DS207BKK
From data to knowledge: interpretation, visualisation, presentation
Faculty
Juan Galeano
Researcher at the Center for Demographic Studies (CED) in Barcelona, Spain
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
In today's data-driven world, technology has made it easier than ever to generate and collect information from nearly every aspect of our personal, social, and professional lives. Interpreting this data empowers us to make informed decisions, uncover trends, generate ideas, and even challenge or validate our assumptions. This course will teach you effective data visualisation techniques, helping you not only understand your findings but also communicate them clearly—whether in a research paper, a business presentation, or a website. Along the way, we'll develop a critical approach to data, learning to recognise its strengths and limitations.
Learning highlights
- Participants in this course will learn to select the most appropriate visual representation based on the specific questions they aim to address with data.
- They will gain insights into why certain types of visualisations are more effective in particular contexts.
- Additionally, students will become familiar with a range of tools for data exploration and presentation, including techniques for creating dynamic and interactive visualisations.
Course outline
15 classes
Introduction to the module
Course goals, assignments and evaluation.
Brief introduction to the history of data visualisation.
How AI is reshaping the field of data visualisation.
Recommended reading: Graphics with a cause.
Data provider: Our World in Data.
Dataset: Titanic Dataset.
Hands-on exercise: Ask relevant questions to a dataset and visualise the results.
Data foundations
Data collection processes.
Types of variables.
Dataset formats: width vs long.
Where to get data?
AI generated data.
Recommended reading: The Politics of Large Numbers (Chapter 1).
Data provider: ASEANstats (ASEAN Statistics Division).
Dataset: Gapminder.
Hands-on exercise: Basic data manipulation, Pandas (Python) vs Tidyverse (R).
Graph Types I
Core principles of data visualisation.
Basic elements of a graph.
Graphs types: Pros and Cons.
Daily sins of data visualisation.
AI generated graphs.
Recommended Reading: Show me the numbers (Chapter 3).
Data provider: FiveThirtyEight Data.
Dataset: Gender pay gap.
Hands-on exercise: Building some basic graphs using plotnine (Python) or ggplot2 (R).
The Visual mind
How does visual perception work?
Gestalt principles for data visualisation.
Goals of data visualisation.
Data Visualisation vs Information Design.
Recommended Reading: Feminist data visualisation.
Data provider: World Bank Data (focus on SE Asia).
Dataset: Life expectancy among the G7 countries.
Hands-on exercise: Datavis challenge.
Integration & Wrap-Up
Define datasets for final projects.
Brainstorming: What can we ask for these datasets?
Basic principles of story-telling.
Review key concepts of the week.
Recommended Reading: Data Humanism.
Data provider: IPUMS-I.
Dataset: World Happiness Report.
Mini Hackathon: In groups you explore and visualise the WHP.
Exploratory data analysis (EDA)
Role of EDA in data science.
Using summary statistics and visualisation together.
AI for quick EDA code generation (strengths/risks).
Data provider: Thailand Open Government Data portal.
Dataset: Thai censuses 1970-2000.
Hands-on: Exploration and visualisation with R/Python.
Graph Types II
Heatmaps, treemaps, bubble charts, ridgeline plots.
Choosing between complexity vs clarity.
AI suggestions for “exotic” graphs: critique them.
Recommended reading: How Charts Lie (selected chapter).
Data provider: UNHCR Data Portal.
Dataset: Refugees and displacement in Asia-Pacific.
Hands-on: Visualising migration/displacement trends.
Let’s go Spatial I
What is a shapefile?
Spatial data formats.
Coordinate Reference Systems (CRS).
Discrete maps.
Data Provider: GADM (Database of Global Administrative Areas).
Dataset: Abortion Status in the US.
Hands-on exercise: Thailand regions and provinces on a map.
Let’s go Spatial II
Long/Lat CRS.
Choropleth maps.
Heatmaps.
Map tile providers.
Data provider: Inside Airbnb.
Dataset: Airbnb in Bangkok.
Hands-on exercise: Heatmap of Airbnbs in Bangkok.
Let’s go Spatial III
Geocoding information.
Getting routes and distances between multiple points.
The Open Street Maps Project.
The Open Buildings Project.
Leaflet: an open source JavaScript library for Interactive maps.
Data provider: Open Street Maps.
Hands-on exercise: Using OSM features and creating interactive maps.
From static to interactive visualisations
How to make a good final presentation.
Plotly.
Shiny apps.
Flourish.
Datawrapper.
Hands-on exercise: you work on your final project.
Work on final presentations
Mentoring and assistance for final presentations
You discuss with the instructor your set of data visualisations.
Hands-on exercise: you work on your final project.
Work on final presentations
Mentoring and assistance for final presentations.
First draft of your final presentation.
Hands-on exercise: you work on your final project.
Work on final presentations
Mentoring and assistance for final presentations.
Second draft of your final presentation.
Hands-on exercise: you work on your final project.
Presentation of final projects
Final presentations.
Summary of the module.
Farewell aperitif invited by the instructor.
Prerequisites
Basics of working with data (e.g. spreadsheet software, R, python pandas).
Basics of statistics (descriptive statistics: mean, median, variance, standard deviation, regression)
Basics of programming (preferably R or Python).
Methodology
Theoretical lectures
Learning-by-doing activities
Grading
Juan Galeano is a researcher at the Center for Demographic Studies (CED) in Barcelona, Spain, where he is currently responsible for Data Infrastructures within the Intergenerational Coresidence in Global Perspective: Dimensions of Change (CORESIDENCE) project, supported by an Advanced Grant from the European Research Council. He is also the co-principal investigator of the project Bringing Social and Computational Sciences Together: Unravelling Household Composition and Change through the Implementation of the First World-Scale Multilevel Analysis, a collaborative initiative between CED and the Barcelona Supercomputing Centre (BSC). Previously, Juan was a postdoctoral researcher at the National Centre of Competence in Research (NCCR) in Geneva, Switzerland. He holds a PhD in Demography, and his research lies at the intersection of demography, human geography, and sociology. Juan is passionate about spatial data, digital cartography, and open-source technologies.
See full profileApply for this course
From data to knowledge: interpretation, visualisation, presentation
by Juan Galeano
Total hours
45 Hours
Dates
Jan 12 - Jan 30, 2026
Fee for single course
€1500
Fee for degree students
€750
How to secure your spot
Complete the form below to kickstart your application
Schedule your Harbour.Space interview
If successful, get ready to join us on campus
FAQ
Will I receive a certificate after completion?
Yes. Upon completion of the course, you will receive a certificate signed by the director of the program your course belonged to.
Do I need a visa?
This depends on your case. Please check with the Spanish or Thai consulate in your country of residence about visa requirements. We will do our part to provide you with the necessary documents, such as the Certificate of Enrollment.
Can I get a discount?
Yes. The easiest way to enroll in a course at a discounted price is to register for multiple courses. Registering for multiple courses will reduce the cost per individual course. Please ask the Admissions Office for more information about the other kinds of discounts we offer and what you can do to receive one.