Data Cleaning for Everyone
Key Takeaways:- Learn about the most common issues in dirty data.
- Learn techniques for dealing with dirty datasets.
- Learn best practices for cleaning data, and how to avoid common pitfalls.
Description
There's a commonly cited statistic that whenever you get a new dataset, 80% of the project time is just spent cleaning the dataset. Even though it's such a commonly used skill, it's one of the toughest data analysis skills to crack. Of all the candidates who go through DataCamp Certifications, data cleaning is the step that is the most common reason to fail.
In this session, you'll learn about the common types of "data dirtiness" and the techniques you need to clean them. You'll also learn about the common mistakes made when cleaning data, and how to avoid them.
This session is essential for anyone considering taking any of the DataCamp Certifications or anyone who has to deal with dirty datasets.
Presenter Bio
Aimée Gott is a Learning Solutions Architect at DataCamp, with a background in statistics and over a decade of experience in data education. She began her career at Mango Solutions, where she led data science training initiatives across a wide range of industries. At DataCamp, she has contributed to the development of certification programs and skills assessments, and now focuses on designing and delivering learning in data and AI for DataCamp's customers.