LO1. Characterize the basic concepts of Bayesian modelling
LO2. Apply Bayesian regression, classification and optimization models to support decision making
LO3. Apply the Bayesian approach to statistical learning

PC1. Bayes Theorem and Bayesian paradigm
PC2. Graphical and hierarchical models
PC3. Bayesian inference
PC4. Bayesian optimization
PC5. Bayesian regression and classification
PC6. Bayesian latent factor models

Students may choose either Evaluation during the semester or Final exam.
Assessment throughout the semester:
- group work with minimum grade 8 (50%)
- individual test with minimum grade 8 (50%)
Approval requires a minimum grade of 10.
EXAM:
The Final Exam is a written exam. Students have to achieve a minimum grade of 10 to pass.

OA1. Understand the fundamentals and challenges of Text Mining
OA2. Learn techniques for document preparation, cleaning, and representation
OA3. Apply Natural Language Processing methods
OA4. Classify texts using machine learning
OA5. Practical application of techniques in Text Mining

The learning objectives are aligned with a teaching method that combines theory and practice. Students will acquire a solid theoretical foundation in Text Mining, its challenges, and main techniques. Through practical activities and projects, they will develop skills in preprocessing, modeling, classification, and information extraction from texts. By the end of the course, students will be capable of applying Text Mining methods in real-world contexts, using current tools and resources, preparing them to tackle complex problems in the field of text analysis.

Introduction
CP1: Importance of large quantities of text, challenges and current methods
CP2: Unstructured vs. (semi-)structured information
CP3: Obtaining and filtering information, information extraction and Data Mining
Document Representation
CP4: Document pre-processing
CP5: Feature extraction: terms as features
CP6: Term weighting schemes
CP7: Vector space models
CP8: Similarity measures
Natural Language Processing
CP9: Language models
CP10: Morphology and part-of-speech tagging
CP11: Complex structures: syntactic analysis
CP12: Information extraction
Text Classification
CP13: Introduction to statistical machine learning
CP14: Evaluation
CP15: Generative classifiers
CP16: Discriminative classifiers
CP17: Unsupervised learning
CP18: Text Mining Resources
Case Study
CP19: Sentiment analysis
CP20: Topic classification and identification

This course uses only assessment throughout the semester and does not include exams.
Assessment components:
a) TESTS (2 mini-tests: 5% each, final test: 40%), taken during the course period;
b) PROJECT (50%).
The TESTS grade can be replaced by a written test to be taken in the assessment period corresponding to the 1st season, 2nd season or special season (Art. 14 of the RGACC).
The PROJECT grade is limited to the TEST grade + 6 points.
Students may improve their grade in the TESTS component by taking a written test during the assessment period corresponding to the 1st season. Students wishing to do so must inform the teachers as soon as the assessment throughout the semester marks are published.

At the end of this course, the student should be able to:
LO1. Explain the development of Data Science over time.
LO2. Differentiate between applied development (application solution) and fundamental development (research).
LO3. Explain the interrelationship between each interdisciplinary topic covered and Data Science.
LO4. Criticise the indiscriminate use of data, whether personal or not, without respect for the principle of minimisation.
LO5. Justify the advantage of using data science processes and methods in societal and environmental problems.

The specific program contents (PC) may change or be adjusted depending on the availability of guest seminars. However, there are high-level contents that will have to be addressed in the light of current knowledge, such as:
CP1. History of Data Science.
CP2. Visual and Narrative Information and Perception.
CP3. Cyberlaw in Data Science.
CP4. Ethically Responsible Artificial Intelligence.
CP5. Data Science in Economics and Management.
CP6. Data Science in Health.
S7. Data Science in the Humanities.
CP8. Data Science in Society.
CP9. Data Science for the Future.

As this is a seminar course, there should be no written exam.
Assessment will take place throughout the semester, with students participating in small collaborative working groups (E1), preparing individual seminar sheets (E2), and carrying out research into a problem related to one of the themes covered in the different seminars with (i) a final oral presentation and (ii) a digital infographic element (E3).
Elements E1 and E2 will be assessed by the coordinator, while E3 will be assessed by peers, moderated by the coordinator.
The final mark will be calculated as: 0.3 E1 + 0.3 E2 + 0.4 E3.

LO1. At the end of the CU, each student should have acquired the necessary skills to understand how to use big data and perform data analysis to out-compete traditional companies in their industries.
LO2. Must also be able to define and implement analytical reports and dashboards, considering basic ETL processes, advanced analytical modeling and effective data visualization.
LO3. Finally, each student must develop soft skills, including Teamwork and Collaboration, Communication, Agile and Critical Thinking.

P1. Data-driven decision making.
P2. Types of Analytics.
P3. Data processing, modeling and visualization.
P4. Effective Business Presentation / communication; ability to explain complex analytical models and results.
P5. Power BI Analytics Platform.

1st Sitting:
- Written work, in groups (25%, minimum of 10 points in the final classification) - LO 1, 2, 3
- Individual laboratory project with digital presentation and oral discussion (75%, minimum of 10 points in the final classification) - LO 1, 2

2nd Sitting:
- Exam (100%, minimum grade of 10 points) - LO 1, 2, 3

Scale: 0-20 points.

