Variance); and Factorial ANOVA are developed in R, including a thorough discussion of each model’s assumptions, use-cases, output, and limitations. Frequently used nonparametric models including Mann-Whitney’s U; Wilcoxon’s Signed-Rank; and Kruskal-Wallis are likewise investigated and developed in R.
In addition to these statistical models, attendees are given a rigorous overview of applied statistical analysis that includes parametric and nonparametric statistical models; causation vs. correlation; power analysis; effect sizes; practical vs. statistical significance; error probabilities and analysis mistakes; testing for interaction effects; reporting results for submissions; and the importance of post-hoc analysis when finding statistically significant results.
By enrolling in this workshop, attendees receive the PDF-slides, interactive R notebooks containing the documented R code, and the confidence to successfully develop and interpret statistical models using the R programming language in their organizations.
Joinless Join: The Impossible Dream Come True Using SAS® Enterprise Guide®, PROC SQL, and DATA Step
Kent Phelps
Saturday, November 16, 2024, 8:00 AM - 12:00 PM
SAS® Enterprise Guide® and Base SAS® can easily combine data from tables or data sets by using a PROC SQL Join to match on like columns or by using a DATA Step Merge to match on the same variable name. However, what do you do when tables or data sets do not contain like columns or the same variable name and a Join or Merge cannot be used? You are invited to attend this exciting half-day training course on the Joinless Join where you will be empowered to expand the power of SAS Enterprise Guide and Base SAS in new ways by creatively overcoming the limits of a standard Join or Merge.
You will learn how to design a Joinless Join based upon dependencies, indirect relationships, or no relationships at all between the tables or data sets using SAS Enterprise Guide and Base SAS PROC SQL and DATA Step. In addition, you will learn how to use a Joinless Join to prepare unrelated joinless data to be utilized by ODS and PROC REPORT in creating a PDF. Come experience the power and versatility of the Joinless Join to greatly expand your data transformation and analysis toolkit. Welcome to the surprising paradox of the Joinless Join.
Mastering the Machine Learning (ML) Toolkit: A Python Workshop for Training, Optimizing, and Deploying Supervised ML Algorithms
Ryan Paul Lafler
Saturday, November 16, 2024, 1:00 PM - 5:00 PM
This half-day workshop is open to all data scientists, statisticians, programmers, machine learning engineers, students, and researchers interested in training, optimizing, and fine-tuning supervised machine learning (ML) algorithms for regression and classification tasks using Python. Whether you’re a Python programmer or coming from SAS, R, or some other programming language, this hands-on Python workshop will give all attendees the confidence to leverage Python’s open-source libraries for supervised machine learning. By enrolling in this course, attendees will receive their personal copies of the PDF-slides, the Python code organized inside of an interactive (and documented) Jupyter Notebook file, and the skills to train, fine-tune, and deploy supervised ML algorithms tailored to their organization’s predictive needs.
Starting simple and then incrementally building towards more advanced machine learning algorithms, attendees will train a wide assortment of supervised ML algorithms; mitigate overfitting and underfitting; evaluate algorithm performance; and effectively interpret any algorithm’s results. All algorithms are trained in Python using the Scikit-Learn library, giving attendees an application-oriented approach to training algorithms on actual data, including discussions about the advantages of using certain algorithms over others and the model complexity vs. interpretability tradeoff. A comparison between statistical modeling and machine learning, including their similarities and differences, will be shown and explained as well. Attendees will learn essential data cleaning techniques; perform exploratory data analysis (EDA) to visualize and understand relationships between features in their data; and engineer advanced Scikit-Learn pipelines that automate data processing workflows when training ML algorithms for regression and classification tasks.
This Python workshop trains and fine-tunes supervised ML algorithms including statistical OLS regression models; penalized regression models (LASSO regression); decision trees for classification; random forest ensembles for classification; and gradient-boosted ensembles for classification. Topics include statistical modeling; data engineering; hyperparameter fine-tuning; feature selection; optimizing algorithms for better predictions on unseen data; evaluating predictive performance; and employing Python libraries like Scikit-Learn, Statsmodels, Pandas, NumPy, SciPy, Matplotlib, and Seaborn to efficiently process, visualize, clean, train, fine-tune, interpret, and deploy supervised ML algorithms for data-driven AI workflows.
