Abstract: Detecting and repairing dirty data is a constant challenge in data analytics. Failure to properly clean and prepare data can result in inaccurate analyses and unreliable decisions. Data cleaning is therefore an essential step in most data analytics techniques, but it is also one of the most time-consuming steps. In this lecture, we will look at common problems found in datasets and how to deal with them. We will also examine how to build data cleaning scripts for data suffering from a wide range of errors/inconsistencies using Pandas, a Python Data Analysis Library.

Bio: Alawya Alawami is an Associate Professor of Data Analytics and the Lab manager at Pennwest University. She also worked as a Data Consultant for Neurobehavioral Medicine Consultants. Her research interest includes text mining and sentiment analysis. Alawya obtained her MS and Ph.D. from the School of Computing and Information in 2017.

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