Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lesson covers how to create new variables in Python, specifically, • what and how to to extract data to create a new variable • what and how to combine data to create a new variable Lesson content, A PowerPoint/PDF presentation, ‘Creating new variables by extracting & combining in Python’ Jupyter notebooks: ‘creating_variables_by_extracting_or_combining_with_answers.ipynb’ (for teachers), ‘creating_variables_by_extracting_or_combining.ipynb’ (for learners) Datasets used in the Jupyter notebooks: the datasets are stored online and imported by the Jupyter notebooks. 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. © 2021. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lesson covers how to summarise datasets in Python (part 1 of 2), specifically, summarise complete datasets perform summary calculations for single variables, such as the total, count, min/max and average values perform summary calculations for multiple variables Lesson content, A PowerPoint/PDF presentation, ‘Summarising datasets in Python (part 1)’ Jupyter notebooks: ‘summarising_datasets_with_answers_part_1.ipynb’ (for teachers) ‘summarising_datasets_part_1.ipynb’ (for learners) Datasets used in the Jupyter notebooks: the datasets are stored online and imported by the Jupyter notebooks. 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. © 2021. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 5 and 6. This lesson covers, Practise switching between wide and long datasets in Excel. This lesson follows on from the Data Science - Reshaping Datasets lesson, which is available through the effini TES shop. Lesson content, A PowerPoint/PDF presentation, ‘Practise reshaping datasets in Excel’ Excel Question workbook on ‘Practise reshaping datasets in Excel’ (for learners) Excel Answers workbook on ‘Practise reshaping datasets in Excel’ (for teachers) Planning document with learning intentions and success criteria The lesson has been designed for learners using Microsoft Excel on a Windows based machine. This lesson uses Power Query to reshape datasets. Power Query is currently only supported on Microsoft Excel when it is run on a Windows based machine. For more information on the Data Science NPA, please see teachdata.science If you have any questions or feedback on this lesson, please email lessons@effini.com 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

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lessons follows on from ‘Dataset Understanding in Python (part 1)’ lesson which is available from the effini TES shop. This lesson continues to look at the data understanding step of the analysis step, specifically, • identification of outliers and missing values Lesson content, A PowerPoint/PDF presentation, ‘Dataset Understanding in Python (Part 2)’ Jupyter notebooks: ‘understanding_datasets_with_answers_part_2.ipynb’ (for teachers), and ‘understanding_datasets_part_2.ipynb’ (for learners) Datasets used in the Jupyter notebook: the datasets are stored online and imported by the Jupyter notebook. 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. © 2022. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 5 and 6. This lesson follows on from ‘Data cleansing in Excel’ and ‘Advanced data cleasning in Excel’ 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 Excel, specifically, • how to rename variables • how to drop unrequired rows and variables • how to drop duplicates • how to handle missing data and outliers Lesson content, A PowerPoint/PDF presentation, ‘Practise dataset cleansing in Excel’ Excel Question workbook on ‘Practise dataset cleansing in Excel’ (for learners) Excel/PDF Answers workbook on ‘Practise dataset cleansing in Excel’ (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. © 2022. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Level 6. This lesson covers how to care for your data, specifically, What are the different data types that need to be cared for How to create a data dictionary Lesson content, A PowerPoint/PDF presentation, ‘Caring for your data’ Excel Question workbook on ‘Caring for your data’ (for learners) Excel Answers workbook on ‘Caring for your data’ (for teachers) Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science If you have any questions or feedback please email lessons@effini.com This lesson has been created by effini in partnership with Data Education in Schools and Skills Development Scotland. © 2022 This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lesson covers, Different structures for holding data. Difference between stored and display formats Lesson content, A PowerPoint/PDF presentation, ‘The structure and format of data’ Excel/PDF Question workbook on ‘The structure and format of data’ (for learners) Excel/PDF Answers workbook on ‘The structure and format of data’ (for teachers) Planning document with learning intentions and sucess 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

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lesson covers how to manipulate columns in Excel, specifically, Selecting columns Reordering columns Reformatting columns Lesson content, A PowerPoint/PDF presentation, ‘Manipulating dataset columns in Excel’ Excel Question workbook on ‘Manipulating dataset columns in Excel’ (for learners) Excel Answers workbook on ‘Manipulating dataset columns in Excel’ (for teachers) Planning document with learning intentions and sucess 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

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lessons is an Introduction to Jupyter notebooks ,covering, an introduction to Jupyter notebooks as a tool for writing code for Data Science projects. Lesson content, Powerpoint presentation: ‘Jupyter notebooks’ Jupyter notebooks: ‘intro_to_jupyter.ipynb’ Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science If you have any questions or feedback on this lesson, please email lessons@effini.com 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. © 2021. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lessons follows on from ‘The Analysis Process’ lesson which is available from the effini TES shop. This lesson covers the data understanding step of the analysis step, specifically, • metadata and data dictionaries • the size, shape and format of a dataset • the data types of variables in a dataset Lesson content, A PowerPoint/PDF presentation, ‘Dataset Understanding in Python (Part 1)’ Jupyter notebooks: ‘understanding_datasets_with_answers_part_1.ipynb’ (for teachers), and ‘understanding_datasets_part_1.ipynb’ (for learners) Datasets used in the Jupyter notebook: the datasets are stored online and imported by the Jupyter notebook. 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. © 2022. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lesson covers how to manipulate columns in Python, specifically, Selecting columns Reordering columns Reformatting columns Lesson content, Powerpoint presentation, 'Manipulating dataset columns in Python’’ Jupyter notebooks: ‘data_manipulation_of_columns_with_answers.ipynb’ (for teachers), and ‘data_manipulation_of_columns.ipynb’ (for learners) The Jupyter notebook for teachers contains answers to the tasks set for learners. 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

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lesson covers how organisations keep data secure and what data rights and responsibilities individuals and organisations have under the law. Lesson content, A PowerPoint/PDF presentation, ‘Keeping organisational data secure’ Excel Question workbook on ‘Keeping organisational data secure’ (for learners) Excel Answers workbook on ‘Keeping organisational data secure’ (for teachers) Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science If you have any questions or feedback please email lessons@effini.com This lesson has been created by effini in partnership with Data Education in Schools and Skills Development Scotland. © 2022. This work is licensed under a CC BY-NC-SA 4.0 license.

Free lesson resources for teaching Data Science NPA (National Progress Award) Levels 4,5 and 6. This lessons covers how to create bar charts in Python, specifically, Creation and modification of bar charts in Python using the seaborn package. Lesson content, Powerpoint presentation: ‘Creating Bar Charts in Python’ Jupyter notebooks: ‘creating_bar_charts.ipynb’ (for learners) ‘creating_bar_charts_answers.ipynb’ (for teachers) The Jupyter notebook for teachers contains answers to the tasks set for learners. Planning document with learning intentions and success criteria For more information on the Data Science NPA, please see teachdata.science If you have any questions or feedback on this lesson, please email lessons@effini.com 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. © 2022. This work is licensed under a CC BY-NC-SA 4.0 license.