Artificial Intelligence & Data Science Master

Este módulo se centra en introducir los fundamentos de la programación usando Python, un lenguaje ampliamente utilizado y versátil, que resulta ideal tanto para principiantes como para profesionales. Python se emplea en aplicaciones web, análisis de datos, inteligencia artificial, automatización de tareas y mucho más. Su sintaxis simple y legible facilita el aprendizaje y desarrollo, proporcionando una base sólida para el resto del programa.

Modules:

• Introduction and Basic Python Features
• Data Types, Variables and Text Manipulation
• Python Data Structures
• Random Data Generation
• Flow Control Structures
• Python functions
• Manipulation of Dates and Times
• Lambda Functions
• Regular Expressions
• Working with JSON Data

This module provides a comprehensive introduction to the world of databases, covering everything from basic modelling principles to practical implementation using SQL. Through this course, students will learn how to define, manipulate and manage data within structured database systems, using SQL as the primary tool. The goal is to equip students with the skills necessary to design efficient databases and perform complex queries to support business decisions.

Modules:

• General Concepts and Database Modelling
• Introduction to the SQL Standard: Data Definition Language (DDL) and Data Manipulation Language (DML)
• Advanced SQL Standard: Subqueries and Common Table Expressions (CTEs)
• Scripting in SQL

This module focuses on data transformation and data modelling, essential techniques for turning raw data into valuable information in business contexts. Through methodologies such as ETL, ELT, and EL, and using modelling standards such as dimensional modelling and Data Vault, students will learn how to create sophisticated data products. In addition, the course will address the use of modern tools for the orchestration of data flows and the creation of applications and APIs that enable the effective exploitation of transformed data.

Modules:

1. Data Transformation
   • Modelling Techniques
   • ETL/ELT/EL
   • Data Flow Orchestrators
2. Data Exploitation
   • Creating APIs
   • Data Applications

Exploratory data analysis (EDA) is a crucial stage in any analytics or modelling project. It helps us understand the structure, content and relationships within the data, making it easier to prepare for the development of Machine Learning models.

Modules:

• Introduction to Exploratory Data Analysis (EDA)
• Python Environment Configuration for EDA
• Descriptive Statistics with Python
• Creating Interactive Graphics and Visualisations
• EDA applications in Machine Learning

El módulo está diseñado para proporcionar a los estudiantes una comprensión profunda de cómo transformar datos complejos en visualizaciones claras, efectivas y accionables.

Modules:

• Introduction to Data Visualisation
• Types of Graphics and Their Applications
• Designing Effective Visualisations
• Visualisation Tools
• Interactive Visualisation and Dashboards
• Advanced Visualisation
• Case Studies and Applied Projects

Advanced data visualisation is essential for turning complex information into clear insights that support strategic decision making in a business environment. In this module, the focus is on using business intelligence tools such as Power BI and Tableau to generate interactive reports and dashboards that present valuable information to decision makers.

Modules:

• Concept and Relevance of Storytelling in the World of Data
• Key Elements of a Good Data Narrative
• Tools for Effective Dashboard Implementation: Power BI
• Python Integration for Preprocessing and Visualisation

This module sets the starting point for the world of Machine Learning, introducing you to the key concepts and essential techniques of the field. Through hands-on and applied learning, you will discover how models can unravel hidden patterns in data. The goal is to prepare you to handle more sophisticated challenges and dive into more advanced techniques in later modules.

Modules:

• Introduction to Machine Learning (ML)
• Life Cycle of an ML Project
• Fundamental concepts of ML
• Supervised Learning: Regression
• Supervised Learning: Classification
• Supervised Learning: Decision Tree and Random Forest
• Unsupervised Learning: Clustering
• Dimensionality Reduction

This module is a cornerstone, providing the fundamental tools to understand and analyse data accurately and rigorously. In this module, you will understand how statistical techniques and probabilistic concepts are essential elements in data-driven decision making, learning to apply statistical methods to draw meaningful inferences, identify patterns and trends, and make reliable predictions. We will acquire skills to assess the uncertainty and risk associated with data, critical in dynamic business environments.

Modules:

• Introduction and Key Mathematical Concepts
• Fundamentals of Statistics
  • Descriptive Statistics
  • Probability Distributions
• Linear Algebra
• Probability
  • Fundamental Concepts
  • Estimation Methods

Once the techniques to start working with Machine Learning are established, this module will allow us to go deeper into more complex algorithms and scenarios, but it will also teach us advanced techniques to optimise our models and face problems when the data does not help us much in its natural state.

