video1.0<iframe src="https://www.loom.com/embed/cec2b1b900254eb9a9aa652e8a014515" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/cec2b1b900254eb9a9aa652e8a014515-00001.gif1634.8666666666597Hi there! In this TORiC training session, I'll be covering the basics of data validation and cleaning. We'll go over why data quality is important, how to check for data quality, and validation essentials such as data profiling, checking data against business rules, identifying errors, missing data, and outliers, and visualizations for data validation. I'll also be touching on data cleaning, including how to handle missing data, standardize your data set, deal with duplicates, and data tag. I'll walk you through my process of profiling, cleaning, and validating three different datasets, and show you some useful nodes and techniques along the way. By the end of this session, you'll have a better understanding of how to ensure your data is of high quality and ready for analysis.