Create Charts and Plots That Are Quickly Easily Understood
LeRoy Bessler
Saturday, November 16, 2024, 1:00 PM - 5:00 PM
Learn best ways to get beyond the defaults, for wise design with SAS® ODS Graphics, THE Graphics Superpower Tool. Not just How To, but What To. ODS Graphics comes with SAS at no added charge. Put its super value to work for your company, your client, or yourself. No prior experience is needed for this course. Visuals are for quick easy inference, but precise numbers are needed for correct reliable inference. Learn all the ways to deliver precise numbers. Free the viewer from estimating where bar end or plot point lands on the axis (axes). Provide certainty, not guesses. That’s only one of the key design principles. Color misuse is a common obstacle to visual communication. Learn how my graphic and color design principles are implemented with widely applicable examples. Learn from what I learned, discovered, and invented over 44 years of effort to get the best out of SAS graphics. See Time Series Plots and Trend Lines, simple or overlaid, including use of uncommon, but powerful, design concepts, for when the familiar and customary is not enough. Categorical Data has so many ways to show itself, and with designs to make it show itself better. For One Categorical Variable: Bar Chart; Dot Plot as horizontal bar chart alternative; Needle Plot as vertical bar chart alternative; Pie Chart, and Donut Chart as a hole-in-the-middle alternative; Text Chart as a surprising but equally communication-effective bar chart alternative. For Two Categorical Variables: Stacked, Clustered, or Overlaid versions of bar charts, dot plots, and needle plots; Butterfly Chart, the bi-directional horizontal bar chart for when the second categorical variable has only two values; Line Chart as on-image second categorical variable companion to a bar chart; Bubble Plot; Heat Map with a Discrete Legend or an Actually USABLE Gradient Legend. ODS Graphics lets you package composites of different graphic views of the same data, or similar graphic views of related data, using PROC SGPANEL, PROC SGSCATTER, or ODS LAYOUT. This is a course for which there is no substitute. It can take you from no experience to proficient at creating charts and plots for quick easy understanding—instant, immediate, unambiguous communication. Resources will be suggested where the student can find additional and more advanced possibilities and capabilities than can be covered in the half-day course.
Regression Expression! 24 Regression Methods in SAS
David Corliss
Sunday, November 17, 2024, 8:00 AM - 5:00 PM
With so many regression procedures available for different situations, it can be difficult to know the breadth of available methods and how to select the ones to apply to a given problem. This course offers an overview of 24 regression-based methods. A decision flowchart is provided to assist in selecting the most useful regression procedures for a given context. The course is practical and example driven, emphasizing which procedures to consider and how to apply them in real situations. A quick introduction to each method followed by two worked examples, with discussion of use cases, options in the SAS procedures, and producing graphical output. The course begins with a basic overview of linear regression, progressing to more advanced techniques. Course modules include basic regression, procedures for specific data issues and needs (e.g., robust regression for outliers), special model types (e.g., quantile regression), logistic regression methods, and mixed, non-linear, and non-parametric SAS procedures. This course will help discern which statistical methods should be considered in a given situation and provide details with source code and examples for using specific procedures.
PROC SQL Programming Masterclass Using SAS®
Kirk Paul Lafler
Sunday, November 17, 2024, 8:00 AM - 12:00 PM
PROC SQL Programming Masterclass Using SAS® provides attendees with core concepts, features, and coding techniques on how to effectively use PROC SQL. Attendees learn how to use PROC SQL to access and retrieve data in SAS datasets (tables); essential programming tasks including specifying SELECT statement clauses (i.e., FROM, INTO, WHERE, ON, GROUP BY, HAVING, ORDER BY); execution order of the SELECT clauses; subsetting data with WHERE, ON and HAVING, ordering data and results with ORDER BY, and grouping data with GROUP BY and HAVING; constructing logic scenarios using case expressions; exploring one-to-one, one-to-many, and many-to-many data relationships; creating inner and outer join constructs (i.e., inner and outer - left, right, and full) as well as the application of set operators to combine two or more tables together; using summary (statistical) functions to aggregate data; creating new tables using three different approaches; interfacing PROC SQL and the macro facility to create single-value (aggregate) and multi-value (list) macro variables; the strategies and techniques related to the design and implementation of simple and composite indexes; the application of query debugging techniques to help detect coding errors, warnings, and other issues; and the application of efficient SQL queries (scaling) for improved performance.
Advanced DATA Step Programming Techniques
Josh Horstman
Sunday, November 17, 2024, 8:00 AM - 12:00 PM
To solve complex coding problems with the SAS® DATA step, one must go beyond a basic understanding of the individual statements. You need to understand how the various statements interact with each other and how their options can be leveraged to build DATA step code that provides innovative solutions to the toughest of problems. Based on Art Carpenter’s book, Carpenter’s Guide to Innovative SAS® Techniques, this class is a must for the DATA step programmer who wants to take his or her programs to the ‘next’ level. Topics include working across multiple observations using look-ahead and Look-back techniques, employing the DOW loop, taking advantage of double SET statements, working with hash objects, performing table lookups, using arrays to transpose data from columns to rows and back again, evaluating complex expressions, applying data set options, adopting new DATA step functions (and old function with new options), and more. This course is designed to be taken by a student who has a basic understanding of the DATA step and its primary statements. The material will focus on advanced topics that will give the student a deeper understanding of the operation of the DATA step. Through examples, students will be exposed to innovative techniques for solving difficult programming problems.
Working with Medicare and Medicaid administrative data sets using BASE SAS programming
Jay Iyengar
Sunday, November 17, 2024, 1:00 PM - 5:00 PM
This training seminar will give attendees an overview of the Medicare and Medicaid programs and a detailed explanation of the different types of Medicare/Medicaid data sets, and the SAS programming constructs to work with them. This includes different data repositories used to access Medicare/Medicaid claims such as the VRDC (Virtual Research Data Center). In addition, attendees will receive a background and in-depth explanation of the Medicare and Medicaid Federal programs. The course features demonstrations using SAS to perform analytic and reporting tasks with healthcare data sets. The course is geared towards SAS programmers and data analysts working for government contractors or research institutions who utilize Medicare and/or Medicaid data.
