SAS Viya for SAS 9.4 Users: What You Need to Know
Transitioning from SAS 9.4 to SAS Viya can feel like learning a whole new language. With a completely different architecture, updated programming paradigms, and an emphasis on advanced analytics like machine learning (ML) and artificial intelligence (AI), the shift can be overwhelming. This session is designed to be your "SAS translator," helping you navigate the key differences between SAS 9.4 and SAS Viya. We'll break down the essentials you need to know about Viya's cloud-based architecture, explore the updated programming techniques, and dive into how ML and AI capabilities are integrated. You'll leave with a clear understanding of how to apply your existing SAS 9.4 skills in this new environment, armed with practical examples and tips.
Proven Practices for Predictive Modeling
our ongoing quest for "analytics excellence," what are some of the strategies and tactics that we, as analytics practitioners, can consider not only for individual predictive modeling projects, but for increasing the value and importance of analytics in our organizations? This presentation
will share some of the common strategies, attributes, processes and best practices of the most successful organizations. Best practices will include considerations for an overall analytics process as well as the discrete steps of building a predictive model, such as data preparation and sampling; input (variable) examination, selection and transformation; model selection and validation; and more.
will share some of the common strategies, attributes, processes and best practices of the most successful organizations. Best practices will include considerations for an overall analytics process as well as the discrete steps of building a predictive model, such as data preparation and sampling; input (variable) examination, selection and transformation; model selection and validation; and more.
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About the Presenter
With over a decade of experience in data analytics and machine learning, Melodie Rush brings a wealth of expertise to her role as an Customer Success Data Scientist. Her passion for solving complex problems with innovative technology has made her an essential member of the SAS team. Melodie's work involves developing and deploying advanced models that help businesses make better decisions based on their data insights. She is constantly exploring new ways to improve analytical methods and technologies, striving to create systems that are faster, more accurate, and more efficient than ever before. In addition to her technical skills, Melodie is also known for her ability to communicate complex concepts in simple terms.
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