Logo UAB

Data Visualisation

Code: 104365 ECTS Credits: 6
Degree Type Year Semester
2503758 Data Engineering OB 3 2


Enric Marti Godia

Teaching groups languages

You can check it through this link. To consult the language you will need to enter the CODE of the subject. Please note that this information is provisional until 30 November 2023.


Judit Chamorro Servent

External teachers

Guillermo Marin Getino


It is recommended that students have or review knowledge and concepts of:
  • Graph analysis and information retrieval.
  • Model adjustment and optimization.
  • Computational learning.
  • Statistical analysis.

Objectives and Contextualisation

The course aims to give students elements to find the best data visualization technique that allows better analysis of data, or allows a specific type of analysis.
KNOWLEDGE: At the end of the course the student must be able to:
  • Know and understand the basic systems of visualization and exploratory analysis of data.
    Know and understand the main visualization models.
    Know and understand current libraries for data visualization (Google Charts, JQuery plug-ins, D3.js)
SKILLS: It is intended that students acquire the following skills:
  • Given a set of data, know how to use the most appropriate visualization system and model for data visualization that allows the analysis to be performed.
  • Given a data set, know how to use one of the data display libraries to get the best graphical results.
  1. Know and understand the basic systems of visualization and exploratory analysis of data.
  2. Know and understand the main visualization models.
  3. Know and understand current libraries for data visualization (Google Charts, JQuery plug-ins, D3.js)
  4. Know how to apply the most optimal data visualization techniques for data analysis based on a specific case.
  5. To Work cooperatively.


  • Search, select and manage information and knowledge responsibly.
  • Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
  • Understand visualisation techniques for big data and be able to select the most adequate for analysis.

Learning Outcomes

  1. Identify the usability and interactivity needs of a data-visualisation method and be able to produce a new version of the visualisation that improves on the above aspects.
  2. Relate statistical and artificial-intelligence techniques to the visualisations that best express the underlying information.
  3. Search, select and manage information and knowledge responsibly.
  4. Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.


  1. 1. Visualization Introduction (2 hours)


    • What is it and per a què serveix?
    • Problem
    • Aplications and examples

    2. Visual Codifition Visual (2 hours)

    • Data types and graphic typpes
    • Visual channels
    • Choose graphic information according his function

    3. Visual perception and dessign (2 hours)

    • Visual perception
    • Dessign fonamentals
    • Typography
    • Colour

    4. Visual precision (2 hours)

    • Common errors in visualization
    • Honestedat visual
    • Bones i dolentes pràctiques en estadística

    5. Data treatment to visualization (4 hours)

    • Uncertainity i Error
    • Dimensionality reduction
    • Transformations
    • Data massatge
    • Computation ans rellevant metrics election

    6. Advances visualization systems (4 hours)

    • Multiple variables and multiple dimensions
    • Nets
    • Dades 3D
    • Vector fields
    • Scientific Visualization
    • Mapes

    7. Interaction. (4 hours)

    • Animation
    • Dashboards

    8. Metodologies de treball (2 hores)

    • Storytelling anb data
    • Planification strategies
    • User centered dessign

    9. Validatión (2 hores)

    • Usability


LEARNING MANAGEMENT SYSTEM (LMS):  Caronte (http://caronte.uab.cat, degree of Enginyeria de Dades (Data Engineering), subject Visualització de Dades (Data Visualization), curs VD Curs 2020-21).

The student learning process is based on two types of face-to-face activities (Theory and Seminars). Attendance at online theory and seminar classes is NOT REQUIRED, but YES HIGHLY RECOMMENDED.


Theory classes are taught through master classes with transparencies and blackboard. All course material will be accessible through the Virtual Platform. Some of the theory sessions will be dedicated to solving exercises posed in the same classroom with the aim of delving into the most relevant concepts and teaching students to identify and analyze typical errors.


In the seminar sessions we will alternate two activities: problems (7 sessions) and practices (6 sessions).

In the PROBLEMS sessions we will propose exercises to solve on the following topics:

  • Simple graphs (bars, lines, boxplots, scatter, etc.) - Tool: R
  • Complex graphs (parallel coordinates, small multiples, networks, etc.) - Tool: R
  • Other tools (commercial and free) - Tools: Tableau Public, DataWrapper, Gephi
  • Network visualization
  • Map display
  • Multivariable and multidimensional data graphs
  • Animated and interactive graphics - Tools: R, 3D viewer
  • Presentation (Titles, scales, captions and annotations) - Tools: R and design software such as Inkscape
  • Dashboards - Tools: Table vs. R

In these problem sessions the exercises to be solved are proposed, which the students do with the tutoring of the teacher. All documentation (script statements, problem results) of the seminars is available on the Virtual Platform.

