Veidlapa Nr. M-3 (8)
Study Course Description
Data Visualisation and Storytelling
Course Code
SZF_184
Branch of Science
Economics and Business
ECTS
5.00
Target Audience
Management Science
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Sergio Andres Uribe Espinoza
Structure Unit Manager
Ieva Puzo
Structural Unit
Faculty of Social Sciences
Contacts
Dzirciema Street 16, Riga, szf@rsu.lv
About Study Course
Objective
The course "Data Visualization and Storytelling" aims to develop students' data visualization and storytelling skills. The course provides knowledge on creating informative and visually appealing visualizations and structuring stories to communicate data insights effectively. Students will learn data analysis, visualization, and communication methods using the R programming language and other tools.
Preliminary Knowledge
Students do not need prior knowledge in programming or data analysis. Basic familiarity with spreadsheets, such as Excel or Google Sheets, may be helpful but is not required. The course is suitable for beginners and provides a foundation in data visualization and storytelling using the R programming language and the ggplot2 package to learn the grammar of graphics.
Learning Outcomes
Knowledge
1.Understand the principles of data analysis and visualization, focusing on storytelling and effective communication.
2.Recognise the importance of orderly data and their role in promoting insights.
3.Learn how to clean and process data to provide context for data.
4.Will acquire knowledge of advanced ggplot2 capabilities for visualization development.
Skills
1.Perform exploratory data analyses to identify trends, patterns, and anomalies.
2.Will be able to choose suitable visualisation techniques to effectively communicate insights to different lecture theatres.
3.Will be used to manipulate and visualize R and ggplot2 data according to graphic grammar.
4.Integrate elements of storytelling into visualizations to build influential stories.
5.Produce reproducible and well-structured messages using R Markdown.
Competences
1.Evaluate data quality and make adjustments to enhance meaningful insights.
2.Create visualizations that clearly and effectively reveal conclusions in a structured way.
3.Develop critical thinking by prioritising data structure, lecture theatre attention and storytelling principles.
4.Demonstrate the ability to create a unified data story that combines analysis, visualization, and storytelling.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
-
|
-
|
|
Exploration of scientific literature |
||
|
2.
Data analysis |
-
|
-
|
|
Collection and analysis of data |
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|
3.
Projects |
-
|
-
|
|
Preparation and presentation of individual and group projects. |
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Final draft |
60.00% from total grade
|
10 points
|
|
Students create and present a visualization project based on data, integrating analysis, visualization, and storytelling. Quality and execution of projects |
||
|
2.
Participation |
40.00% from total grade
|
10 points
|
Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Introduction to Data Visualization and Storytelling
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Introduction to Data Visualization and Storytelling
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Fundamentals of the Grammar of Graphics with ggplot2
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Fundamentals of the Grammar of Graphics with ggplot2
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Choosing the Right Visualization
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Choosing the Right Visualization
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Fundamentals of Data Wrangling: Providing Context to the Data
|
-
Class/Seminar
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Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Fundamentals of Data Wrangling: Providing Context to the Data
|
-
Lecture
|
Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Simplifying Visuals and Removing Clutter
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Simplifying Visuals and Removing Clutter
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Directing Audience Attention with Design
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Directing Audience Attention with Design
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
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Creating Visual Narratives
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
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Creating Visual Narratives
|
-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
|
Auditorium
|
2
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Topics
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Reporting and Sharing Visual Stories
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Reporting and Sharing Visual Stories
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Advanced Customization in ggplot2
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Advanced Customization in ggplot2
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Capstone Project: Telling a Data Story
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
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Capstone Project: Telling a Data Story
|
Total ECTS (Creditpoints):
5.00
Contact hours:
40 Academic Hours
Final Examination:
Exam
Bibliography
Required Reading
1.
Storytelling with data - Cole Nussbaumer KnaflicSuitable for English stream
2.
Data organization in spreadsheets - Broman & WooSuitable for English stream
3.
How to Share Data for Collaboration - Ellis & LeekSuitable for English stream
4.
R for Data Science (2e) - WickhamSuitable for English stream
Additional Reading
1.
Points of view: Elements of visual style. Nat. Methods 10, 371–371Suitable for English stream
2.
Points of view: Labels and callouts. Nat. Methods 10, 275–275Suitable for English stream
3.
Points of view: Axes, ticks and grids. Nat. Methods 10, 183–183Suitable for English stream
4.
Points of view: Storytelling. Nat. Methods 10, 687–687Suitable for English stream
5.
Points of view: Multidimensional data. Nat. Methods 10, 595–595Suitable for English stream
6.
Points of view: Plotting symbols. Nat. Methods 10, 451–451Suitable for English stream
7.
Points of View: Bar charts and box plots. Nat. Methods 11, 117–117Suitable for English stream
8.
Design of data figures. Nat. Methods 7, 665Suitable for English stream
9.
Data visualisation for People in government who design and publish chartsSuitable for English stream
Other Information Sources
1.
Fundamentals of Data VisualizationSuitable for English stream
2.
Data Visualization: A practical introductionSuitable for English stream
3.
BBC Visual and Data Journalism cookbook for R graphicsSuitable for English stream