This page provides a set of resources covering more than half of the NPA curriculum across levels 4, 5 and 6 (we have focused initially on providing resources for areas where there is no content available elsewhere). These are continuously being added to—for the most up-to-date list of resources available, check our Learn Data Science page. This page is also helpful if you would like to download each lesson quickly and easily by clicking on each one.

Materials are provided for use with Excel and Python.

The NPA can be achieved using only a basic spreadsheet tool, such as Microsoft Excel or Google Sheets. It is important that learners gain a secure grounding in their knowledge of data science and statistical concepts. Gaining experience of particular tools or programming languages that might be favoured by industry is much less important to learners at this stage of their career. Although it is not a requirement, Level 6 learners might benefit from carrying out data analysis and visualisation tasks using a programming language.

All links below point to pages which link to zip files containing PowerPoint lessons along with the relevant additional resources, such as worksheets with answers, Excel spreadsheets, and/or Python code.

If you require these documents in an alternative format, such as large print or a coloured background, please contact dataschools@ed.ac.uk.

All lessons

Each topic below is recommended to be covered in one to two lessons. Some topics contain two parts for Python, which are aimed to be covered in two classes. Click on the pages for more details and download links.

Topic NumberTopic TitleNPA 4NPA 5NPA 6PDA 7PDA 8
1Qualitative and Quantitative Data✔️✔️✔️✔️✔️
2Scales of Measurement✔️✔️✔️✔️
3The Structure and Format of Data ✔️✔️✔️✔️✔️
4Data Types and Storage ✔️✔️✔️✔️✔️
5Python only: Introduction to Jupyter Notebooks ✔️✔️✔️✔️✔️
6Python only: Introduction to Python for Data Science ✔️✔️✔️✔️
7Manipulating Columns in a Dataset✔️✔️✔️✔️✔️
8Manipulating Rows in a Dataset✔️✔️✔️✔️✔️
9Creating New Variables by Calculation✔️✔️✔️✔️
10Creating new Variables by Extracting and Combining✔️✔️✔️✔️✔️
11Summarising Data✔️✔️✔️✔️✔️
12Reshaping Datasets✔️✔️✔️✔️
13Practice Reshaping Datasets✔️✔️✔️✔️
14Understanding Datasets ✔️✔️✔️✔️✔️
15Practice Understanding Datasets✔️✔️✔️✔️✔️
16The Analysis Process✔️✔️✔️✔️✔️
17Data Cleansing✔️✔️✔️✔️✔️
18Advanced Data Cleansing✔️✔️✔️✔️
19Practice Data Cleansing✔️✔️✔️✔️
20Creating Graphs✔️✔️✔️✔️✔️
21Practice Creating Graphs✔️✔️✔️✔️✔️
22Keeping Personal Data Secure ✔️✔️✔️✔️✔️
23Keeping Organisational Data Secure✔️✔️✔️✔️✔️
24Data Misuse✔️✔️✔️✔️✔️
25Ethical Use of Data✔️✔️✔️
26Causes and Impacts of Bias✔️✔️✔️✔️✔️
27Importance of Data Quality✔️✔️✔️✔️✔️
28Caring for Data✔️✔️✔️✔️
29Data Management✔️✔️
30Combining Datasets✔️✔️✔️✔️
31Practice Combining Datasets✔️✔️✔️✔️