WUSS 2022 Classes

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 2022!

Course Title (click for description) Instructor(s)
(click for bio)
Time
Tuesday, Sept. 13, 2022 – FULL DAY
From %MACRO to %MEND: Getting Started with SAS Macro Language Basics Josh Horstman 8:00 AM – 5:00 PM
Introduction to Statistical Analysis Using R Software Lida Gharibvand 8:00 AM – 5:00 PM
Tuesday, Sept. 13, 2022 – HALF DAY MORNING
CDISC ADaM – Implementation by Example (HOW) Richann Watson 8:00 AM – 12:00 PM
Python Programming Techniques by Example for SAS® Users (HOW) Kirk Lafler 8:00 AM – 12:00 PM
10 SAS Enterprise Guide Tips for Experienced SAS programmers Charu Shankar 8:00 AM – 12:00 PM
Hands-On Data-Driven Design: Developing More Flexible, Reusable, Configurable SAS Software (HOW) Troy Hughes 8:00 AM – 12:00 PM
Tuesday, Sept. 13, 2022 – HALF DAY AFTERNOON
CDISC ADaM – Principles, Rules and Complex Examples (HOW) Richann Watson 1:00 PM – 5:00 PM
PROC SQL Programming: Beyond the Basics Using SAS® (HOW) Kirk Lafler 1:00 PM – 5:00 PM
Git Introduction (HOW) Zeke Torres 1:00 PM – 5:00 PM
What’s black and white and sheds all over? The Python Pandas DataFrame, the Open-Source Data Structure Supplanting the SAS Data Set (HOW) Troy Hughes 1:00 PM – 5:00 PM
Wednesday, Sept. 14, 2022 – HALF DAY MORNING
Data Cleaning 101 Ron Cody 7:30 AM – 11:30 AM
Forecasting Methods: Regression, Time Series, and Neural Networks Theresa Ngo 7:30 AM – 11:30 AM
Working with Administrative Healthcare Data Sets Using BASE SAS Programming Jay Iyengar 7:30 AM – 11:30 AM
Getting Off the Ground with R (HOW) Kelly Bodwin 7:30 AM – 11:30 AM
ODS Graphics I: Creating Quick and Easy Graphs with the Statistical Graphics (SG) Procedures Josh Horstman 7:30 AM – 11:30 AM
Favorite FunKey Functions: Functions for Your Programming Toolbox Richann Watson 7:30 AM – 11:30 AM
Friday, Sept. 16, 2022 – HALF DAY AFTERNOON
Automate your Business Processes with SAS Macros Michael Aleman 1:30 PM – 5:30 PM
Custom Excel Reports and Spreadsheets Using PROC REPORT and the ODS Excel Destination Kirk Lafler 1:30 PM – 5:30 PM
SAS: Untangling the World Wide Web Mark Jordan 1:30 PM – 5:30 PM
Hands-On Functions: How to Build Your Own User-Defined FCMP Functions and Macro Functions (HOW) Troy Hughes 1:30 PM – 5:30 PM
Everything is Better with Friends: Using SAS in Python Applications with SASPy and Open-Source Tooling (Beyond the Basics) (HOW) Isaiah Lankham
& Matthew Slaughter
1:30 PM – 5:30 PM
Statistical Modeling in R with Tidymodels (HOW) Kelly Bodwin 1:30 PM – 5:30 PM



Course Descriptions

From %MACRO to %MEND: Getting Started with SAS Macro Language Basics
Josh Horstman
Tuesday, September 13, 2022
8:00 AM – 5:00 PM PDT

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.

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Introduction to Statistical Analysis Using R Software
Lida Gharibvand
Tuesday, September 13, 2022
8:00 AM – 5:00 PM PDT

The objective of this course is for the beginner and intermediate students to develop an overall understanding of how to use R software to analyze data in real-world applications. The students will first be introduced to statistics and critical thinking through foundational concepts such as types of data, quantitative and qualitative data with tables and graphs, normal distribution, Central Limit Theorem, sampling distribution and its use. Then, students will learn how to define a confidence interval, perform hypothesis testing, understand the difference between null and alternative hypotheses, Type I and Type II error, statistical significance, and clinical importance. Next topic will be the difference between the level of significance of a test and a p-value followed by how to use a p-value or critical value to determine statistical significance. The students will learn the important skill of how to use a confidence interval to determine statistical significance. The class will also provide practical knowledge so students can determine when to use a paired t-test and when to use an independent t-test or one way ANOVA using R. The course will help students to define statistical power and learn how power and sample size are related. Finally, the course will discuss correlation and regression analysis using R software. This course aims to equip beginner and intermediate students with practical knowledge of R software to analyze real-world datasets and use the power of data to improve the decision process.