The PRACTICE sessions aim to train students to study and define the different views of data sets and assess the advantages and disadvantages. We will propose two practical works:

  1. Treatment with a default date set. Given a date set provided by teachers, it is necessary to clean, prepare and visualize with exploratory graphs, justifying which is the best.
  2. Treatment with a free date set. From a date set chosen or generated by the students, clean, prepare and visualize with exploratory graphs, justifying which is the best.

They will be carried out in groups of THREE people and for each student a dedication of 12 hours will be required in 6 sessions on line supervised by professor and 33 hours of work of the student of individual or group way of preparation of the practices, work of the student without the tutelage of the teacher and the preparation of reports.

Attendance at online internship sessions supervised by a teacher IS MANDATORY. Cases of absence will be studied in the duly justified internship sessions.

The student prepares the internship work explained in the internship statement. This preparation must be prior to attending the corresponding online internship session. The work is done by attending the online internship sessions at the established times.

The regulations for the delivery and evaluation of internships can be found in the regulations document available on the Virtual Platform. The work of internships will be evaluated and an individual exam will be held in the delivery of each internship.

Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.


Title Hours ECTS Learning Outcomes
Type: Directed      
Explanation and problem solving 14 0.56 3, 1, 2
Lectures 26 1.04 1, 4, 2
Resolution and practical exercises 12 0.48 3, 1, 2
Type: Supervised      
Case studies 4 0.16 3, 1, 2
Type: Autonomous      
Explanation and problem solving 30 1.2 3, 1, 2
Individual study 26 1.04 1, 4, 2
Resolution of practical exercises 30 1.2 3, 1, 2


The subject assessment is done by continous evaluation, based in two indicators: Theory and seminars:practices.


There will be two individual written exams, with a weight of 50% each on the Theory Note. The first test (Par1) will be done approximately in the middle of the semester and will evaluate the theoretical concepts explained to date and the ability to solve visualization problems similar to those worked on in the seminar sessions: problems. The second test (Par2) will be carried out at the end of the semester and will evaluate the concepts explained so far, of theory and the ability to solve problems similar to those worked on in the seminar sessions: problems.

Recovery test: In case the theory note does not reach the approved, students may submit to a review of recovery on the contents of the partial examination suspended.



The practice assessment (notes Pr_1, Pr_2, Pr_3) is done by means of two activities:

  • Assessment of the report written and the practical work done by the students (grades D1, D2), group grade.
  • Individual written exam carried out on each practice (grades E1, E2).

Examen de recuperació: En cas de que no s’assoleixi no s’assoleixi el 5 en qualsevol de les proves, l’alumne té una nova oportunitat d’avaluació amb el mateix format i normes.

The practice work mark in the second part of the zsaemestre (after first exam) consists in an Optative Practice exercice (EPO)  that will be proposed in a theiry or seminar session eithout any advice and it willbe given in the classroom. The value of this mark is 1 point maximum. This mark will be added to second exam mark.



The final grade of the subject is calculated in the following way (between square brackets the minimum marks to make half):

  • Pract = PG + PI
  • Par1 = ExPar1 + Pract [ExPar1 >=4]
  • Par2 = ExPar2 + EPO      [ExPar2 >=4]
  • Theory Mark = 0,5 * (Par1) + 0,5 * (Par2)  [Par1, Par2 >=5]
  • Project Mark = 0,4 * Prj_1 + 0,6 * Pr_2    [Pr_1, Pr_2 >=5]

FINAL SUBJECT MARK = 0,5 * Theory Mark + 0,5 * Project Mark                              



Marls will be published in LMSl.


