Logic and Discrete Mathematics

The course "Logic and Discrete Mathematics" introduces fundamental concepts of Discrete Mathematical Logic, focusing on basic principles and applications. It covers topics such as Counting Principles, Graph Theory, and their applications in problem modelling. Additionally, it introduces Propositional Logic and First-Order Logic, developing essential skills in mathematical reasoning and algorithm analysis. Students apply these concepts to informatics problems, such as network optimization and complexity theory

Introduction to Probability and Statistics

The course "Introduction to Probability and Statistics" introduces students to the fundamental concepts of Probability and Statistics. It covers essential topics such as Random Variables, Probability Distributions, Statistical Functions, and Sampling Distributions. Additionally, it examines methods of Parameter Estimation and Confidence Intervals. Students acquire practical skills through applications using languages such as Python (NumPy, SciPy, Statsmodels) and R, developing a toolkit of mathematical methods and programming techniques for data analysis.

Calculus

The course "Calculus" introduces students to the fundamental concepts of Calculus and Algebra, with an emphasis on Group Theory. It covers basic structures such as Groups, Permutation Groups, and Cyclic Groups, as well as their applications in programming and cryptography. Students explore algebraic properties and their use in algorithms, while the course includes practical implementation of mathematical concepts through programming languages such as Python.

Programming

The course "Programming" introduces the fundamental concepts and principles of Programming, covering key topics such as Control Flow Structures, Functions, Recursion, Search & Sorting Algorithms, as well as File Input/Output (File I/O). No prior programming experience is required, as all practical exercises are conducted using the Python language in a modern Integrated Development Environment (IDE). The course emphasizes problem-solving, understanding algorithms, and developing programming skills through practical applications.

Introduction to Data Science and Modelling

The course "Introduction to Data Science and Modelling" introduces students to the fundamental concepts of Data Science (DS) and Modelling. It covers essential topics such as Data Types, Matrix Construction and Operations, and Data Visualization. Students extensively use Python libraries, such as NumPy for numerical computations, Pandas for data management and analysis, and Matplotlib for creating graphs and statistical visualizations. The course also focuses on data preprocessing techniques and an introduction to statistical analysis, providing practical applications in the analysis and modelling of real-world data.

Professional Work Skills

The course "Professional Skills and Behaviours" introduces students to the essential professional skills and behaviours necessary for successful integration into the job market.

Legal, Ethical and Sustainability Issues

This course introduces students to legal, ethical, and sustainability issues in the field of informatics, with an emphasis on data protection, privacy, security, and intellectual property rights. It also examines good and bad ethical practices in the use of information technologies and analyses the sustainability of large technological projects, their business risks, and their environmental impact.

Team Project

This course enhances students' employability skills through the simulation of working in a professional team. Students participate in two activities: first, they are introduced to a simulated professional environment, and then they develop a small project for a real industrial employer. The course covers issues of project management, programming, ethics, legal matters, and teamwork, focusing on the needs of each specialization, such as software development and coding standards.

Linear Algebra

The course "Linear Algebra" extends the mathematical techniques introduced at previous levels, focusing on fundamental concepts of Linear Algebra (LA). It covers essential topics such as Linear Systems, Matrices, and Vector Spaces. Additionally, it introduces the concepts of Eigenvalues and Eigenvectors, as well as Projections and their applications in numerical and computational methods. The course includes practical application of these concepts to problems in optimization, machine learning, and data processing, using libraries such as NumPy and SymPy, among others.

Statistical Methods for Data Analysis

The course "Statistical Methods for Data Analysis" introduces students to the fundamental concepts of Statistical Analysis and Statistical Tests. It covers basic measures such as Measures of Central Tendency, Variance, and Random Sampling. Additionally, advanced methods such as Linear Regression, Multiple Linear Regression, Analysis of Variance and Covariance Analysis are examined. The course also includes the construction of Randomized Experiments and the writing of Statistical Reports. Students apply these methods using software such as R and Python, utilizing libraries like statsmodels and scipy.

