CC1 - HOSTING
Display charts in your own site.
Setting up a personal website using GitHub and embedding existing charts into the live webpage.
CC2 - BUILDING
Create your own visualisations
Creating charts using the Economics Observatory Data Hub and embedding them into the website.
CC3 - DEBATE
Use a visualisation in policy commentary
Producing charts linked to a policy topic and adding short written commentary alongside the visual evidence.
The United Kingdom has one of the highest rates of youth unemployment among OECD countries, despite efforts to address this problem.
Source: Own elaboration using data from the OECD Youth Unemployment Rate
The gap has widened in the latest year between the male and female NEET rate at age 16-24. Since the pandemic male rates have been higher than females, changing the trend which historically saw higher rates for females.
Source: Own elaboration using data from the Labor Force Survey 2024, GovUK
CC4 - REPLICATION
Re-create, then improve, someone else's chart.
Replicating an existing chart from a public source and producing an improved version.
CC5 - ACCESING DATA
Accesing data using an API
Collecting data through a live API and web scraping, cleaning the data, and visualising it on the website.
This chart uses data from the World Bank API.
The base URL is:
https://api.worldbank.org/v2/country/{countries}/indicator/{indicator}
To get the data, the API call specifies:
- A list of countries:
ind; mex; arg; usa; aus - The indicator code:
NY.GDP.TOTL.RT.ZS(Total natural resources rents as a % of GDP) - The output format:
format=json - Observations per page:
per_page=1000
Full API request:
https://api.worldbank.org/v2/country/ind;mex;arg;usa;aus/indicator/NY.GDP.TOTL.RT.ZS?format=json&per_page=1000
Source: Own elaboration using data from the World Bank, Total Natural Resources Rents (% of GDP)
Accesing data vía scraping
I scraped a Wikipedia table containing education index data by country from 1990 to 2019, cleaned the dataset, selected relevant countries, and reshaped the data into tidy (long-form) format.
Source: Own elaboration using data from the Wikipedia: Education Index
Colab Notebook: CC5. Scraping and cleaning data
CC6. LOOPS
Build a dashboard
Using JavaScript loops and Vega-Lite to automatically generate a small dashboard of related macroeconomic indicators.
Source: Own elaboration using data from the World Bank Open Data
Colab Notebook: CC6. Loops. Build a dashboard
CC7. MAPS
Base maps and choropleths
Creating coordinate maps and choropleths for Scotland and Wales using geographic data.
This choropleth map links business data from StatsWales to official ONS Local Authority District boundaries using LAD geographic identifiers, ensuring consistency between statistical and spatial units.
Source: Own elaboration using data from Stats Wales, Business demography by area and year
Coordinate map showing drug-related deaths by council area in Scotland, using binned colors and sizes to highlight regional disparities and mortality intensity.
CC8 - BIG DATA
Extracting a story from millions of prices
Working with large price datasets, reducing and aggregating the data, and visualising selected trends.
CC9 - INTERACTIVE CHARTS
Two interactive visualisations
Building charts with interactive elements such as sliders or selection tools to explore the data.
Source: World Bank, Global Findex Database 2025
Source: Own elaboration using data from the OECD/INFE 2023 International Survey of Adult Financial Literacy
CC10 - Advanced Analysis
Advanced Analysis and Machine Learning
Applying advanced analytical methods and machine learning techniques and visualising the results.
Source: Own elaboration using data from the World Bank API
Source: Own elaboration using data from the World Bank API
Colab notebook: Refers to chart 7 in this Colab notebook
Gen AI Disclosure
I used Claude and Gemini as a coding assistant for data cleaning in Python, creating charts in VegaLite, and for website style design.