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CDISC ADaM – Implementation by Example (HOW)
Richann Watson
Tuesday, September 13, 2022
8:00 AM – 12:00 PM PDT

This course will provide a high-level overview of some of basic ADaM concepts. It is assumed that the attendee will have a fundamental knowledge of the different ADaM structures and principles. The primary focus of the course is to illustrate the implementation of some of the concepts found in both the ADaM Implementation Guide (ADaM IG) and the ADaM Structure for Occurrence Data (OCCDS) documents. Items covered include setting up subject level analysis data set (ADSL) for common trial designs and walking through the process of creating a basic data structure (BDS) starting from a simple BDS and building on it to structure a data set that will support one or more analyses. Additionally, the course will demonstrate how to implement some variables that are only found in OCCDS, such as the standardized MedDRA query (SMQ) and customized query (CQ) variables.

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Python Programming Techniques by Example for SAS® Users (HOW)
Kirk Lafler
Tuesday, September 13, 2022
8:00 AM – 12:00 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 a vast collection of libraries that add a tremendous amount of features. Python’s use cases include analytics, data science, web development, game development, web scraping, text processing, image recognition, artificial intelligence, machine learning, and Internet of Things. What seems to be propelling Python’s dominance is that its relatively easy to learn, consists of a large community of users, and is freely available as open source. Attendees learn valuable programming techniques through the application of real-world examples. Topics include data access, data cleaning, chaining of comparison operators, functions as powerful building blocks, positional and keyword arguments, object oriented programming with classes, built-in data structures using lists and iterables, identify the most frequent value in a list, reverse lists, collect unordered key-value pairs as a dictionary, collect unordered distinct hashable objects as a set, collect immutable values as a tuple, sort data, append / concatenate data, and merge / join data.

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10 SAS Enterprise Guide Tips for Experienced SAS programmers
Charu Shankar
Tuesday, September 13, 2022
8:00 AM – 12:00 PM PDT

Come check out 10 of the many reasons to seriously consider using SAS Enterprise Guide if you are a programmer. Discover how to enhance your productivity by using SAS Enterprise Guide EG to perform standard tasks.
The 10 Tips:
1. Learn about the powerful data explorer
2. Leverage the AUTOEXEC process flow
3. Visually split the screen to see more than one activity
4. Learn to submit code at session start up automatically- big use for library assignment
5. Utilize tools like format code to get messy code nicely indented
6. Hide wrapper code in the log
7. Learn to easily spot errors in the log
8. Slice and dice your data with the summary tables task
9. Check out the program analyzer tool to get a cool GUI out of your EG points and clicks
10. Use the built in AI in EG to help you write SAS code.

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Hands-On Data-Driven Design: Developing More Flexible, Reusable, Configurable SAS Software (HOW)
Troy Hughes
Tuesday, September 13, 2022
8:00 AM – 12:00 PM PDT

Attend and receive a FREE physical copy of the author’s 550-page book, SAS® Data-Driven Development: From Abstract Design to Dynamic Functionality, Second Edition, released in 2022! Students can optionally run all programs live during the training. This HANDS-ON workshop installs the student as the new SAS consultant within Scranton, Pennsylvania’s most infamous paper supply company — charged with improving software functionality and performance through data-driven software design. Navigate office intrigue and antics to gather software requirements, analyze hardcoded legacy SAS programs, and refactor (improve) software to help Jim, Dwight, Phyllis, and Stanley sell more paper through higher quality data-driven software! Data-driven design describes software in which configuration items, business rules, data validation rules, data models, data dictionaries, report style, and other dynamic elements are maintained in external data structures — NOT in underlying code. Benefits include increased software flexibility, reusability, maintainability, modularity, readability, interoperability, extensibility, and configurability. Topics include: Build reusable procedures, functions, and subroutines (CALL routines) using SAS macros and PROC FCMP. Introduce data structures (parameters, macro lists, arrays, hash objects, control tables, configuration files, data sets, Excel spreadsheets, CSV files, and CSS files). Use SAS components (CALL EXECUTE, CNTLIN option in PROC FORMAT, SYSPARM option, SAS dictionary tables, and CSSSTYLE option in PROC REPORT). Create color-coded, “traffic light” reports that identify bad data while standardizing good data. Configure the format, font, color scheme, graphics of reports using SAS formats and CSS files. Understand how master data management (MDM) supports interoperability, by creating data structures leveraged by SAS and Python simultaneously.

