Dealing with Missing Data in Python

Yes, upon completion of the course you will receive a DataCamp Certificate of Completion.

This course covers topics such as null value operations, establishing patterns in missing and non-missing data, basic imputation techniques, advanced imputation techniques, and evaluating missing data.

This course covers numerical, categorical, and time-series data.

This course is ideal for data scientists and analysts who need to clean data more efficiently and accurately. It can also be beneficial for software engineers, databases administrators, and other professionals that work with data in their day-to-day.

This course uses air quality and diabetes datasets to demonstrate how to use the various methods presented.