WUSS will once again be offering a full menu of optional pre- and post-conference training classes on a variety of topics taught by seasoned experts. Classes are not included in conference registration and must be added separately. Half-day classes are $200, and full-day classes are $400. Don’t miss this chance to maximize your experience at WUSS 2023!
*HOW = Hands-on Workshop. These courses will feature instructor-led exercises that can be completed in class.
Course Descriptions
Hands-on Workshop (HOW) – Python Programming Masterclass with Comparison to SAS®
Kirk Paul Lafler
Monday, October 30, 2023
8:00 AM – 5:00 PM PDT
As a general-purpose programming language used by millions of users and developers around the world, Python offers clear syntax, scalability, versatility, and powerful libraries that add tremendous value for anyone to incorporate into their skill sets. Python’s use cases include programming, analytics, data science, web development, web scraping, text processing, image recognition, game development, artificial intelligence, machine learning, and Internet of Things. What seems to be propelling Python’s dominance is that it is relatively easy to learn, consists of a large and growing user community, and is freely available as open source. This course is designed for beginners who have never used Python and/or SAS software before, as well as programmers of other languages who want to enhance their skill set and career opportunities by learning Python and/or SAS software programming techniques.
Intended Audience: Programmers, Data Analysts, Data Scientists, Statisticians, and Others wanting to learn Python and/or SAS software
Prerequisites: No previous Python / SAS programming experience required
Delivery Method: Instructor-led with code examples
Course Material: e-Course Notes (PDF format) and Python / SAS code are provided to Attendees.
Introduction to Regression Analysis
Theresa Ngo
Monday, October 30, 2023
8:00 AM – 5:00 PM PDT
This course provides an introduction to regression analysis from model building and variable screening to residual analysis. Some regression pitfalls are highlighted with solutions to resolve or minimize errors and improve model fitting and accuracy. We will begin with Multiple Linear Regression, predicting a quantitative response variable based on two or more independent variables. Then we will build a Logistic Regression model for a binary qualitative response variable. At the end of this course, students will have a good understanding of regression analysis to model quantitative and binary qualitative response variables using SAS/STAT and SAS/GRAPH.
SAS Arrays To Save the Day!
Troy Hughes
Tuesday, October 31, 2023
7:30 AM – 11:30 AM PDT
Attend and receive a FREE copy of the author’s 550-page book, SAS® Data-Driven Development: From Abstract Design to Dynamic Functionality, Second Edition, released in 2022! Students will receive the physical book at the training.
SAS arrays containerize linear, tabular, and multidimensional data into same-type data structures. Arrays can be iterated using various DO loops, and can greatly simplify software by reducing code redundancy and complexity, which in turn maximizes software readability and maintainability. In many cases, an array can be declared and used in lieu of a series of variables, such as when the OF operator is leveraged by built-in functions and subroutines. Moreover, arrays represent the only method to pass multi-element arguments to and from user-defined functions and subroutines created using PROC FCMP (aka, the SAS Function Compiler). This course summarily demonstrates array syntax and advantages in both the DATA step and PROC FCMP.
DATA step array functionality includes:
• declaration and usage (e.g., DO loop) of explicitly indexed arrays
• declaration and usage (e.g., DO OVER loop) of implicitly indexed arrays
• usage of _I_ automatic variable to increment loops
• usage of the DIM, LBOUND, and HBOUND functions to evaluate array dimensions
• declaration of _TEMPORARY_ arrays and their benefits
• use of the OF operator to reference arrays within built-in functions and subroutines
• advanced array syntax to merge data sets and perform lookup operations
PROC FCMP array functionality additionally includes:
• passing a dynamic array to a user-defined function or subroutine
• returning a dynamic array from a user-defined function or subroutine (using the OUTARGS statement)
• declaring a static array inside a user-defined function or subroutine
• usage of the READ_ARRAY function to ingest arrays into FCMP, and the WRITE_ARRAY function to convert arrays into data sets
• usage of the DYNAMIC_ARRAY built-in subroutine (and NOSYMBOLS option) to declare a dynamic array
• conversion between arrays and hash objects
• limitations (and workarounds) of arrays within PROC FCMP
Flip It, Slice It, Merge It, Splice It: Manipulating and Transforming Data Using SAS
Josh Horstman
Tuesday, October 31, 2023
7:30 AM – 11:30 AM PDT
Data are rarely received in a format and structure convenient for analysis and reporting. Data frequently requires extensive preparation to rearrange and transform it. Fortunately, SAS provides a comprehensive array of tools for manipulating data. Understanding how these tools work and when to use them is essential for getting the most value out of your data. This half-day course will provide an overview of various SAS programming techniques for combining, rearranging, transposing, and summarizing data. Topics will include DATA step merges, PROC SQL joins, PROC TRANSPOSE, PROC MEANS/SUMMARY, and more. Through real-life examples, the course shows how these various tools can be combined to accomplish complex data manipulation. Upon completion of this course, students will have a better understanding how to get from “the data they have” to “the data they want”.