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CDISC ADaM – Principles, Rules and Complex Examples (HOW)
Richann Watson
Tuesday, September 13, 2022
1:00 PM – 5:00 PM PDT

This course will provide a high-level overview of some of the basic ADaM concepts; however, it is assumed that the attendee will be familiar with the different ADaM structures and principles. The course will delve into what is meant by traceability and analysis ready as well as look at some rules and best practices. However, the primary focus is to illustrate the implementation of some of the more difficult or less common concepts found in both the ADaM Implementation Guide (ADaMIG) and the ADaM Structure for Occurrence Data (OCCDS) documents. The course includes an illustration of the use of criterion variables (CRITy and MCRITy) and record-level and parameter-level population flags (-RFL and -PFL), as well as a demonstration of how to set up time-to-event and questionnaire/rating/scales analysis data sets. In addition, it will go into depth about AEs of special interest and the use of Standard MedDRA Queries (SMQ) and provide an illustration of how the OCCDS can be used to handle the non-typical analysis for events data.

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PROC SQL Programming: Beyond the Basics Using SAS® (HOW)
Kirk Lafler
Tuesday, September 13, 2022
1:00 PM – 5:00 PM PDT

PROC SQL Programming: Beyond the Basics Using SAS® provides SAS® and SQL users with core concepts, features and techniques on how to effectively use PROC SQL. Attendees learn how to use PROC SQL to access data in SAS datasets (tables); review essential programming tasks including retrieving, execution order of the SELECT clauses, subsetting, ordering, and grouping data; construct logic scenarios with case expressions; explore one-to-one, one-to-many, and many-to-many data relationships; understand the similarities and differences between DATA step merges and joins; create inner and outer join constructs as well as apply set operators to combine two or more tables together; use summary (statistical) functions to aggregate data; create new tables using three different approaches; interface PROC SQL and the macro facility to create single-value and multi-value (list) macro variables; apply a number of query debugging techniques to help detect coding errors, warnings and other messages; and scale SQL queries for improved performance.

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Git Introduction (HOW)
Zeke Torres
Tuesday, September 13, 2022
1:00 PM – 5:00 PM PDT

This intro gives a SAS user the chance to start working smarter and more efficient with GIT. Covers the installation, basics and operation of GIT and local repositories. As well as remote repositories the user will likely start to encounter. There is also brief mention and overview of team related GIT topics which are covered in the GIT Team course. A user will find this course essential as they create SAS (or any code) especially as the code is shared and/or maintained by more than one person. The examples of best practices and suggested “concepts” come from a SAS code point of view which embraces a “team” and collaborative code ecosystem. Regardless of which software (SAS, Python, R, others) or which interface (SAS EG, GitHub Desktop, etc) this course centers around the “USER” and the benefits to the User from GIT regardless if the rest of the team is using GIT or not. Our typical user will see improvement in “code” and the documentation of the “code” with GIT. Solving issues with “why did i change that?”. The use of GIT will improve the testing and development of code and iterative work. The user will also see how the practices of GIT reduces code inaccuracy because we will be ‘reviewing’ our code in our revision of our overall workflow. We will take our “code” and migrate it into a new GIT repository and start to use GIT to manage our changes and our code. Examples and use cases that a SAS user commonly finds.

