| Competences |
| Type A | Code | Competences Specific |
| A10 | Use and develop methodologies, methods, techniques, specific-use programmes, regulations and standards for graphic computing. | |
| A11 | Conceptualise, design, develop and evaluate the person-computer interaction of computer products, systems, applications and services using advanced artificial intelligence techniques interaction. | |
| A12 | Create and operate virtual environments, and create, manage and distribute multimedia content guaranteeing the protection of privacy and copyright by techniques of computer security and Artificial Intelligence. | |
| Type B | Code | Competences Transversal |
| CT2 | Forming opinions on the basis of the efficient management and use of information | |
| CT3 | Solve complex problems critically, creatively and innovatively in multidisciplinary contexts. | |
| CT5 | Communicate complex ideas effectively to all sorts of audiences | |
| Type C | Code | Competences Nuclear |
| Learning outcomes |
| Type A | Code | Learning outcomes |
| A10 |
Identify the data representation models used in graphics systems. Classify the models with respect to their scope. Evaluate the necessary algorithms for manipulating the objects that form the scene and its display. Identify the optimum graphic representations for each data type. | |
| A11 |
Recognise the elements that comprise the interaction, techniques and devices. Adapt the interaction models to the type of data displayed. | |
| A12 |
Recognise the basis of the design and the presentation of the information. Evaluates the algorithms for the representation of scientific data. | |
| Type B | Code | Learning outcomes |
| CT2 |
Master the tools for managing their own identity and activities in a digital environment. Search for and find information autonomously using criteria of importance, reliability and relevance, which is useful for creating knowledge Organise information with appropriate tools (online and face-to-face) so that it can be updated, retrieved and processed for re-use in future projects. Produce information with tools and formats appropriate to the communicative situation and with complete honesty. Use IT to share and exchange the results of academic and scientific projects in interdisciplinary contexts that seek knowledge transfer. | |
| CT3 |
Recognise the situation as a problem in a multidisciplinary, research or professional environment, and take an active part in finding a solution. Follow a systematic method with an overall approach to divide a complex problem into parts and identify the causes by applying scientific and professional knowledge. Design a new solution by using all the resources necessary and available to cope with the problem. Draw up a realistic model that specifies all the aspects of the solution proposed. Assess the model proposed by contrasting it with the real context of application, find shortcomings and suggest improvements. | |
| CT5 |
Produce quality texts that have no grammatical or spelling errors, are properly structured and make appropriate and consistent use of formal and bibliographic conventions Draw up texts that are structured, clear, cohesive, rich and of the appropriate length, and which can transmit complex ideas. Draw up texts that are appropriate to the communicative situation, consistent and persuasive. | |
| Type C | Code | Learning outcomes |
| Contents |
| Topic | Sub-topic |
| Introduction to Data Visualization | What is Data Visualization? Why is Data Visualization so important? What is Data Visualization useful for? The problem of Data Visualization Types of Data Visualization |
| Graphical Perception | The elementary perceptual tasks |
| Graphical Excellence, Integrity and Sophistication | The principles of Graphical Excellence The principles of Graphical Integrity Graphical Distortion The lie factor The principles of Graphical Sophistication The data-ink ratio Data density Proportion and scale |
| Statistical traps | Summary statistics Quoting data out of context Incorrect normalization of the data Jumping to the wrong conclusions The Simpson's paradox |
| Plots | Types of plots How to choose the type of plot according to your data and purpose Common mistakes with plots |
| Introduction to R | The basics of R Data Types in R Data Utilities in R |
| Introduction to ggplot2 | The Grammar of Graphics The components of the Grammar of Graphics |
| Mastering ggplot: the grammar | Geometries Datasets and mappings Statistical transformations Position Adjustments Scales Coordinate Systems Themes Facets |
| Mastering ggplot: the plots | The line plot family The scatter plot family The bar plot family Displaying distributions I Displaying distributions II Maps Custom Plots |
| Planning |
| Methodologies :: Tests | ||||||||||
| Competences | (*) Class hours |
Hours outside the classroom |
(**) Total hours | |||||||
| Introductory activities |
|
1 | 1.5 | 2.5 | ||||||
| Lecture |
|
9 | 13.5 | 22.5 | ||||||
| IT-based practicals |
|
13 | 20 | 33 | ||||||
| Presentations / oral communications |
|
2 | 2 | 4 | ||||||
| Personal attention |
|
1 | 0 | 1 | ||||||
| Short-answer objective tests |
|
2 | 4 | 6 | ||||||
| Extended-answer tests |
|
2 | 4 | 6 | ||||||
| (*) On e-learning, hours of virtual attendance of the teacher. (**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||||||||
| Methodologies |
| Description | |
| Introductory activities | Presentació de les eines i conceptes bàsics a utilitzar en l'assignatura |
| Lecture | Presentació dels continguts |
| IT-based practicals | Realització d'exercicis que treballen els continguts que es van presentant al llarg del curs |
| Presentations / oral communications | Presentació de les pràctiques realitzades |
| Personal attention | L'alumne es pot posar en contacte amb el professor en horaris de consulta o via correu electrònic |
| Personalized attention |
| Description |
| The student can talk with the professors at the hours scheduled at the professor's office, or send an email to arrange a specific time for personalized attention. |
| Assessment |
| Methodologies | Competences | Description | Weight | ||||||
| IT-based practicals |
|
Short deliverable exercises | 15 | ||||||
| Presentations / oral communications |
|
Final project | 35 | ||||||
| Short-answer objective tests |
|
Theory exercise | 15 | ||||||
| Extended-answer tests |
|
Theory exam | 35 | ||||||
| Others |
| Other comments and second exam session | |||
|
The subject is divided into two parts: theory and practice. Each one accounts for the 50% of the grade. Both theory and practice must be passed (>=5) to pass the course. In 1st call, the subject follows a continuous evaluation: the theory part consists of a deliverable theory exercise (mid-semester) and a theory exam (end of semester). The practice part consists of a deliverable exercise (mid-semester) and a final project (end of semester). In second call, the evaluation follows a single evaluation model. The evaluation of the theory part consists in a single exam, and the evaluation of the practice part is only done via delivery of a final project. Thus, the grades of the deliverable exercises are not taken into account in second call. |
|||
| Sources of information |
| Basic |
Colin Ware, Information visualization : perception for design, 2004, Morgan Kaufmann
Edward Tufte, The Visual Display of Quantitative Information, 2nd,
Hadley Wickham, A layered grammar of graphics, ,
Stephen Few, Now you see it, , |
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| Complementary | |||||||||
| Recommendations |
(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.