  • In order to consider approved any of the parts (theory and practices) a minimum of 5 must be obtained, that is to say Theory Mark> = 5 and Practice Work Mark > = 5.
  • PERMANENCE IN THE EXAMS: In any exam the student will not be able to leave the classroom during the first 45 minutes of the exam time, except for justified reasons or by indication of the teaching staff.
  • In allretakes of assessable activities (partial or practice exams, RV project, etc.), the maximum mark will be 8.
  • IMPROVING AN EXAM MARK: Students may take a retake to improve an exam score. Failure to pass the exam means waiving the previous exam mark obtained. In case it does not deliver it means that it accepts the previous exam mark obtained.
  • To calculate the finalgrade of the subject, the theory and practices work marks must be approved.
  • NOT EVALUABLE: In the case of not reaching the minimum required in any of the assessment activities, if the calculation of the final grade is equal to or greater than 5, a 4 note will be placed on thefile.
  • NOT PASS: In the event of not reaching the minimum required in any of the evaluation activities, if the calculation of the final grade is equal to or greater than 5, a grade of 4 will be added to the file.
  • MH: The student with a grade equal to or higher than 9 points will be able to obtain an MH, at the discretion of the teacher. As the number of MHs cannot exceed 5% of the enrolled students, they will be awarded to those with the highest final grades, at the discretion of the teacher. Students who have taken all the assessment tests in the first call will be awarded.
  • REPEATING STUDENTS: No approved part is approved (theory, problems, practices) from one academic year to another.
  • IMPORTANT FOR ALL STUDENTS: It is important to enroll in Caronte, because the materials of the subject are published, the deliveries are made and the notes of the subject are published. You must also sign up for a group of internships. At the start of the course you must sign up for Caronte in the Interactive Graphical View course "VGI Curs 2021-22" course. If this is the first time you enter Caronte, you must provide your NIU, password, first and last name and a photo ID in JPG format. Caronte is a manager different from the UAB Virtual Campus, with the need for passwords to be the same.
  • Students who have a mark equal to or greater than 9 points can obtain a MH. As the number of MH can notexceed 5% of students enrolled, students who have the highest final grades will be awarded, at the discretion of the teacher.


  • Partial Exam: Dates at the end of the teaching guide, published in the LMS and provided to students on the first day of class.
  • Recovery Exam: According to the academic calendar ofthe Schoolof Engineering.
  • Exams and deliveries of practices: Dates in the rule of practices, that will publish inthe Virtual Platform.

The delivery of notes will also be done through Caronte. At the beginning of the course, you must sign up in the LMS. If its your first time in Caronte, you must to create an account providing your NIU, password and a photo card in JPG format. Carontees is a manager different from the UAB Virtual Campus, which means that passwords may not the same.

Notwithstanding other disciplinary measures deemedappropriate, and in accordance with the current academic regulations, irregularities committed by a student that may lead to a variation of the qualification will be classified by zero (0). Assessment activities qualified in this way and by this procedure will not be recoverable. If it is necessary to pass any of these assessment activities to pass the subject, this subject will be suspended directly,without opportunity to recover it in the same course. These irregularities include, among others:

  • The total or partial copy of a practice,report, or any other evaluation activity
  • Let copy
  • Present a group work not done entirely by the members of the group.
  • Present as own materials produced by a third party, although they are translations or adaptations, and generally works with non-original and exclusive elements of the student.
  • Have communication devices (such as mobile phones, smart watches, etc.) accessible during theoretical-practical tests (individual exams).

If you do not pass the subject due to the fact that none of the evaluation activities do not reach the required minimum level of work, the numerical note of the file will be the lowest value between 4 and the weighted average of the notes. With the exceptions that the "non-evaluable" qualification will be awarded to students who do not participate in any of the assessment activities, and that the numerical note of the file will be the lowest value between3.0 and the weighted average of the notes in case the student has committed irregularities in an evaluation act (and therefore not approved for compensation).

SUMMING UP: Copying, copying, or plagiarizing any of the assessment activities is equivalent to a SUSPENSION with a score of less than 3.5.


Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Practical mark (examen and deliveries) 50% 4 0.16 3, 1, 2
Theory Mark (partial exams) 50% 4 0.16 1, 4, 2


SUBJECT MATERIALS (notes, slides, papers): In LMS.


  • T. Munzner, Visualization Analysis & Design, CRC Press, 2014.


  • S. Few, Information Dashboard Design: Displaying Data for At-a-glance Monitoring, 2nd edition, Analytics Press, 2013.
  • A. Cairo, The truthful art. Data, charts and maps for communication, New Riders, 2016.
  • A. Kirk, Data Visualization. A handbook for Data Driven Design, 2nd edition, SAGE Publications, 2019.
  • C.O. Wilke, Fundamentals of Data Visualization. A Primer on Making Informative and Compelling Figures, O’Reilly Media, 2019.


In this subject we will use the following software:

  • Software R: R is ‘GNU S’, a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques. You can download this environment in the following link: https://cran.rstudio.com/ .
  • RStudio: Es tracta d’un framework que treballa sobre R i Python. This framework can be downloaded in different (Windows, MacOs, Linux) in the following link: https://www.rstudio.com/products/rstudio/ .
  • Tableau is a powerful data analysis and visualization tool used in the area of Business Intelligence. We have an educational license for our students to use. We can also download a free demo version in the following link: https://www.tableau.com/es-es/why-tableau/what-is-tableau .

All software to be used is freely accessible or offered free of charge to students with educational licenses.