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What’s black and white and sheds all over? The Python Pandas DataFrame, the Open-Source Data Structure Supplanting the SAS Data Set (HOW)
Troy Hughes
Tuesday, September 13, 2022
1:00 PM – 5:00 PM 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. Tired of paying for your SAS license and curious about exploring the most popular, freely downloadable open-source data analytic software?! This course will introduce the Python “Pandas” library, which is the predominant data manipulation module within the Python language, and the “DataFrame” data structure. Pandas is an open-source library, and a core component of the popular Anaconda software — the most widely utilized Python distribution supporting data analytics and data science. All examples will first demonstrate Base SAS syntax (including use of SAS macros), after which a functionally equivalent Python solution will be demonstrated and fully discussed. All Python examples leverage the latest software releases, including Python 3.10.0 and Pandas 1.4.1. No previous Python experience is required to attend! Pandas DataFrame topics include: Mathematical operators and logical operators. Sorting columns (akin to PROC SORT) and values (akin to CALL SORT). Evaluating categorical frequency (akin to PROC FREQ). Mathematical functions (akin to MIN, MAX, FLOOR, CEIL, ROUND, RAND, SUM, MEAN). Character functions (akin to STRIP, SUBSTR, CATX, PUT, FIND, INDEX, LENGTH). Creating a user-defined function (akin to PROC FCMP). Mapping data to clean and categorize (akin to PROC FORMAT). Data validation through lookup tables (hash objects). SASPy library topics include: Importing a SAS data set into a Pandas DataFrame. Exporting a Pandas DataFrame to a SAS data set.

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Data Cleaning 101
Ron Cody
Wednesday, September 14, 2022
7:30 AM – 11:30 AM PDT

This half-day workshop discusses ways to look for data errors using both DATA step programming and Base SAS procedures. We start with programs and procedures to detect errors in character data. Functions such as NOTDIGIT and NOTALPHA are especially useful in this regard. For numeric data, we investigate methods for detecting data points outside of fixed ranges and continue on to develop programs for automatic outlier detection.This workshop does much more than just showing you techniques for detecting and fixing data errors—it’s also aims to make you a better SAS programmer. Whether you are new to SAS programming or are a veteran programmer, you will take valuable programming tips and tricks away with you. Although this course does not require you to know the SAS macro language, you will be taught how to run macros that perform a variety of data cleaning functions. For example, one macro, called AUTO_OUTLIERS, checks for possible data errors in numeric data, using a concept called “trimmed statistics.” The workshop ends with a demonstration of integrity constraints and audit trails. They allow you to define rules or constraints on one or more data values. For example, you may require that values for Gender must be M’s or F’s. As another example, you may define valid ranges for each of your numeric variables. All of these constraints are stored in the data descriptor portion of your SAS data set. Once these constraints are in place, they can prevent new data that violates one or more constraints, from being added to your data set. Most of the material for this workshop is based on the two-day course, Data Cleaning Techniques, offered by SAS Institute.

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Forecasting Methods: Regression, Time Series, and Neural Networks
Theresa Ngo
Wednesday, September 14, 2022
7:30 AM – 11:30 AM PDT

This course teaches students to analyze and model univariate time series data, evaluate forecast models for goodness of fit and accuracy, and then select the best model to forecast future values. The focus is on the application of forecasting rather than the statistical theory. The course begins with an overview of time series components, data preparation, and variable screening methods. Next, the three forecasting methods are sequentially presented and demonstrated with examples using SAS/ETS, SAS/STAT, and SAS/GRAPH. First, the widely used multiple linear regression is covered for modeling a linear relationship between target and input variables. Second, the traditional Box-Jenkins method is discussed in detail from differencing to modeling time series components in an autoregressive integrated moving average (ARIMA) model. Third, a brief introduction to neural networks, which is an extension of a regression model with flexibility to model any association between target and input variables. The course concludes by summarizing the properties of the three forecasting methods.

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Working with Administrative Healthcare Data Sets Using BASE SAS Programming
Jay Iyengar
Wednesday, September 14, 2022
7:30 AM – 11:30 AM PDT

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.

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Getting Off the Ground with R (HOW)
Kelly Bodwin
Wednesday, September 14, 2022
7:30 AM – 11:30 AM PDT

Have always wanted to delve into R, but weren’t even sure where to start? Then this course may be for you. We will walk through every step of getting comfortable learning R, for students who are adept at statistical software but new to this particular language. The course will cover setting up an RStudio environment, using scripts as well as R Markdown, and common frustrations with updates and package installs in an open source world. We will also go over the basics of simple calculations in Base R and of using the tidyverse for data manipulation and visualization. The goal of this course is to get you over the hurdle of picking up something brand new, and to set you up for success in your future learning journey.

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ODS Graphics I: Creating Quick and Easy Graphs with the Statistical Graphics (SG) Procedures
Josh Horstman
Wednesday, September 14, 2022
7:30 AM – 11:30 AM PDT

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.

