Free lesson resources for teaching Data Science NPA (National Progress Award) Level 5 and 6. This lessons follows on from the ‘Practise combining datasets’ lesson which is available from the effini shop. This lesson covers how to combine datasets in Excel, specifically: how to join datasets when the key columns have different names how to join datasets with multiple key columns how to solve problems by selecting the appropriate join type Lesson content: • A lesson plan (this document) • a PowerPoint presentation, ‘Advanced practise combining datasets in Excel’ • a question worksheet (for learners) on ‘Advanced practise combining datasets in Excel’ in Excel • an answers worksheet (for teachers) on ‘Advanced practise combining datasets in Excel’ in Excel a 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 The Data Lab. © 2024 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 2 of 2), specifically, group rows of data based on logical criteria perform summary calculations on grouped data Lesson content, A PowerPoint/PDF presentation, ‘Summarising datasets in Python (part 2)’ Jupyter notebooks: ‘summarising_datasets_with_answers_part_2.ipynb’ (for teachers) ‘summarising_datasets_part_2.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, the difference between qualitative and quantitative data the difference between discrete and continuous data. Lesson content, A PowerPoint/PDF presentation, ‘Qualitative & Quantitative’ Excel/PDF Question workbook on ‘Qualitative & Quantitative’ (for learners) Excel/PDF Answers workbook on ‘Qualitative & Quantitative’ (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. © 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 follows on from ‘The Analysis Process’ which is available from the effini TES shop. This lesson covers the data understanding part of the analysis process in Excel, specifically, • what is metadata and the importance of a data dictionary • how to identify the shape and size of a dataset in Excel and data types of variables • how to identify missing values and outliers in Excel Lesson content, A PowerPoint/PDF presentation, ‘Dataset understanding in Excel’ Excel Question workbook on ‘Dataset understanding in Excel’ (for learners) Excel/PDF Answers workbook on ‘Dataset understanding 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.

This document is a guide to the educational resources developed by effini, in collaboration with Data Education in Schools and The Data Lab, to support educators delivering the National Progression Award (NPA) in Data Science or Professional Development Award (PDA) in Data Science. It provides an overview of these resources and is intended to be used by educators. It provides educators with information on: • What the NPA and PDA in Data Science are • Options for delivering the NPA • What the resources are • What can be done with them, and • How to access them. If you have any questions, please contact us at lessons@effini.com

Free lesson resources for teaching Data Science NPA (National Progress Award) Level 5 and 6. This lessons follows on from the ‘Combining datasets’ lesson which is available from the effini shop. This lesson covers how to combine datasets in Python, specifically, • how to append rows to a dataset, and • how to join columns to a dataset Lesson content, A Powerpoint presentation, ‘Practise Combining Datasets in Python’ Jupyter notebooks: o ‘practise_combining_datasets_with_answers.ipynb’ (for teachers), and o ‘practise_combining_datasets.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 If you have any questions or feedback please email lessons@effini.com This lesson has been created by effini in partnership with The Data Lab. © 2023 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 5 and 6. This lesson follows on from ‘Combining datasets’ lesson, which is available from the effini TES shop. This lesson covers how to combine datasets in Excel, specifically, • how to use Power Query Editor to append rows to a dataset • how to use Power Query Editor to join columns to a dataset Lesson content, A PowerPoint/PDF presentation, ‘Practise combining datasets in Excel’ Excel Question workbook on ‘Practise combining datasets in Excel’ (for learners) Excel Answers workbook on ‘Practise combining datasets in Excel’ (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 The Data Lab. © 2023 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 ‘Dataset cleansing in Python’ (part 1 & 2) and ‘Advanced data cleansing in Python’ (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, • 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 data cleansing in Python’ 2 Jupyter notebooks: ‘practise_data_cleansing.ipynb’ (for learners) ‘practise_data_cleansing_with_answers.ipynb’ (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) Levels 5 and 6. This lesson follows on from ‘The Analysis Process’ and the ‘Dataset cleansing in Python’ (part 1 & 2) which are available from the effini TES shop. This lesson covers the advanced data cleansing part of the analysis process in Python (part 2 of 2), specifically, • how to fix strings, • how to handle missing/outlying values Lesson content, A PowerPoint/PDF presentation, ‘Advanced data cleansing in Python (part 2)’ 2 Jupyter notebooks: ‘advanced_data_cleansing_part_2.ipynb’ (for learners) ‘advanced_data_cleansing_with_answers_part_2.ipynb’ (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) Levels 4, 5 and 6. This lesson follows on from ‘The Analysis Process’ which is available from the effini TES shop. This lesson covers the data cleansing part of the analysis process in Python (part 2 of 2), specifically, • how to handle missing values • how to handle outliers Lesson content, A