Statistics for Programmers
Jim Box
Tuesday, October 31, 2023
7:30 AM – 11:30 AM PDT
Ever wonder about the how statistics behind some of the analyses you create work? Want to know what p-values really mean? Join this class for a look at the concepts of probability and statistcs used in research. This is a concepts class, so we won’t go too heavy on the math, but will cover topics like hypothesis testing and other inferential statistics
Hands-on Workshop (HOW) – Getting Started with SAS Studio: A Point-And-Click Approach to Preparing and Exploring Data
Tom Grant
Tuesday, October 31, 2023
7:30 AM – 11:30 AM PDT
This hands-on workshop shows how one can use the menu driven tasks and SAS code in SAS Studio to perform common reporting and research tasks, including querying, reporting, and analyzing data. SAS Studio provides a point-and-click, graphical user interface, as well as predefined code that helps you exploit the power of SAS. In this workshop you will learn to access your data, combine tables, compute new variables, and explore data with simple statistics and graphs.
Hands-on Workshop (HOW) – Commit early, commit often! A gentle introduction to the joy of Git and GitHub!
Isaiah Lankham, Matthew Slaughter
Thursday, November 2, 2023
1:30 PM – 5:30 PM PDT
In this hands-on workshop, we’ll introduce you to the joy of Git and GitHub for managing codebases of any size, whether working alone or as part of a team.
In recent years, the social coding platform GitHub has become synonymous with open-source software development, with many developers also publishing their code as a form of résumé. Behind the scenes, GitHub uses software called Git, which was originally developed as a distributed version control system for managing contributions of thousands of developers to the Linux kernel.
Collaborating together, we’ll practice using the GitHub website and Git from the command line. Topics will include basic Git/GitHub concepts like forking, cloning, and branching, as well as best practices for maintaining a well-organized history of code changes. We’ll also use the GitHub web interface for pull requests, which are the standard mechanism for contributing to open-source projects, and we’ll ensure every participant leaves this workshop with (a) a fully setup GitHub account and (b) at least one open-source contribution.
No knowledge of Git or GitHub will be assumed, and no software will need to be installed. In order to work through interactive examples, accounts will be needed for GitHub and Google. Complete setup steps will be provided at https://github.com/saspy-bffs/wuss-2023-class
Automate your Business Processes with SAS Macros
Michael Aleman
Thursday, November 2, 2023
1:30 PM – 5:30 PM PDT
The course will be presented to leverage SAS to enhance your existing data ecosystem to automate key reports and processes to stakeholders, automate alerts when processes are run and completed, and creating automated programs that require low to no maintenance using SAS Macros. The course will go over aspects regarding the identification of processes and/or reports that are candidates for automation and benefits of maximizing your outputs. Inputs of data, internal or external (e.g. Oracle, IBM, etc.) that can be transformed into macros such as lists, table transformations, and parameters in the where clause will also be reviewed
Mastering the Machine Learning Toolkit to Power Your Classification and Regression Needs
Ryan Lafler
Thursday, November 2, 2023
1:30 PM – 5:30 PM PDT
The rise of Big Data has led to the rapid development of new statistical and machine learning models introduced in Python and SAS. Suitable for all data scientists interested in developing models using Python and SAS, this course empowers them to choose, fine-tune, optimize, and deploy powerful models that are tailored to their organization’s needs. Topics include minimizing the bias-variance tradeoff associated with choosing the right model, statistical inference vs. predictive power, generalizing models to predict beyond their training dataset, balancing model complexity with interpretability, and providing examples of each model programmed in Python and SAS. Generalized linear models, ensemble learning methods, gradient boosting, support vector machines, and multi-layered neural networks are fully developed and showcased for classification and regression applications. Code examples for each model are shown using SAS/STAT High-Performance (HP) Procedures and popular Python packages including scikit-learn, Statsmodels, and TensorFlow.