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Favorite FunKey Functions: Functions for Your Programming Toolbox
Richann Watson
Wednesday, September 14, 2022
7:30 AM – 11:30 AM PDT

Functions are an invaluable part of the programmer’s toolbox. While some functions are extremely popular, for good reason, there are some that could be considered hidden gems. This training will highlight less commonly used functions, such as the PRXCHANGE and PRXPARSE functions, which are essential for efficient string manipulation. Another example is the COALESCE(C) function, which can facilitate the population of missing values based on parameters, and more. This course will illustrate through examples these and more FunKey functions. Additionally, this course will explore the utility of writing functions with the FCMP procedure, which combines the reusability of macro processing with the power of functions, and learn to enhance SAS reporting with user-written style functions. The course is designed for everybody of all skill levels.

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Automate your Business Processes with SAS Macros
Michael Aleman
Friday, September 16, 2022
1:30 PM – 5:30 PM PDT

This course demonstrates how SAS Macros can enhance your existing data ecosystem by automating programs, reports and processes plus produce email alerts when processes complete. The course will cover macro techniques, identification of processes and/or reports that are candidates for automation and benefits of maximizing your outputs.  This course will also demonstrate how data inputs, internal or external (e.g., Oracle,DB2, Netezza, etc.) such as lists, table transformations, and parameters in the where clause can be transformed into macro statements.

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Custom Excel Reports and Spreadsheets Using PROC REPORT and the ODS Excel Destination
Kirk Lafler
Friday, September 16, 2022
1:30 PM – 5:30 PM PDT

SAS® users everywhere turn to the REPORT procedure to customize and satisfy their reporting needs as they create and deliver quality “custom” detail and summary reports, and specialized output for management, end users, and customers. This popular course explores an assortment of techniques to create custom spreadsheets, reports and specialized output using PROC REPORT and the powerful ODS Excel destination. Attendees learn how to create detail and summary spreadsheets, reports and output using PROC REPORT; acquire useful Output Delivery System (ODS) skills; combine PROC REPORT and the powerful ODS Excel destination to produce quick and formatted detail and summary Excel workbook results; customize output and results with SAS-supplied styles; compute subtotals and totals at the end of a report using a COMPUTE Block; calculate percentages; produce statistics for analysis variables; apply conditional logic to control summary output rows; add background images; build custom autofilter drill-down (interactive) reports and Excel workbooks; and add traffic lighting scenarios to Excel workbooks.

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SAS: Untangling the World Wide Web
Mark Jordan
Friday, September 16, 2022
1:30 PM – 5:30 PM PDT

Learn how to use SAS to acquire and manipulate data from internet sources including: 1. Process remote data files on FTP servers in place with FILENAME FTP 2. Access web pages to extract data from HTML tables 3. Follow hyperlinks to automate data acquisition from multi-layered HTML pages 4. Work with Internet APIs and extract data from XML and JSON response files 5. Extract data from delimited text, CSV, and Excel files 6. Work with compressed (ZIP) files

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Hands-On Functions: How to Build Your Own User-Defined FCMP Functions and Macro Functions (HOW)
Troy Hughes
Friday, September 16, 2022
1:30 PM – 5:30 PM PDT

Attend and receive a FREE physical copy of the author’s 550-page book, SAS® Data-Driven Development: From Abstract Design to Dynamic Functionality, Second Edition, released in 2022! “User-defined” functions (and subroutines) are created by SAS users, as contrasted with “built-in” functions, which are part of out-of-the-box SAS. This introductory course demonstrates how to build both functions and subroutines using both Base SAS (PROC FCMP) and the SAS macro language. No prior experience with SAS macros or PROC FCMP syntax is required. User-defined functions improve software reusability, which facilitates a more productive SAS team, and software configurability, which allows one function to be reused by many different SAS users. This HANDS-ON workshop enables students to run all programs in real-time using SAS Display Manager, SAS Enterprise Guide, or SAS OnDemand for Academics. Macro topics include: Introduction to the SAS macro language. Differentiation between positional and keyword parameters, and required and optional parameters. Passing macro lists to functions, by value and by reference. Use of PARMBUFF and &SYSPBUFF to facilitate multi-element arguments. Argument validation, exception handling, and use of return values / return codes. PROC FCMP topics include: Introduction to PROC FCMP syntax and the FUNCTION and SUBROUTINE statements. VARARGS option to enable passing multi-element arguments, and OUTARGS option to modify multiple arguments. Passing character and/or numeric data types to functions. Passing arrays to functions, and utilizing arrays within functions.Declaring, initializing, and referencing hash objects within functions. Calling functions and subroutines from the DATA step, PROC FORMAT, and %SYSFUNC and %SYSCALL.

