Teaching plan for the course unit



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General information


Course unit name: Data Processing and Visualization Laboratory

Course unit code: 366223

Academic year: 2021-2022

Coordinator: Santiago Marco Colas

Department: Department of Electronic and Biomedical Engineering

Credits: 3

Single program: S



Estimated learning time

Total number of hours 75


Face-to-face and/or online activities



-  Lecture

Face-to-face and online




-  Laboratory session

Face-to-face and online




-  Student presentation and discussion

Face-to-face and online



Supervised project


Independent learning






Students need some familiarity with one of the following programming languages: R, Python, MATLAB.

Basic statistical knowledge is also required.



Competences to be gained during study



To use IT tools to search for reference resources or information related to medical technologies and bioengineering (Personal).


To be able to work independently (Personal).


To gain knowledge of basic and technological subjects required to learn new methods and technologies and ensure versatility and the ability to adapt to new situations (Personal).


To be able to take further studies and to develop a positive attitude in order to keep knowledge up-to-date in a process of lifelong learning. To have sufficient depth of knowledge to start postgraduate studies in the field of advanced biomedical engineering.

Learning objectives


Referring to abilities, skills

— Be able to use an Integrated Development Environment (IDE) for programming and debugging.


— Be able to generate reports automatically using integrated code.


— Be able to program in a high-level programming language including flow control and definition of functions.


— Be able to produce attractive visualisation to effectively communicate the information contained in the data.



Teaching blocks


1. Data import

*  Reading text files with a variety of data types, delimiters and headers; Combining heterogeneous data files

2. Data manipulation

*  Raw data processing: extraction, manipulation, aggregation and data imputation; Working with missing data; Working with categorical data

3. Data visualisation

*  Recording and modifying standard graphics to produce customised graphics; Determining the properties of the graphic objects and their values; Locating and manipulating graphic objects; Modifying the properties of the graphic objects

4. Visualisation of multidimensional data

*  Interpolation in several dimensions; Methods to visualise highly dimensional and complex data



Teaching methods and general organization


The methodology is strongly based on the paradigm “learning by doing”.

Throughout the course, students carry out three small projects for about 3 sessions each. Each project is carried out in a different high-level programming language, namely: Python, MATLAB, and R. These small projects will be partially guided by the lecturer.

Independent work is favoured when possible.

All the course activities are carried out in English.

If restrictions are applied due to the current health situation, laboratory sessions will be held partially online. Lecturers will follow the development of the projects via online meetings with the tools available (BB Collaborate or similar). If required, oral presentations for assessment purposes will also be carried out online.

Whenever relevant, the perspective of genre will be incorporated.



Official assessment of learning outcomes


Assessment is based on three project reports and an oral presentation and demonstration of the projects carried out during the course.


Examination-based assessment

Single oral presentation of the three projects carried out during the course.



Reading and study resources

Consulteu la disponibilitat a CERCABIB


Kyran Dale, Data Visualization with Python and JavaScript, O’reilly, 2016.

Jake VanderPlas, "Python Data Science Handbook: Tools and Techniques for Developpers’, O’reilly (2016)

Stormy Attaway, “MATLAB: A practical introduction to Programming and Problem Solving”,  3rd Edition, Amsterdam, Elsevier, 2013..

Andrew King, Paul Aljabar, “MATLAB programming for Biomdical Engineers and Scientist”, Academic Press, 2017.

Garret Grolemund, Hadley Wickham, “R for Data Science”, O’Reilly, 2016

Andy Kirk, ’Data Visualilzation: A Handbook for Data Driven Design’, SAGE Pub, (2016)