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 4,5 and 6. This lesson covers how to create new variables by calculations in Excel, specifically, Creating variables by performing calculations Using Excel to create new calculated variables Introduction to conditional formulas Using conditional formulas in Excel Lesson content, A PowerPoint/PDF presentation, ‘Creating new variables by calculation in Excel’ Excel Question workbook on ‘Creating new variables by calculation in Excel’’ (for learners) Excel Answers workbook on ‘Creating new variables by calculation 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. © 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 create new variables in Excel, 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 Excel’ Excel Question workbook on ‘Creating new variables by extracting & combining in Excel’ (for learners) Excel Answers workbook on ‘Creating new variables by extracting & combining 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. © 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 keep your personal data and online accounts secure by using strong passwords, password managers and Multi-Factor Authentication. How to protect your devices with anti-virus software, firewalls, and VPNs. Lesson content, A PowerPoint/PDF presentation, ‘Keeping personal data secure’ Excel/PDF Question workbook on ‘Keeping personal data secure’ (for learners) Excel/PDF Answers workbook on ‘Keeping personal 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 on this lesson, 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 how to summarise datasets in Excel, specifically, calculate the total, count, min/max and average of rows data group rows of data based on logical criteria perform calculations on grouped data Lesson content, A PowerPoint/PDF presentation, ‘Summarising datasets in Excel’ Excel Question workbook on ‘Summarising datasets in Excel’ (for learners) Excel Answers workbook on ‘Summarising 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 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, Definitions of wide and long data Reasons for reshaping data Examples where data will need to be wide or long Lesson content, A PowerPoint/PDF presentation, ‘Reshaping datasets’ Excel/PDF Question workbook on ‘Reshaping datasets’ (for learners) Excel/PDF Answers workbook on ‘Reshaping 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 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 covers how data can be misused, specifically, What is data misuse Malicious data misuse and how it can happen Accidental data misuse and how it can happen Lesson content, A PowerPoint/PDF presentation, ‘Data misuse’ Excel Question workbook on ‘Data misuse’ (for learners) Excel Answers workbook on ‘Data misuse’ (for teachers) Planning document with learning intentions and sucess 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 creating bar charts and histograms in Excel, specifically, • how to amend the font, colour and display format of graph elements • how to amend gridlines on a graph • how to change the order and gaps of the bars in a bar chart • how to change the size of the bins in a histogram Lesson content, A PowerPoint/PDF presentation, ‘Practise creating graphs in Excel (part 1)’ Excel Question workbook on ‘Practise creating graphs in Excel (part 1)’ (for learners) Excel Answers workbook on ‘Practise creating graphs in Excel (part 1)’ (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. © 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 use data ethically, specifically, What are ethical data risks How to identify ethical risks What are data ethics frameworks and how they can be used to prevent ethical risks Lesson content, A PowerPoint/PDF presentation, ‘Ethical use of data’ Excel Question workbook on ‘Ethical use of data’ (for learners) Excel Answers workbook on ‘Ethical use of 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 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 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 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 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 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 covers how to manipulate rows in Python, specifically, Subsetting Filtering Sorting Deduplicating Lesson content, Powerpoint presentation, 'Manipulating dataset rows in Python’ Jupyter notebooks: ‘data_manipulation_of_rows_with_answers.ipynb’ (for teachers), and ‘data_manipulation_of_rows.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 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 to create new variables by calculations in Python (part 1 or 2), specifically, Creating new calculated variables, where the calculation that is used is the same for each row in the dataset. Lesson content, • A Powerpoint presentation, ‘Creating new variables by calculation in Python part 1’ • Jupyter notebooks: o ‘creating_variables_by_calculation_with_answers_part_1.ipynb’ (for teachers), and o ‘creating_variables_by_calculation_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 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 lessons covers how to create line graphs and scatterplots in Excel, specifically, how to make standard changes to line graphs and scatterplots how to plot a line graph without date value variables how to add data labels how to amend data points Lesson content, A PowerPoint/PDF presentation, ‘Practise creating graphs in Excel (part 2)’ Excel Question workbook on ‘Practise creating graphs in Excel (part 2)’ (for learners) Excel Answers workbook on ‘Practise creating graphs in Excel (part 2)’ (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. © 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 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.