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Everything is Better with Friends: Using SAS in Python Applications with SASPy and Open-Source Tooling (Beyond the Basics) (HOW)
Isaiah Lankham, Matthew Slaughter
Friday, September 16, 2022
1:30 PM – 5:30 PM PDT

Are you familiar with Python syntax? Want to go beyond the basics, and use SAS and Python together like a pro? In this hands-on class, we’ll practice writing Python scripts in Google Colab (an online implementation of JupyterLab). These Python scripts will link to SAS OnDemand for Academics using the Python package SASPy developed by SAS Institute. We’ll also practice using the popular Python package pandas, whose DataFrame objects are the Python equivalent of SAS datasets. Along the way, we’ll work through common data-analysis tasks using both regular SAS code and Python together with the SASPy package, highlighting important tradeoffs for each and emphasizing the value of being a polyglot programmer fluent in multiple languages. Specific examples include advanced data-manipulation techniques, using SASPy as an interface for SAS/STAT, rectangularizing complex JSON-formatted data returned by web APIs, and creating simple Python web applications incorporating SAS analytics. This class is aimed at intermediate to advanced SAS programmers, but assumes only basic familiarity with Python syntax and pandas DataFrames. However, no knowledge of JupyterLab is assumed. Accounts for Google and SAS OnDemand for Academics will be needed to interact with code examples. All class materials, including complete setup instructions, will be made available through https://github.com/saspy-bffs/wuss-2022-class

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Statistical Modeling in R with Tidymodels (HOW)
Kelly Bodwin
Friday, September 16, 2022
1:30 PM – 5:30 PM PDT

An introduction to the tidymodels R package suite, for intermediate R users. Students are expected to have a basic working knowledge of R, or sufficient programming background to quickly adopt new syntax. The course will mainly focus on principles and concepts of predictive modeling, as reflected in the tidymodels workflow structure. Emphasis will be placed on general concepts of cross-validation for model selection and interpretation of results. We will introduce a few basic classification and regression models, and practice fitting these to real data, interpreting results, and computing appropriate performance metrics. The course will end with the introduction of a custom Kaggle modeling challenge for students to take home and practice what they have learned.

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Meet the Instructors

Michael Aleman is a SAS Programmer with ~10 years experience in the financial (loan originations and asset recovery) and health care sectors.  Michael’s areas of expertise include automating daily patient identification processes, targeting populations for communications using APIs, dashboard reporting, and data management /monitoring.  He received a Bachelors’ of Science from National University and is currently employed at MedImpact Healthcare Systems, Inc.

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Kelly Bodwin is an Assistant Professor of Statistics and Data Science at Cal Poly University in San Luis Obispo and an RStudio Certified Instructor. Her primary area of teaching and research is computational statistics. While her work is mostly in R software development, she has recently begun teaching in python, and is enthusiastic about any and all statistical software. She has also published in the areas of statistics education, bioinformatics, and the digital humanities. In her free time, Kelly enjoys board games, backpacking, and hot tubs.

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Ron Cody – biography not available

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Dr. Lida Gharibvand is the Director, Statistics and Research Education, Associate Professor at Loma Linda University’s School of Allied Health Professions. Dr. Gharibvand has broad academic teaching and research expertise related to epidemiology, health-related research, statistical analysis, biostatistics, survival analysis, advanced mathematics, computer engineering, experimental designs and clinical trials. Dr. Gharibvand is teaching courses related to bio/statistics, research methods and SAS data analytics to graduate and undergraduate students from multiple disciplines. Her research interests involve air pollution, cancer, public health, geriatrics, DNA Methylation, medical science and oncology. Dr. Gharibvand is also the president of Orange County and Inland Empire SAS Users Group and serves on the Leadership Council of American Statistical Association for Orange County & Long Beach Chapter. She has written several papers and articles, been an Invited speaker at various conferences and has been the recipient of multiple awards. She holds a PhD degree in Epidemiology from Loma Linda University, Master of Science degree in Applied Statistics from University of California-Riverside and Master of Science degree in Mathematics from University of Nevada-Reno.

