Editing and imputation in Python

Open to
Government analysts
Training category
Analytical, Data science
Type of training
4 to 6 hours
Data Science Campus Faculty
Data Science Campus Faculty

This course covers the practical application of editing and imputation in Python.

This course does not cover the theory of any of the methods specified. The theory of these methods is covered in the Introduction to editing and imputation course.

Who this course is for

To enrol on this course you will need to have completed the “Introduction to editing and imputation” course.

Learning outcomes

On this course you will learn how to:

  • apply methods of reviewing data — for example, identifying missing values, visualising the missing value, and finding duplicates in data in Python
  • do automatic editing where you can apply restrictions, check which restrictions have been violated and make corrections
  • impute missing values using model-based methods, including mean, median, ratio, and regression imputation in Python
  • impute missing values using donor-based imputations, including the random hot deck, sequential hot-deck, hierarchical hot-deck, and k-nearest neighbours (KNN) imputation in Python

How to book

Please use your Learning Hub account to access this course online.

If you do not have a Learning Hub account, please contact Data.Science.Campus.Faculty@ons.gov.uk.


If you would like more information about this course, please email Data.Science.Campus.Faculty@ons.gov.uk.

Related courses

Editing and imputation in R