Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 5 and 6.

This lesson follows on from 鈥楧ataset cleansing in Python鈥� (part 1 & 2) and 鈥楢dvanced data cleansing in Python鈥� (part 1 & 2) which are available from the effini TES shop.

This lesson allows learners to practise the skills covered in the Data Cleansing part of the analysis process in Python, specifically,

鈥� how to rename variables
鈥� how to drop unrequired rows and variables
鈥� how to drop duplicates
鈥� how to handle missing data and outliers

Lesson content,

A PowerPoint/PDF presentation, 鈥楶ractise data cleansing in Python鈥�
2 Jupyter notebooks:

鈥榩ractise_data_cleansing.ipynb鈥� (for learners)
鈥榩ractise_data_cleansing_with_answers.ipynb鈥� (for teachers)
Planning document with learning intentions and success criteria
For more information on the Data Science NPA, please see teachdata.science

This lesson has been created by effini in partnership with Data Education in Schools, The Data Lab and Data Skills for Work, with funding from the Scottish Government.

漏 2022. This work is licensed under a CC BY-NC-SA 4.0 license.