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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 user conferences. 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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Troy Martin Hughes has been a SAS practitioner for more than 20 years, has managed SAS projects in support of federal, state, and local government initiatives, and is a SAS Certified Advanced Programmer, SAS Certified Base Programmer, SAS Certified Clinical Trials Programmer, and SAS Professional V8. He has given more than 100 presentations, trainings, and hands-on workshops at SAS conferences, including at SAS Global Forum, SAS Analytics Experience, WUSS, SCSUG, SESUG, MWSUG, PharmaSUG, BASAS, and BASUG. He has authored two groundbreaking books that model software design and development best practices:
• SAS® Data-Driven Development: From Abstract Design to Dynamic Functionality, Second Edition (2022)
• SAS® Data Analytic Development: Dimensions of Software Quality (2016)
Troy has an MBA in information systems management as well as other credentials, including: PMP, PMI-RMP, PMI-PBA, PMI-ACP, SSCP, CISSP, CSSLP, Network+, Security+, CySA+, CASP+, Cloud+, CISA, CGEIT, CISM, CRISC, ITIL Foundation, CSM, CSP-SM, CSD, A-CSD, CSP-D, CSPO, CSP-PO, CSP, and SAFe Government Practitioner (SGF). He is a US Navy veteran with two tours of duty in Afghanistan.

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Jay Iyengar is Principal of Data Systems Consultants LLC. He is a SAS Consultant, Trainer, and SAS Certified Advanced Programmer. He was co-chair 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.

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Mark Jordan – biography not available

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Kirk Paul Lafler is a SAS, SQL and Python programmer, application developer and consultant. He teaches at San Diego State University and the University of California San Diego Extension as a lecturer and adjunct professor; and is an author of several books including, PROC SQL: Beyond the Basics Using SAS, Third Edition. Kirk has served as an Invited speaker, educator, keynote and section leader at SAS conferences for 40 years, and is the recipient of 25 “Best” contributed paper, hands-on workshop (HOW), and poster awards.

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Isaiah Lankham is an Advanced SAS Certified Programmer and a polyglot data analyst for the University of California’s systemwide office in Oakland, CA, specializing in data management/warehousing using Salesforce and data analysis/visualization using Tableau, SAS, and Python. Initially trained as a mathematician and educator, Isaiah is also an adjunct faculty member for the Statistics Department at California State University, East Bay, and enjoys regularly teaching graduate SAS programming courses.

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Theresa Ngo is a Data and AI Strategy, Senior Manager at Accenture. She advises clients on analytics vision, strategy, and transformation. Prior to Accenture, she was a Systems Engineer at SAS® Institute, recommending appropriate SAS solutions to clients to address various business needs. She focused primarily on Retail, CPG, and Utilities industries using SAS solutions (i.e., SAS/ETS, SAS Forecast Server, and SAS Visual Forecasting) to forecast sales, inventory, and electric load. Theresa holds a Master of Science in Applied Statistics from the University of California, Riverside. She has been an enthusiastic SAS user since 2007!

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Charu Shankar – biography not available

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Matthew T. Slaughter, MSBA is an Advanced SAS Certified Programmer and a Data Scientist at the Kaiser Permanente Center for Health Research in Portland, Oregon. With a focus on clinical prediction modeling, Matthew provides data management, programming, and analytical support to research projects in various topic areas.

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Zeke Torres has been a dedicated SAS user for over 25 years. His work is focused on “data”, with a passion for data driven systems/automation. His work includes realms such as: Credit/Bank/Finance (Citi, BMO, Discover Card, HSBC, Transamerica, GE); Insurance (Allstate, Trustmark); Healthcare (MPA HC Analytics, SG2) ; Software (RedMane, SAS); and Retail (Spiegel, Macy’s). He recently launched his own firm and startup Code629, which is focused on Healthcare Analytics and specializing in patient related claims facts/analytics. Code629 helps teams understand how SAS software and code can scale to enable insights, allowing clients to be more competitive and strategic. Zeke also has worked with user groups for over 15 years, ranging from the local Chicago SAS Group to regionals. He served on the board of directors for MWSUG and holds Director Emeritus status. He leads wcsug.com – The Chicago Area SAS Group, regularly advocating for user benefits and related topics. His recent work within SAS user groups revolves around building SAS user communities regardless of geography.

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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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