Data Cleaning Seminar

Seminar recording and materials:

PowerPoint Presentation

• The presentation is available at this link. 
• "LEARNING SAS® HAS NEVER BEEN EASIER" is available from this link.

Seminar description

Data Cleaning: What does data cleaning mean? How do I do it? What steps are involved and in what order? This seminar answers these questions and covers all the steps that occur between data collection and analysis. You'll learn a framework for approaching cleaning your data that will guide the order in What does data cleaning mean? How do I do it? What steps are involved and in what order? This seminar answers these questions and covers all the steps that occur between data collection and analysis. You'll learn a framework for approaching cleaning your data that will guide the order in which steps should be completed and assist with some trickier issues, like identifying anomalous, vague, or missing data. Upon the conclusion of this session, you will be well-equipped to select the subsequent complimentary online workshops that suit your needs. These workshops are designed to provide a deeper understanding of the topic and further enhance your research skills. 

Presenter

Nancy Rausch is a Senior Software Manager and Data Scientist in Research and Development at SAS Institute. She leads a team of engineers that develop SAS’ Data Governance and Data Quality products, with a focus on leveraging AI and Machine Learning methods for data lifecycle management. She is also the Chairperson of the Linux Foundation AI & Data Technology Advisory Council, working to help expand the adoption of open-source technologies at the intersection of AI and Data.  Nancy is a researcher in SAS’ energy technology sector, applying machine learning methods to energy forecasting in support of the smart grid, and an Advisory board member for the NSF funded non-profit LASER Institute promoting Learning Analytics in STEM Education Research.  She is a mentor in SAS’ industry/education partnership program, supporting researchers at North Carolina State University’s Department of Computer Science. She has authored over 20 research papers and publications and presented at numerous conferences on a wide variety of topics related to the intersection of AI and Data Management. She holds a Master of Science degree from Capella University in Data Analytics, a Master of Science degree from Duke University in Computer Engineering, and a Bachelor of Science degree from Michigan Technological University in Electrical Engineering.