By the end of the course, students should be able to:
LO1. Understand and identify the problems associated with processing large amounts of information.
LO2. Understand the concepts and ecosystem of Big Data.
LO3. Know how to design and implement fault-tolerant data storage solutions in a distributed environment.
LO4. Know how to extract, manipulate and load large amounts of information from unstructured data sources.
LO5. Know how to manipulate and process non-relational databases.
LO6. Understand and apply distributed programming and computing models.
LO7. Understand and know how to apply techniques for handling JSON structures and real-time data flow.
LO8. Develop creativity, technological innovation and critical thinking.
LO9. Develop self-learning, peer review, teamwork, written and oral expression.

S1. The concept of Big Data, the applicable problems and the respective ecosystem.
S2. Introduction to non-relational databases and MongoDB.
S3. Computing architecture for Big Data: (1) redundant and fault-tolerant and (2) distributed to support large volumes of data. Example of the Hadoop platform and its distributed file system.
S4. The MapReduce programming model.
S5. Database design in MongoDB.
S6. Handling JSON structures and real-time data.
S7. The ETL (Extract, Transform and Load) process applied to datasets with denormalized real data and the development of Big Data processing applications in Spark and MongoDB environments.

This course follows the model of assessment throughout the semester (ATS), which does not include a final exam with a weighting of 100%.

The ATS consists of the following elements:
- 8 weekly assignments [2.5% * 8 = 20% in total]
- 2 mini-tests [15% each * 2 = 30% in total]
- Laboratory project [50%]

The laboratory project can be carried out individually or in groups. It consists of drawing up a practical project which will then be the subject of an individual oral discussion.

If the student fails the regular exam (<10 marks), the student will sit the 1st or 2nd exam, worth 50% of the mark, and it is compulsory to pass the laboratory project or carry out an individual project (50%). If the student does not pass the laboratory project or the individual project (if applicable), they have failed the course.

Upon completion of the course, students should:
LO1: Recognize the advantages and challenges of using Artificial Intelligence (AI) techniques and approaches, demonstrating critical awareness of informed and uninformed search methods.
LO2: Select and justify the most appropriate technological approaches and algorithms, including search methods, representation, and reasoning logics.
LO3: Apply the concepts and techniques discussed in the design and development of AI-based systems, as well as in the modeling of examples based on real scenarios.
LO4: Develop, implement, and evaluate solutions involving predicate logic and logic programming.
LO5: Understand the fundamentals of genetic algorithms, being able to implement and adapt them to solve specific problems.
LO6: Work autonomously and in groups to develop projects that apply the acquired knowledge, demonstrating the ability to adapt and solve complex problems in the AI field.

S1: Fundamental notions of AI with emphasis on the search-based approach.
S2: Search algorithms: depth first and breadth first, A*, greedy BFS, Dijkstra.
S3: Fundamental notions relating to knowledge, representation and the architecture of knowledge-based systems.
S4: First-order predicate logic: representation and deduction.
S5: Declarative knowledge represented in Logic Programming.
S6: Genetic algorithms.

Assessment throughout the semester consists of 3 assessment blocks (AB), and each AB consists of one or more assessment moments. It is organised as follows:
- AB1: 4 mini-tasks [7.5% each mini-task * 4 = 30%]
- AB2: 2 mini-tests [20% each mini-test * 2 = 40%]
- AB3: 1 project in Artificial Intelligence [30%]

Assessment by exam:
- 1st Season [100%]
- 2nd Season [100%]

All blocks of periodic assessment (BA1, BA2 and BA3) have a minimum mark of 8.5. In any BA, it may be necessary to hold an individual oral discussion to assess knowledge.

Assessment by examination consists of a written exam covering all the knowledge set out in the syllabus of the course, with a weighting of 100 per cent.

Attendance at classes is not compulsory.

LO1 Introduction to the Python programming language.
LO2 Understand the principles of data science and data mining and understand and be able to apply the Cross-Industry Standard Process for Data Mining (CRISP-DM) in practical cases, including understanding the business and data and preparing the data for modeling.
LO3 Can execute and debug Python applications and use the fundamental libraries in practical cases of data preparation, exploration, visualization, and analysis of data features.
LO4 Understand machine learning algorithms in supervised prediction problems (classification, regression, time series) and unsupervised clustering problems, and be able to apply and evaluate their performance in practical problems, using the Python language, in the context of the CRISP-DM methodology.
LO5 Understand and be able to apply ethical and privacy considerations in data analysis and discuss future trends in this domain.

S1 Introduction to the programming language (Python 3)
S2 Python development environments
S3 Control primitives, variables, expressions, and declarations. Objects and object classes. Functions, modules, and packages
S4 File operations. Interpretation of JSON, XML data. Database operations. Web scrapping
S5 Introduction to data science, the data cycle, and data mining. CRISP-DM model
S6 Data preparation and cleaning techniques
S7 Exploratory analysis and data visualization
S8 Selection and engineering of relevant data features
S9 Prediction methods in machine learning (classification, regression, time series). Essential algorithms, including their evaluation with performance metrics
S10 Clustering methods in machine learning. Essential algorithms
S11 Ethical considerations: privacy, security, and responsible data handling
S12 Emerging technologies and their impact on data analysis

Course unit with Periodic Assessment, not including a Final Exam. Weight of assessment:
Individual assignments, 80% mandatory (25%)
Laboratory project (group of 2), with individual oral discussion (50%)
2 multiple-choice mini-tests (25%)
If students fail in the regular period (grade < 10), they can take the 1st or 2nd term exam, which will count for 50% of the grade. It is mandatory to pass the group project or to carry out an individual project (50%).