SAS vs Python : To Bee or Not to Bee?
Charu Shankar
Sunday, November 17, 2024, 1:00 PM - 5:00 PM
Ready to dive into the sweetest data science session ? Join us for a first time ever exclusive to MWSUG To Be or Not to Bee: SAS vs. Python with SASsy instructor Charu Shankar!
Whether you're a seasoned coder or just getting started, this hands-on seminar is your chance to learn the ropes of SAS and Python while working with some seriously buzz-worthy data—bumblebee data!
You’ll learn cutting-edge techniques to analyze and visualize data. Plus, you’ll get to see how SAS and Python stack up in the ultimate data showdown. Will it be the structured reliability of SAS or the flexible power of Python that wins your heart? Only one way to find out!
Why You Should Attend:
- Hands-On Learning: Get practical experience with SAS and Python.
- Fun & Engaging: Work with cute bumblebee datasets that make learning feel like play.
- Expert Guidance: Charu is a data wiz who makes even the toughest concepts a breeze.
- Community Vibes: Connect with fellow data enthusiasts and share your passion.
- Innovative New Interface – Get your hands to your very own personal SAS Viya workbench environment to practice
Instructor Biographies
Charu Shankar
Charu Shankar, a SAS instructor with a background in computer systems management, engages with logic, visuals, and analogies to spark critical thinking. Prior to joining SAS, Charu served in the United Nations managing educational projects and taught computer and natural languages at Rotman School of Management, University of Toronto. At SAS, Charu curates and delivers unique content on SAS, SQL, Python, Viya, etc. via the SAS YouTube channel, SAS global forum, SAS Ask the Expert Series, SAS Training Post, etc. When not coding, Charu teaches yoga and loves to explore Canadian trails with her husky Miko.
Josh Horstman
Joshua Horstman is an independent statistical programming consultant and trainer based in Indianapolis with over 25 years of experience using SAS, primarily in the life sciences industry. Josh is a SAS Certified Advanced Programmer who loves coding and presenting at SAS user group conferences and other industry events. Josh also enjoys travelling and hiking with his family and has been to 47 states and 28 national parks.
Jay Iyengar
Jay Iyengar is Director of Data Systems Consultants LLC. He is 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 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 consultant, developer, programmer, educator, and data scientist; and teaches SAS Programming and Data Management in the Statistics Department at San Diego State University. Kirk also provides project-based consulting and programming services to client organizations in a variety of industries including healthcare, life sciences, and business; and teaches “virtual” and “live” SAS, SQL, Python, Database Management Systems (DBMS) technologies (e.g., Oracle, SQL-Server, Teradata, MySQL, MongoDB, PostgreSQL, AWS), Excel, R, cloud-based technologies as well as other software and tools. Currently, Kirk serves as the Western Users of SAS Software (WUSS) Executive Committee (EC) Open-Source Advocate and Coordinator and is actively involved with several proprietary and open-source software, DBMS, machine learning, cloud-computing user groups and conference committees. Kirk is the author of several books including the popular PROC SQL: Beyond the Basics Using SAS, Third Edition (SAS Press. 2019), along with other technical books and publications. He is also an Invited speaker, educator, keynote, and leader; and is the recipient of 29 “Best” contributed paper, hands-on workshop (HOW), and poster awards.
Kent Phelps
Kent and Ronda Team Phelps are The SASketeers: All for SAS and SAS for All! They founded Illuminator Coaching, Inc., as a platform to share the ever-expanding wonders of SAS®. Their background includes 7 Presentations and 4 Hands-On Workshops (Best HOW 2019) at MidWest SAS® Users Group Regional Conferences; an Advanced Presentation and a Presentation Video at SAS® Global Forums; and a Livestream Presentation at the Southeast SAS® Users Group Regional Conference. Kent is a Senior Data, Technical, Business Analyst, Developer, Engineer, Programmer, Trainer, Coach, and Consultant and a SAS® Certified Professional Programmer who has happily programmed in SAS® since 2007. He endeavors to coach, encourage, and equip you to fulfill your life, career, and leadership potential as you build an enduring legacy of inspiration, excellence, and honor. Ronda is a Writer and Coach who previously served in the Banking and Insurance industries. She believes that YOU are a gift the world is waiting to receive and endeavors to coach, encourage, and equip you to pursue your unique destiny as you navigate your life journey with intentionality, fulfilling purpose, and enduring hope.
LeRoy Bessler
LeRoy Bessler PhD is a data artist, the world’s longest-serving advisor to SAS users on best practices for graphics design and color use, and author of Visual Data Insights Using SAS ODS Graphics: A Guide to Communication-Effective Data Visualization. His principles explained and demonstrated in the book are useful for any graphics software. As a data analyst and SAS software expert, his distinguishing non-graphics expertise with SAS is software-intelligent application development for reliability, reusability, maintainability, extendibility, and flexibility—to deliver Strong Smart 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.