PowerPoint/PDF presentation, ‘Dataset cleansing in Python (part 1)’ 2 Jupyter notebooks: ‘data_cleansing_part_2.ipynb’ (for learners) ‘data_cleansing_with_answers_part_2.ipynb’ (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) Levels 4, 5 and 6. This lesson follows on from ‘The Analysis Process’ and the ‘Dataset cleansing in Python’ (part 1 & 2) which are available from the effini TES shop. This lesson covers the advanced data cleansing part of the analysis process in Python (part 1 of 2), specifically, • how to convert between data types Lesson content, A PowerPoint/PDF presentation, ‘Advanced data cleansing in Python (part 1)’ 2 Jupyter notebooks: ‘advanced_data_cleansing_part_1.ipynb’ (for learners) ‘advanced_data_cleansing_with_answers_part_1.ipynb’ (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 5 and 6. This lesson covers how to combine datasets, specifically, • what we mean by combining datasets • to add rows to a dataset by appending • to add columns to a dataset by joining • understand common types of joins Lesson content, A PowerPoint/PDF presentation, ‘Combining datasets’ Excel/PDF Question workbook on ‘Combining datasets’ (for learners) Excel/PDF Answers workbook on ‘Combining datasets’ (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 The Data Lab. © 2023 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 what is involved in the analysis process, specifically, • what we mean by analysis • a structured way of performing analysis (the analysis steps) • how to understand data through visual inspection Lesson content, A PowerPoint/PDF presentation, ‘The analysis process’ Excel Question workbook on ‘The analysis process’ (for learners) Excel Answers workbook on ‘The analysis process’ (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. © 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 follows on from ‘The Analysis Process’ which is available from the effini TES shop. This lesson covers the data cleansing part of the analysis process in Excel, specifically, • how to drop unrequired rows and columns • what naming conventions are commonly used for variables and how to rename variables • how to remove duplicates • how to fix missing and outlying values that have already been identified • how to remove metadata Lesson content, A PowerPoint/PDF presentation, ‘Dataset cleansing in Excel’ Excel Question workbook on ‘Dataset cleansing in Excel’ (for learners) Excel/PDF Answers workbook on ‘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) Levels 4,5 and 6. This lesson follows on from ‘Dataset understanding in Excel’ lesson which is available from the effini TES shop. This lesson allows learners to practice the skills covered in the Dataset Understanding in Excel, specifically, • Using data dictionaries/metadata • Identifying size and shape • Identifying missing values and outliers Lesson content, A PowerPoint/PDF presentation, ‘Practise Dataset Understanding in Excel’ Question worksheet (for learners) on ‘Practise Dataset understanding in Excel’ in Excel Answers worksheet (for teachers) on ‘Practise Dataset understanding in Excel’ in Excel 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 covers, how pandas indexes work practise reshaping from long to wide and wide to long Lesson content, A PowerPoint/PDF presentation, ‘Practise reshaping datasets in Python’ 2 Jupyter notebooks: ‘practise_reshaping_data.ipynb’ (for learners) ‘practise_reshaping_data_with_answers.ipynb’ (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 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 lesson follows on from ‘Dataset understanding in Python’ (parts 1 &2) lessons which is available from the effini TES shop. This lesson allows learners to practise the skills covered in the Dataset understanding in Python lessons, specfically, • how to import a dataset without importing metadata • how to use a data dictionary to find out about a dataset • how to find the shape, size and format of datasets, using Python • how to find the data types of variables in a dataset, using Python • how to identify outliers and missing values in Python Lesson content, A PowerPoint/PDF presentation, ‘Practise Dataset Understanding in Python’ Jupyter notebooks: ‘practise_data_understanding withanswers.ipynb’ (for teachers), and 'practise_data_understanding.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’ which is available from the effini TES shop. This lesson covers the advanced data cleansing part of the analysis process in Excel, specifically, • how to convert between different data types • how to fix strings • understand the reasons why there may be missing or outlying values, and how these reasons affect the ways in which we handle them Lesson content, A PowerPoint/PDF presentation, ‘Advanced dataset cleansing in Excel’ Excel Question workbook on ‘Advanced dataset cleansing in Excel’ (for learners) Excel/PDF Answers workbook on ‘Advanced 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) Levels 4, 5 and 6. This lesson follows on from ‘The Analysis Process’ which is available from the effini TES shop. This lesson covers the data cleansing part of the analysis process in Python (part 1 of 2), specifically, • how to import a dataset without importing metadata • what naming conventions are commonly used for variables and how to rename variables • how to drop unrequired rows and variables • how to drop duplicates Lesson content, A PowerPoint/PDF presentation, ‘Dataset cleansing in Python (part 1)’ 2 Jupyter notebooks: ‘data_cleansing_part_1.ipynb’ (for learners) ‘data_cleansing_with_answers_part_1.ipynb’ (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) 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.