This page provides an initial set of resources covering the first half of the NPA across levels 4, 5 and 6.
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 language 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 would benefit from carrying out data analysis and visualisation tasks using a programming language.
All links below point to zip files containing Powerpoint lessons along with the relevant additional resources.
If you require these documents in an alternative format, such as large print or a coloured background, please contact dataschools@ed.ac.uk.
Excel Lessons
1. Quantitative and Qualitative Data
Explores the difference between qualitative and quantitative data and the difference between discrete and continuous data. NPA (4,5,6) PDA (7,8)
Explores the difference between qualitative and quantitative scales of measurement. NPA (5,6) PDA (7,8)
3. The Structure and Format of Data
Understand the difference between stored and display formats. NPA (4,5,6) PDA (7,8)
Understand different structures for holding data and the difference between stored and display formats. NPA (4,5,6) PDA (7,8)
5. Manipulating Columns in a Dataset
Selecting, reordering and reformatting columns. NPA (4,5,6) PDA (7,8)
6. Manipulating Rows in a Dataset
Subsetting, filtering, sorting and deduplicating. NPA (4,5,6) PDA (7,8)
7. Creating New Variables by Calculation.
Understand what it means to create a new variable by performing calculations, and the concept of conditional statements. NPA (4,5,6) PDA (7,8)
8. Creating New Variables by Extracting and Combining
Creating new variables by extracting and combining data. NPA (4,5,6) PDA (7,8)
9. Summarising Data.
Summarise by count, total, average, max and min. NPA (4,5,6) PDA (7,8)
Understanding definitions of wide and long data and when to reshape data. NPA (5,6) PDA (7,8)
11. Practice reshaping Datasets
Switching between wide and long datasets. NPA (5,6) PDA (7,8)
Understanding data dictionaries/metadata, size and shape, missing values and outliers. NPA (4,5,6) PDA (7,8)
13. Practice Understanding Datasets
Using the skills learned from Understanding Datasets. NPA (4,5,6) PDA (7,8)
Understanding analysis how to visually inspect data. NPA (4,5,6) PDA (7,8)
15. Data Cleansing
Removing metadata and duplicates, dropping columns and rows, renaming variables, fixing missing and outlying values. NPA (4,5,6) PDA (7,8)
This lesson allows learners to practise the techniques learnt in the data cleansing lessons. NPA (5,6) PDA (7,8)
Converting between different data types, fixing strings, focusing on the causes of missing/outlying values. NPA (5,6) PDA (7,8)
18. Creating Graphs
Creating bar charts, histograms, line graphs, scatterplots. NPA (4,5,6) PDA (7,8)
Practise creating/amending different simple graphics types in Excel. NPA (4,5,6) PDA (7,8)
Python Lessons
1. Quantitative and Qualitative Data
Explores the difference between qualitative and quantitative data and the difference between discrete and continuous data. NPA (4,5,6) PDA (7,8)
Explores the difference between qualitative and quantitative scales of measurement. NPA (5,6) PDA (7,8)
3. The Structure and Format of Data
Understand the difference between stored and display formats. NPA (4,5,6) PDA (7,8)
Understand different structures for holding data and the difference between stored and display formats. NPA (4,5,6) PDA (7,8)
5. Introduction to Jupyter Notebooks
Understand different structures for holding data and the difference between stored and display formats. NPA (4,5,6) PDA (7,8)
6. Introduction to Python for Data Science Part 1
An introduction to the use of Python for Data Science projects including installing, importing and using Python, getting help and naming variables. NPA (5,6) PDA (7,8)
7. Introduction to Python for Data Science Part 2
Understanding Python data types and data structures that are important for data science, manipulating strings, creating and calling Python functions. NPA (5,6) PDA (7,8)
8. Manipulating Columns in a Dataset
Selecting, reordering and reformatting columns. NPA (5,6) PDA (7,8)
9. Manipulating Rows in a Dataset
Subsetting, filtering, sorting and deduplicating. NPA (5,6) PDA (7,8)
10. Creating new Variables by Calculation in Python Part 1
Creating new calculated variables, where the calculation that is used is the same for each row in the dataset. NPA (5,6) PDA (7,8)
11. Creating new Variables by Calculation in Python Part 2
Creating new calculated variables, where the calculation that is used is conditional. This lesson follows from Part 1. NPA (5,6) PDA (7,8)
12. Creating New Variables By Extracting and Combining in Python
Understanding what it means to extract and combine data to create new variables. NPA (5,6) PDA (7,8)
13. Summarising Datasets in Python Part 1
Summarising complete datasets and selected variables from a dataset, using summary calculations such as the total, count, min/max and mean. NPA (5,6) PDA (7,8)
14. Summarising Datasets in Python Part 2
Summarising within groups, using summary calculations such as the total, count, min/max and mean. NPA (5,6) PDA (7,8)
Understanding definitions of wide and long data and when to reshape data. NPA (5,6) PDA (7,8)
16. Practice Reshaping Datasets in Python
Reshape datasets from long to wide and wide to long formats. NPA (5,6) PDA (7,8)
17. Understanding Datasets in Python Part 1
What is metadata, the importance of a data dictionary, how to shape, size and format datasets and find the data types of variables in a dataset. NPA (5,6) PDA (7,8)
18. Understanding Datasets in Python Part 2
Identification of outliers and missing data in the data understanding step in the analysis process. NPA (5,6) PDA (7,8)
19. Practise Understanding Datasets in Python
This is a consolidation activity to give learners the chance to apply the data understanding skills they have learned in Data Understanding in Python Part 1 and Part 2. NPA (5,6) PDA (7,8)
Understanding what is meant by analysis and how to understand data through visual inspection. NPA (4,5,6) PDA (7,8)
21. Data Cleansing in Python Part 1
Introduction to data cleansing activities as part of the analysis steps, including importing datasets without importing metadata; dropping unrequired rows and variables; removing duplicate rows, and renaming variables. NPA (5,6) PDA (7,8)
22. Data Cleansing in Python Part 2
Part 2 of an introduction to data cleansing activities as part of the analysis steps, including handling missing values and outliers. NPA (5,6) PDA (7,8)
23. Advanced Data Cleansing in Python Part 1
Lesson outline to follow.
24. Advanced Data Cleansing in Python Part 2
This lesson is intended to follow the Advanced Data Cleansing in Python Part 1 lesson. NPA (5,6) PDA (7,8)
25. Practise Data Cleansing in Python
This is a consolidation activity to give learners the chance to apply the data cleansing skills they have learned in Data Cleansing in Python Part 1 and Part 2. NPA (5,6) PDA (7,8)
26. Creating Bar Charts in Python
Creation and modification of bar charts in Python using the seaborn package. NPA (5,6) PDA (7,8)
27. Creating Other Graphs in Python
Creation and modification of histograms, line graphs and scatter plots in Python using the seaborn package. NPA (5,6) PDA (7,8)