Presented at LILAC 2022

1. Teaching Data Literacy and Data
Visualization as One-Credit Course
Tatiana Usova, CMU-Qatar
LILAC 2022/Manchester, UK

2. SOME CONTEXT
Before
Georgetown University in Qatar (GU-Q)
Undergraduate degree B.S. in Foreign Service
399 students
Now
Carnegie Mellon University in Qatar (CMU-Q)
Undergraduate degree B.S. in Biological
Sciences, Business Administration, Computer
Science and Information Systems
439 students
Education City, Doha, Qatar
12 square kilometer campus housing branches of six
American universities where students receive Western
education in a Middle Eastern setting.
2
Image source: https://mosqpedia.org/en/mosque/154

3. A PICTURE IS WORTH A THOUSAND WORDS
3
WHY?
Information is the power.
Data is the new oil in the digital economy . – WIRED, 2014
https://www.thejobnetwork.com/10-in-demand-skills-need-get-hired-fast/
Example
The ability to discover, evaluate, and use information
and data is a core competency of CMU students

4. ACTIVITY
Pair with your neighbour and
discuss what the term “data
literacy” stands for.
Post your answer to menti.com
(Code: 1048 1614)
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menti.com

5. “
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“a suite of data acquisition-, evaluation-, handling-, analysis- and
interpretation-related competencies” (Prado and Marzal, 2013, p. 124).
“specific skill set and knowledge base that enables us to transform data
into information and ultimately into actionable knowledge” (Koltay,
2017, p.9)
DATA LITERACY

6. PROBLEM: Data Visualization Literacy is Low
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(Börner et al., 2016)

7. “
7
DATA VISUALIZATION LITERACY
NAS Colloquia, 2018, 7’51”

8. PROJECT ROADMAP
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1 3 5
6
4
2
Conversations with alumni
and environmental
scanning, spring 2019
Curriculum committee
approval,
October 2019
Online teaching: one-
credit course (Tableau),
Jan-Feb 2021
Course design,
summer 2019
Pilot : in person one-credit
course (Venngage),
Jan –Feb 2020
Hybrid: two one-credit
courses (Tableau),
Jan-Feb 2022

9. Course structure and
learning objectives
9

10. COURSE LEARNING OUTCOMES
We prepare students to be responsible readers, interpreters and
communicators of data. At the end of the course they
◉ Understand effective design principles and be able to apply them in
visual communication.
◉ Grasp the concept of a dataset and know where to find and gather
datasets as well as how to interpret them and generate informative
visualizations.
◉ Use digital tools to organize and display quantitative and qualitative
data.
◉ Incorporate effective visual design and data visualization into personal
and professional self-promotion.
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11. PILOT COURSE SETUP
11
1-credit course 12,5 contact hours
(2hrs/week,
in person)
14 students in
total
active and
collaborative
learning

12. OUTLINE OF THE COURSE
12
Lesson Objectives Activities
1. Data Visualization
and Basic Design
Principles
Understand core concepts of effective
design.
Develop the ability to interpret and
critically analyse charts.
Generate charts in Google Sheets that
accurately convey the meaning of data.
In class: evaluate and critique assigned graphs from the
news media, spot misrepresentations of information.
Homework: create the best-suited graphs in Google
Sheets using provided data; complete online quiz.
2. Data discovery and
processing: how to find,
treat and cite data
Locate and utilize credible datasets.
Evaluate the quality of data. Treat, clean
and organize data.
Create multivariable charts.
Properly attribute data sources.
In class: clean a given dataset and prepare a
multivariable visualization.
Homework: explore a list of data portals; pick one
dataset, clean and organize it; create easy to
understand graphs.

13. 13

14. 14
Which of the
following bar charts
has the best design?
ACTIVITY

15. 15
IN CLASS ASSIGNMENT – DATA CLEANING

16. OUTLINE OF THE COURSE (continued)
16
Lesson Objectives Activities
3. Charts, Graphs, and
Maps. Using Venngage
Software
Understand the mechanics of the
Venngage software.
Generate various types of visualizations
(graphs, timelines and maps) using the
Venngage tool.
In class: use Venngage to create graphs and geo
visualizations from provided datasets.
Homework: use Venngage and create a graph and a
geo-visualization from a real-world dataset.
4. Storytelling with
Data
Sharpen skills of information
presentation in Venngage.
Create infographics.
Become familiar with Venngage
templates.
In class: create infographics in Venngage using
provided data.
Homework: choose a dataset and create an
infographic in Venngage; conceptualize the final
project, conduct exploratory data research and
analysis, write a short synopsis of the idea.

17. 17
INFOGRAPHIC
a fun and quick way to learn
about the topic without a ton
of heavy reading.
Websites with datasets used:
• UNData
• The World Bank - Data Catalog
• Qatar Ministry of Development
Planning and Statistics

18. OUTLINE OF THE COURSE (continued)
18
Lesson Objectives Activities
5. Tools for
Independent Learning
and Skill Building
Understand the importance of the
LinkedIn profile and identify ways to
enhance an online web portfolio.
Build a data visualization dashboard in
Venngage.
In class: present the “elevator pitch” project proposal,
seek feedback and ideas for improvement.
Homework: prepare the final project—visual
presentations of data applying data visualization best
practices.
6. Final projects
presentation.
Wrap-up and
course
assessment
Present a storyboard with visualizations
of insights gained from data analysis.
Effectively communicate the connection
between visualizations following the
narrative thread.
In class: present the project;
participate in a course wrap-up discussion;
complete the course survey.

19. STUDENTS’ FINAL PROJECTS
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20. ASSESSMENT AND
CONCLUSIONS
Data Literacy imperative Leadership role of librarians Upskilling

21. FUTURE DIRECTIONS
continue as a one-credit
course with improved
instruction
offer two one-credit
bearing courses: one at a
basic level and one more
advanced
teach a three-credit course
with enhanced content,
and more sophisticated
technology tools (e.g.
Tableau)
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collaborate with faculty on
course-integrated
instruction, add data
visualization to course
content

22. REFERENCES
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Börner, K., Maltese, A., Balliet, R. N., & Heimlich, J. (2016). Investigating aspects of data
visualization literacy using 20 information visualizations and 273 science museum
visitors. Information Visualization, 15(3), 198–213. https://doi.org/10.1177/1473871615594652
Koltay, T. (2017). Data literacy for researchers and data librarians. Journal of Librarianship and
Information Science, 49(1), 3–14. https://doi.org/10.1177/0961000615616450
NAS Colloquia. (2018, March 28). Data visualization literacy—Katy Borner. [Video]. Youtube.
https://www.youtube.com/watch?v=N5wYoIR1K_4
Prado, J. C., & Marzal, M. Á. (2013). Incorporating data literacy into information literacy programs:
Core competencies and contents. Libri, 63(2), 123–134. http://dx.doi.org/10.1515/libri-2013-0010
Usova, T., & Laws, R. (2021). Teaching a one-credit course on data literacy and data
visualisation. Journal of Information Literacy, 15(1), 84-95. https://doi.org/10.11645/15.1.2840

23. THANKS!
Any questions?
23

24. Tatiana Usova
Director of the Library,
Carnegie Mellon University in Qatar
Email: tusova@qatar.cmu.edu
Telephone: 974 4454 8403
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