Introduction to Machine Learning

The course "Introduction to Machine Learning" introduces students to the fundamental concepts of Machine Learning (ML), focusing on both Supervised Learning and Unsupervised Learning. It covers algorithms such as Linear and Logistic Regression, Decision Trees (DTs), k-Nearest Neighbours (k-NN), and Random Forests (RFs). In the field of Unsupervised Learning, methods such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Clustering (K-Means, DBSCAN), and Autoencoders (AEs) are examined. The course includes practical applications in languages such as Python, using libraries like Scikit-Learn, TensorFlow, and PyTorch, with an emphasis on data analysis and the development of learning algorithms.

Pathway Modules

Operational Research

The course enables students to acquire the skills to apply Operational Research techniques in key areas for businesses and industry, with an emphasis on forecasting, planning, and decision-making. It focuses on developing a mathematical background to enhance students' modelling capabilities.

Functional Programming

The course "Functional Programming" broadens students' understanding of software development by focusing on the principles of Functional Programming (FP). It covers concepts such as Immutable Data Structures, Recursion, Higher-Order Functions, and Functional Control Structures. Additionally, it emphasizes the construction of Parsers, the creation of Abstract Syntax Trees (ASTs), and the application of functional techniques for data analysis and processing. The course uses languages like Haskell, focusing on practical applications in compilation, language programming, and algorithm optimization.

Statistical Modelling

This course forms one of the foundations in data analysis. It involves the creation and evaluation of statistical models, using methods such as linear regression, generalized linear models (GLMs), Analysis of Variance (ANOVA), and non-parametric techniques like the Mann-Whitney test or the Wilcoxon test. Additionally, methods such as cross-validation are applied to evaluate model performance. Through these techniques, statistical modelling provides valuable tools for decision-making and understanding relationships between variables in each dataset.

Frontiers in Artificial Intelligence and Data Science

This course focuses on modern methods of Artificial Intelligence (AI) and Data Science, covering techniques such as neural networks, machine learning, Deep Learning, and reinforcement learning. It includes algorithms like Random Forests, Support Vector Machines (SVMs), K-Means Clustering, and Gradient Boosting, as well as methods for natural language processing (NLP) and computer vision. Students apply these techniques to real data, developing practical skills in analysis and modelling.

Deep Learning

This course (Deep Learning - DL) is an advanced branch of Machine Learning (ML) that uses Artificial Neural Networks (ANNs) to learn complex patterns from data. The key techniques include Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) for sequential data processing, as well as Transformers, which have revolutionised Natural Language Processing (NLP). Additionally, techniques such as GANs (Generative Adversarial Networks) are used for generating synthetic data, while Reinforcement Learning (RL) is applied to optimisation problems and autonomous systems. Deep Learning is widely used in fields such as image recognition, text translation, and autonomous driving.

Individual Project

The course allows students to independently develop a project related to their specialization, applying the knowledge and skills they have acquired. Under the guidance of an academic supervisor, they choose or propose a topic and work on writing a dissertation and developing a practical item. The project includes design, literature research, development, and evaluation.

Pathway Modules 

Advanced analytics

The course "Advanced Analytics" (AA) includes advanced data analysis methods, focusing on Machine Learning (ML), Artificial Intelligence (AI), and Statistical Modelling (SM). It employs techniques such as Regression Analysis, Clustering (K-Means, DBSCAN), Decision Trees (DTs), and Random Forests (RFs) to extract predictions and patterns from data. Additionally, it includes Time Series Analysis (TSA) with algorithms like ARIMA and LSTM, as well as Sentiment Analysis through Natural Language Processing (NLP). Advanced Analytics is used in business forecasting, fraud detection, and operations optimisation.

Optimisation techniques

The course "Optimisation Techniques" (OT) focuses on improving algorithms and processes to maximise performance and accuracy in complex problems. It includes methods such as Linear Programming (LP), Nonlinear Optimisation (NLO), and Dynamic Programming (DP). Additionally, Evolutionary Algorithms (EA) like Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO) are used. These techniques are applied to problems in routing, resource allocation, and machine learning.