UNITED NATIONSECONOMIC AND SOCIAL COMMISSION FOR ASIA AND THE PACIFICSTATISTICAL INSTITUTE FOR ASIA AND THE PACIFIC (SIAP)

e-Learning Course
Principles of Data Visualization for Official Statistics
and SDG Indicators
13 January – 24 February 2021

I. About the Course
This course introduces data visualization as a tool to produce high-quality graphics formonitoring official statistics and the Sustainable Development Goals (SDGs) indicators.
The course provides an opportunity for participants to explore and comprehend thetechniques of data visualization for data exploration as well as for data presentation.
Participants will discover, evaluate and apply the rules of data visualization on devoted
cases studies and also tackle the problem of visualizing complex data. 1 The course
proposes strategies for visualizing in multi-dimensions as well as presenting the practicalmethods for representing statistical indicators on maps or within dashboards.
The course is not based, nor does it focus, on any software. While some popular software
will be introduced in the course, participants will be free to use their favourite software for
their needs.
The e-course has been developed as an interactive training composed of 6 modules. Eachmodule is composed of several mandatory pedagogical activities, following a logicalstructure. Activities include videos, interactive videos, chats, live lectures, webinars,
document reading, exercises, homework polls and quizzes.
The course involves mandatory live lectures and webinars, scheduled once a week. The
learners are expected to interact during live sessions and through chat boxes embedded ineach module.

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The course does not cover advanced topics such as the visualization of networks and text.

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The course is hosted on the SIAP Learning Management System which contains a forum
for general questions and interactions with the SIAP’s lecturers and e-learning platform
administrators.

II. Target Audience
The course is designed for personnel working in the field of statistics, whose main
responsibilities include data collection, exploration, analysis or dissemination of SDG
indicators and related statistics. The course is also open to any personnel in charge of thedesign of data based dashboards and platforms, including people working in the IT or in
the communication sector.
Policy and decision makers, who are looking to understand the methodological details
behind data-based graphics and willing to interpret them accurately for evidence-based
decision-making are also in the target audience.
Participants are expected to have some experience in producing data-based graphics using
any type of software.

III.

Learning Objectives

At the end of the course, participants should be able to:-Identify data visualization objectives and limits
Comprehend data visualization as a visual language
Classify the different types of graphics that can be used to represent SDG
indicators
Apply data visualization rules to explore SDG statistics or to produce SDG-relatedgraphics
Compare statistical summaries and tables with their data visualizations counterparts
Elaborate strategies for visualizing multidimensional statistics used to produce
SDG indicators
Construct maps and alternative graphics for SDG indicators comparisons.
Apply dynamic data visualization principles for producing dashboards

IV.

Course Design and Content

The course is divided in 6 modules and will last for 6 weeks and will require approximately
3h of work each. The elements (slides and references) used for each activity will beavailable for download in pdf format. In the last module, we propose optional activities
based on popular data visualization software and to free online tools that will not serve forthe evaluation.
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Coverage

1 - “ What is Data Visualization?”-2 - “Data Visualization rules to apply to SDG
indicators”

3 - “Choosing the right data visualization for theright SDG indicator“-Data visualization objectives and limits
Features and goals of popular graphics
Types of graphics to represent SDG
indicators
Design of a graphic based on a data set-Badly designed data visualization
Data visualization rules
Misleading graphics
Criteria for an effective visualization-

Choice of a data visualization
Visual perception
Statistical tables with their data visualizations
counterparts
Processes leading to an efficient datavisualization

4 - “ Comparing many or complex indicators”

5 - “ Maps”-

Visualization in many dimensions
Applied data visualization rules
New types of graphics
Strategies for visualizing in multi-dimension-

Importance of maps for SDG indicators
comparisons
Limitations of statistical maps
Alternatives to maps for representing SDGindicators

6 - “ Interactive and dynamic data visualization”-3

Principles of dynamic data visualization
Tools and platforms for creating interactive
data visualizations
Features of dynamic visualization

V. Evaluation
The evaluation will be based on the learner’s ability to apply the methods and training
materials described during the course, as well as on their capacity to recall the main
outcome of each module. The final grade will be based on two mandatory assessments
exercises:
1) An individual data-based project to be submitted before the end of the course.
2) Four Multiple Choice Questions (MCQ)-like tests to complete at the end of eachmodule.

The individual data-based report counts for 40% of the final grade, the weekly tests will
therefore account for the remaining 60% of the final grade. Participation to the various
chats and Q&A will provide bonus points.
To succeed in the course participants must have at least a final score of 70/100, and comply
with the following conditions:- Follow all of the mandatory activities- Complete all the weekly assessments- Return an individual data-based project in due time
- Grade at least one data-based project from peers in due time
Participants must complete the end-of-course evaluation to receive a certificate.

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