Data Visualization

Code:

CC4056

Acronym:

CC4056

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2022/2023 - 1S

Active?	Yes
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Master in Bioinformatics and Computational Biology

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
E:BBC	5	PE_Bioinformatics and Computational Biology	1	-	6	42	162
M:BBC	11	The study plan since 2018	2	-	6	42	162
M:DS	21	Official Study Plan since 2018_M:DS	1	-	6	42	162

Teaching language

Portuguese and english
Obs.: All course material will be given in English

Objectives

This course introduces the concepts of Data Visualization with a focus on Data Science and Visual Analytics. It spans a multi-disciplinary domain that combines data visualization with machine learning and automated techniques to help people make sense of data.

Students are introduced to the design of visual representations that support tasks that take the user from raw data into insights. Topics include basic concepts of information visualization; visual analytics of evolving phenomena; analysis of spatial and temporal data sets; visual social media analytics; and the visual analytics of text and multimedia collections.

Students will prototype visual analytics applications using existing frameworks and libraries, coupling machine learning and visualization methods. Students will gain competency in performing data analysis through visualization tasks in different application domains.

In particular:

• Create graphs appropriate to the type of context and problem to be explored
• Create and enhance graphics using R and Python tools
• Integrate graphics developed in R / Python into interactive environments.
• Design and develop a Big Data access dashboard for interactive manipulation of multiple graphs.

Learning outcomes and competences

• Understand and know how to apply the key concepts involved in creating an R (mainly) and Python chart using your latest libraries.


• Explain the standard methods of automatic learning and data mining and their particularities for visualization purposes.


• Explain the methods of visualization and visual analysis and the differences between them.


• Describe the limits of machine learning and data mining methods to solve data analysis problems and when visualization methods are possible.


• Create elegant and simple strategies for solving data analysis problems by integrating visualization with machine learning and data mining methods and justifying choices


• Perform data analysis tasks on large data sets to visually discover data patterns and formulate hypotheses

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Programming experience, ideally with R or Python.

Program

• Data representation and transformation


• Data visualization overview


• Visual analysis: motivation, models, challenges and analytical thinking.


• Key elements in graphical data visualization


• Chart types, problem fit, and graphic integrity


• Graphing for univariate and multivariate data


• Conditional, temporal and multi-aggregate value graphs


• Visual data mining: dimensionality reduction for cluster results visualization and visualization


• Visual analysis for text and multimedia collections


• Visual analysis for spatial and temporal data sets


• Panels for interactive manipulation of simultaneous graphic views

Mandatory literature

M. O. Ward, G. Grinstein, D. Keim; Interactive Data Visualization: Foundations, Techniques, and Applications, CRC Press, 2015 (Main book)
Hadley Wickham; ggplot2: Elegant Graphics for Data Analysis. ISBN: ISBN-10: 0387981403 (Main book)

Complementary Bibliography

L. Wilkinson; The Grammar of Graphics, 2005. ISBN: ISBN-10: 0387245448
Eduard Tufte; The Visual Display of Quantitative Information, 2001. ISBN: ISBN-10: 0961392142
William Cleveland; The Elements of Graphing Data . ISBN: ISBN-10: 0963488414 (Historical perspective)

Comments from the literature

Main articles used in the course:

• Sacha, A. Stoffel, F. Stoffel, B. C. Kwon, G. Ellis and D. A. Keim, "Knowledge Generation Model for Visual Analytics," in IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 1604-1613, 2014.
• Endert, W. Ribarsky, C. Turkay, B.W. Wong, I. Nabney, I. D. Blanco, and F. Rossi, “The State of the Art in Integrating Machine Learning into Visual Analytics”, Computer Graphics Forum, vol. 36, pp. 458-486, 2017.

Teaching methods and learning activities

During the lectures, it will be used the expository method, being presented an organized view of the topics of the program, as well as practical examples of their application.

There will be monitoring of the implementation of practical work and the creation of execution milestones.

Some lectures will include Individual and class analysis of scientific articles and texts as well as presentation and discussion of classwork.

Each lab class will be divided into a very short synthesis of the previous theoretical part, followed by a discussion, and then a practical part to apply the knowledge in real-world cases.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Apresentação/discussão de um trabalho científico	10,00
Exame	30,00
Teste	10,00
Trabalho escrito	10,00
Trabalho prático ou de projeto	40,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Apresentação/discussão de um trabalho científico	3,00
Elaboração de projeto	10,00
Estudo autónomo	47,00
Frequência das aulas	42,00
Trabalho laboratorial	60,00
Total:	162,00

Eligibility for exams

Execute all mandatory assessment items:

• Mini-test (MT)


• Projet proposal (PP)


• Project development (DP)


• Projet final presentation (AF)


• Final exam (EF)

Final exam with a grade equal to or greater than 40%.
Final grade equal to or greater than 9.5.

Calculation formula of final grade

Final grade = 0,1*MT + 0,1*PP + 0,4*TDP + 0,1*AP + 0,3*EF

Internship work/project

The course project is the student opportunity to develop skills and expertise in Data Visualization. The course project is designed to take students through all stages of development, from coming up with a new idea for a data visualization to a visual analytics application to designing, implementing, testing, and demonstrating the application. For manageability, the project is divided into different deliverables: proposal, implementation, and project demonstration.

Classification improvement

Only the on the "Exame Final" component.