At the end of this learning unit's term, the student must be able to:
LG1. Recognize and apply the classical time series models;
LG2. Recognize and apply ARIMA and GARCH models;
LG3. Recognize and apply multivariate time series models;
LG4. Recognize and apply Machine Learning algorithms (neural networks) for time series forecasting /trading.
LG5. Basic programming and computation with R and Python
LG6. Application of the studied concepts: information and value extraction from real-world data.

P1. Time series (2 lectures)
P1.1. Basic concepts
P1.2. Trends and seasonality
P2. Introduction to univariate stochastic time series models (4 lectures)
P2.1. Stationarity, unit root tests
P2.2. ARMA/ARIMA models
P2.3. Residuals assumptions, diagnoses tests
P2.4. Volatility, risk, ARCH/GARCH models
P2.5. Forecasting, measuring the forecast accuracy
P3. Introduction to multivariate time series models (2 lectures)
P3.1. VAR/VECM models
P3.2. Cointegration analysis and applications
P3.3. Forecasting
P4. Machine (Deep) Learning (6 lectures)
P4.1. Neural networks for time series
P4.2. LSTM, forecasting and trading
P5. Programming/computing with Python
P6. Application of the studied concepts: information and value extraction from real-world data (2 lectures)

The following learning methodologies (LM) will be used:

TM1. Expositional, to the presentation of the theoretical reference frames
TM2. Participative, with analysis of scientific papers
TM3. Active, with the realization of group work;
TM4. Experimental, in computer laboratories, performing analyzes on real data
TM5. Self-study, related with autonomous work (AW) by the student, as is contemplated in the Class Planning

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The periodic evaluation includes the realization of:
a) An individual test (60%).
b) A team work (40%).

The periodic evaluation requires that students attend at least 80% of classes. The test is covering the entire topics.

In this type of evaluation, the students have to achieve a minimum grade of 8,5 in the individual test and of 10 in the team work. Otherwise the students should do a final exam (minimum approval score: 10).

OA1. Skill acquisition to define a specific research problem
OA2. Skill acquisition to identify a suitable dataset to answer to the proposed research goal
OA3. Skill acquisition to evaluate and critically discuss the achieved results in the light of the defined problem
OA4. Skill acquisition to conduct a literature review that enables to position the research problem and its relevance
OA5. Skill acquisition of scientific writing

CP1. Framing the research subject
CP2. Defining the research problem and problem
CP3. Conducting literature review
CP4. Defining the scientific body of knowledge
CP5. Identifying and analysing a relevant data source to the research problema
CP6. Critically analyzing the results in Data Science
CP7. Developing scientific writing

1st and 2nd season evaluation: Individual writing of a scientific article and its presentation (100%)

O1: To know the basic digital image formation process
O2: To represent an image in different color spaces
O3: To perform typical image processing operations
O4: To extract low-level characteristics from an image
O5: To implement an automatic learning system based on classic algorithms for image content classification
O6: To know the typical architecture of a convolutional neural network (CNN) and to understand how it works
O7: To solve a medium complexity image classification problem CNNs
O8: To apply knowledge transfer and fine-tuning methodologies based on pre-trained CNNs
O9: To use deep learning algorithms for image objects identification
O10: To know deep learning algorithms for automatic generation of multimedia content
O11: To manipulate images using the OpenCV library
O12: To use the Tensorflow library to develop machine learning applications

C1 - Image acquisition and representation
C2 - Image operations
C3 - Extraction of image features
C4 - Introduction to machine learning
C5 - Artificial neural networks
C6 - Convolutional neural networks
C7 - Knowledge transfer
C8 - Network architectures for detecting and identifying image objects
C9 - Network architectures for automatic content generation

Given the imminently practical nature of the course there is no exam assessment modality - there are only assessment modalities carried out along the semester.

Modality A (requires attendance to at least 60% of the classes)
- Exercises (30%) - group work, includes participation on class activities (10%) and two work assignments (challenges) delivered online (10% each);
- Test (30%) - individual;
- Project (40%) - group work, but evaluated individually; includes report and oral discussion.

Modality B
- Test (45%) - individual;
- Project (55%) - individual or in a group, but evaluated individually; includes report and oral discussion.

The "Project" component requires a minimum of 10 (out of 20) values, regardless of the assessment modality.

The "Test" component is carried out at the end of the classes period; if a student misses the test with justified reason, he/she can take a new test during the exam’s season.

The grade of the "Project" component is subject to an oral discussion. This oral discussion may impose a limit on the project's grade if the performance shown by the student is below the quality of the delivered project; the student may fail if performance shown in the discussion is not sufficient.

There is no process for grade improvement.

The evaluation process in the special season is identical to modality B, but the "Project" must be carried out individually.

Learning goals (LG):
LG1- Independent scientific thought and originality
LG2- Scientific skills
LG3- Logical coherence and scientific argumentation
LG4- Quality of the presentation

Syllabus contents (SC):
SC1- Formulate the starting question
SC2-Identify the relevant literature and elaborate a theoretical and empirical revision
SC3-Formulate the research problem and the hypotheses
SC4- Design a study to test the hypotheses
SC5- Carry out the study
SC6-Analyse and interpret the results
SC7-Elaborate the Master Project plan
SC8-Write the Master Project

A panel of judges in public tests will assess the Master Project, after the supervisor's approval of its
conclusion and quality to be presented in public tests. Assessment will be based on the scientific merit of the study and on its theoretical and methodological adequacy.