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 covers, Different structures for holding data. Difference between stored and display formats Lesson content, A PowerPoint/PDF presentation, ‘Data types and storage’ Excel/PDF Question workbook on ‘Data types and storage’ (for learners) Excel/PDF Answers workbook on ‘Data types and storage’ (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 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 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 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) Level 6. This lesson covers how to manage data, specifically, The areas of data management and the activities organisations undertake. Why it’s important to manage data, and what happens when data is not managed well. Lesson content, A PowerPoint/PDF presentation, ‘Data Management’ Excel Question workbook on ‘Data Management’ (for learners) Excel Answers workbook on ‘Data Management’ (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 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) Levels 4,5 and 6. This lessons is an Intro to Python for Data Science (part 2 of 2) ,covering, understand Python data types and data structures that are important for data science manipulate strings create and call Python functions call a Python object’s methods and access its properties perform a sequence of operations using method chaining Lesson content, Powerpoint presentation: ‘Introduction to Python for Data Science (Part 2)’ Jupyter notebooks: ‘intro_to_python_for_data_science_part_2.ipynb’ (for learners) ‘intro_to_python_for_data_science_with_answers_part_2.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. © 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 is an Intro to Python for Data Science (part 1 of 2) ,covering, why Python is widely used in data science install, import and use Python packages understand how to get help when using Python name variables clearly and consistently Lesson content, Powerpoint presentation, ‘Introduction to Python for Data Science (Part 1)’ Jupyter notebooks: ‘intro_to_python_for_data_science_part_1.ipynb’ (for learners) ‘intro_to_python_for_data_science_with_answers_part_1.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. © 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 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) 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 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 lesson covers, Understanding the scales of measurement. Qualitative data - nominal and ordinal. Quantitative data - interval and ratio Lesson content, A PowerPoint/PDF presentation, ‘Scales of measurement’ Excel/PDF Question workbook on ‘Scales of measurement’ (for learners) Excel/PDF Answers workbook on ‘Scales of measurement’ (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 rows in Excel, specifically, Subsetting Filtering Sorting Deduplicating Lesson content, A PowerPoint/PDF presentation, ‘Manipulating datasets rows in Excel’’ Excel Question workbook on ‘Manipulating datasets rows in Excel’’ (for learners) Excel Answers workbook on ‘Manipulating datasets rows 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 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.