Applied SAS® Programming & Analytics Summit

Modern Techniques for Today’s Data-Driven Organizations

Join fellow SAS® professionals from around the world for an immersive 5-hour virtual learning

experience designed to sharpen your programming skills, modernize your analytics workflow, and

accelerate real-world impact.

The Applied SAS® Programming & Analytics Summit delivers expert-led instruction focused on

modern SAS® programming practices, analytics workflows, AI, and Machine Learning. Participants will

explore efficient coding strategies, analytical techniques, and best practices that support enterprise-scale data initiatives and decision-making.

Whether you work in healthcare, life sciences, energy, finance, government, research, or education,

you don’t want to miss this event of usable skills that you can immediately apply to your projects.

Sessions and Schedule

Exploring the Skills Needed by the Data Scientist – Analytics Professional

As approximately 2.5 quintillion bytes (1 followed by 18 zeros) of new data are created every day, the era of big data has taken on unprecedented significance. Organizations across industries are increasingly embracing data science and analytics, driving a growing demand for qualified and experienced professionals. According to the U.S. Bureau of Labor Statistics (BLS), employment in data science – related roles is projected to grow by 19 percent over the next two decades – nearly three times the average growth rate for all occupations.

Motivated by this strong employment outlook, students and professionals across diverse job functions are actively preparing for the expanding demands of data science and analytics by developing comprehensive, multidisciplinary skill sets. In response, colleges, community colleges, universities, and vocational training organizations now offer a wide range of degree and certificate programs designed to meet the rising demand for analytical expertise.

This session examines the essential competencies required of today’s data science and analytics professionals. These include technical skills such as data access, data wrangling, statistical analysis, and proficiency in general-purpose programming and statistical languages including Python, R, SAS®, Structured Query Language (SQL), Microsoft Excel, and data visualization tools. Equally important, the discussion highlights critical non-technical skills, including collaboration, critical thinking, business acumen, and effective verbal and written communication.

Instructor: Kirk Paul Lafler

Intended Audience: Data Scientists, Statistical Programmers, Data Analysts, Statisticians, Data Engineers, and Others

Prerequisites: None

Delivery Method: Instructor-led with code examples

Session Time: 10:10 – 11:00 AM

Length: 50 minutes

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Under the Hood: The Mechanics of SQL Query Optimization Techniques

The SAS® software and SQL procedure provide powerful features and options for users to gain a better understanding of what’s taking place during query processing. This session explores SQL SELECT query syntax order and order of execution, the fully supported SAS® MSGLEVEL=I system option, and PROC SQL _METHOD option to display valuable informational messages on the SAS® Log about the SQL optimizer’s execution plan as it relates to processing SQL queries; along with an assortment of query optimization techniques.

Instructor: Kirk Paul Lafler

Intended Audience: Data Scientists, Statistical Programmers, Data Analysts, Statisticians, Data Engineers, and Others

Prerequisites: Minimum 6-months Base-SAS programming experience

Delivery Method: Instructor-led with code examples

Session Time: 11:10 – 12:00 PM

Length: 50 minutes

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Popular SAS® Macro Programming Techniques

The SAS® Macro Language is a powerful and flexible tool for extending and automating the capabilities of the SAS® System. This session explores a wide range of proven tips and programming techniques for designing effective, reusable, and maintainable macros. Topics include processing SAS statements that contain macro code; replacing text strings with macro variables; generating dynamic SAS programs; manipulating macro variable values using macro functions; integrating the macro language with the DATA step and PROC SQL; storing and reusing macros; and building macros with both positional and keyword parameters. This session also covers best practices for troubleshooting and debugging macro code, along with strategies for developing efficient, portable, and production-ready macro programs.

Instructor: Kirk Paul Lafler

Intended Audience: Data Scientists, Statistical Programmers, Data Analysts, Statisticians, Data Engineers, and Others

Prerequisites: Minimum 1-year SAS programming experience

Delivery Method: Instructor-led with code examples

Session Time: 12:10 – 1:00 PM

Length: 50 minutes

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Modern Deep Learning Architectures for Today’s Diverse Data Types (with

SAS® Viya® and Python Ecosystems)

Deep learning (DL) provides powerful methods for modeling tabular, unstructured, and time-dependent data across a wide range of applications. This session offers a high-level overview of modern deep learning architectures and their practical use for classification, forecasting, and generative tasks. Emphasis is placed on how these approaches are implemented in practice using SAS® Viya®, including PROC NNET within SAS Visual Data Mining and Machine Learning, alongside widely adopted Python deep learning frameworks such as TensorFlow, PyTorch, and Keras. This session also introduces transfer learning as a key strategy for accelerating model development and improving performance when adapting models to new data and problem domains.

Instructor: Ryan Paul Lafler

Intended Audience: Data Scientists, Statistical Programmers, Data Analysts, Statisticians, Data Engineers, and Others

Prerequisites: No previous experience required

Delivery Method: Instructor-led with code examples

Session Time: 1:10 – 2:00 PM

Length: 50 minutes

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Enhancing Your SAS® Viya® Workflows with Python: Integrating Python’s

Open-Source Libraries with SAS® using PROC PYTHON

Data scientists, statistical programmers, machine learning engineers, and researchers are increasingly leveraging a growing number of open-source tools, libraries, and programming languages that can enhance and seamlessly integrate with their existing data workflows. One of these integrations, built into SAS® Viya®, is its pre-configured Python runtime integration, PROC PYTHON, that gives SAS programmers access to Python's open-source data science libraries for wrangling and modeling structured and unstructured data alongside the validated procedures provided in SAS. This session demonstrates how to install and import external Python libraries into their SAS Viya sessions; generate Python scripts containing methods that can import, process, visualize, and analyze data; and execute those Python methods and scripts using the SAS Viya PYTHON procedure. By integrating the added functionalities of Python's libraries for data processing and modeling with SAS procedures, programmers can enhance their existing data workflows with Python's open-source data solutions.

Instructor: Ryan Paul Lafler

Intended Audience: Data Scientists, Statistical Programmers, Data Analysts, Statisticians, Data Engineers, and Others

Prerequisites: No previous experience required

Delivery Method: Instructor-led with code examples

Session Time: 2:10 – 3:00 PM

Length: 50 minutes