Modules:

• Advanced Algorithms
• Support Vector Machines (SVM)
• Stochastic Gradient Descent
• Ensemble algorithms: AdaBoost, XGBoost, among others.
• Model Optimisation
• Hyperparameter setting
• Selection of characteristics
• Regularisation
• Cross-validation
• Time Series Analysis
• Introduction to time series analysis
• Modelling and trends
• ARIMA and SARIMA models
• Networks
• Fundamental concepts of graphs
• Learning graph representations
• Classification and prediction of links
• Reinforcement Learning
• Concept of reinforcement learning
• States, actions and rewards
• Reinforcement learning algorithms
• Anomaly Detection and Learning from Unbalanced Data
• Identifying outliers using statistical methods, clustering and supervised learning
• Techniques for handling unbalanced data, such as additional data collection, synthetic generation and modification of algorithms

The Deep Learning module is the next level in machine learning, where you'll explore deep neural networks and advanced architectures for tackling complex problems. Discover how these revolutionary techniques have transformed the field, enabling the analysis of more complex data and solving challenges in computer vision, natural language processing and more.

Modules:

• Introduction to Deep Learning
• Convolutional Neural Network (CNN)
• Recurrent Neural Network (RNN)
• Natural Language Processing (NLP)
• Generative Adversarial Networks (GAN)

The module on Generative Artificial Intelligence (Generative AI) provides students with an in-depth understanding of technologies that enable the creation of original content from existing data. The aim is to provide both theoretical knowledge and practical experience to implement generative models in different fields.

Modules:

• Generative AI Fundamentals
• Development and Coding with Generative AI
• Practical Applications of Generative AI
• Ethics and Responsibility in Generative AI
• Generative AI in Digital Transformation

Distributed processing has revolutionised the way we manage large volumes of data, and Apache Spark has established itself as one of the leading tools in this field. Its ability to process data in a parallel and distributed fashion, harnessing the power of cluster computing, has made it essential for professionals looking to extract value from the vast amount of information generated today.

Modules:

• Introduction to Distributed Processing with Spark: Understand the distributed processing paradigm offered by Spark. Its ability to split tasks across multiple cluster nodes allows operations to be performed at high speed and in parallel.
• Data manipulation with Spark DataFrame: DataFrames in Spark are optimised structures that allow efficient manipulation of tabular data. Here it is important to know:
• Loading data from multiple sources.
• Filtering and column selection.
• Aggregations and transformations.
• Spark SQL: This Spark module provides an interface that allows the use of SQL queries to manipulate data, facilitating analysis and gaining valuable insights.
• Data Cleaning and Preparation: Before any analysis, the data must be ready for use:
  • Detection and treatment of null values.
  • Handling of missing data.
  • Data type conversion.
  • Data standardisation.
• Data Transformation and Enrichment:
  • Date and time operations to correctly handle time data.
  • String manipulation for formatting and transforming textual data.
  • Creation of new columns to provide additional information for analysis.

Esta asignatura ofrece diferentes talleres para que los estudiantes exploren la aplicación de los datos a partir de casos de éxito de empresas y profesionales de diferentes industrias.

The course explores the critical components and underlying technologies of Industry 4.0, a paradigm that integrates advanced digital tools within the industrial context to improve production processes and data-driven decision making. Students will learn about digital transformation and how companies can become Data Driven entities. In addition, the fundamentals of emerging technologies such as Cloud Computing, Big Data, Internet of Things (IoT) and Artificial Intelligence will be introduced, highlighting their importance and application in today's environment.

Modules:

• Digital Transformation
• Data Driven Companies
• Cloud Fundamentals
• Big Data Fundamentals
• IoT Fundamentals
• Fundamentals of Artificial Intelligence

It provides a detailed understanding of the journey towards cloud adoption, including the technical, strategic and management aspects involved. Students will be guided through fundamental and advanced concepts of cloud computing, effective migration strategies and techniques for optimising and managing cloud infrastructures. A hands-on approach will be encouraged through the design, implementation and evaluation of cloud-based solutions.

Modules:

• Cloud Computing Fundamentals
• Key Components of Cloud Infrastructure
• Cloud Migration Planning and Strategies
• Design and Architecture of Cloud Solutions
• Security and Compliance Management in the Cloud
• Cloud Operations Management and Optimisation
• Innovation and Advanced Cloud Services

This comprehensive module teaches how to strategically manage and use data to foster innovation in a variety of organisational contexts. Through a combination of advanced theory and applied practice, you will study methodologies for effective data management and the implementation of innovative processes that capitalise on emerging opportunities in the technological and business environment.

Modules:

• Fundamentals of Data Management
• Innovation and Creativity in Business
• Emerging Technologies and Digital Transformation
• Entrepreneurship and Innovative Startups
• Innovation Project Management

This module provides a comprehensive overview of data governance, highlighting its importance in the management and protection of data assets within an organisation. Through the analysis of frameworks and regulations, students will learn how to implement effective policies that ensure data quality, security and compliance. The module combines theory with practical case studies to teach students how to design and implement a robust data governance programme that supports the organisation's strategic and operational objectives.

Modules:

• Fundamentals of Data Governance
• Metadata Management and Data Quality
• Roles and Responsibilities in Data Governance
• Technologies and Tools for Data Governance

This module focuses on project management methodologies used to effectively lead, plan and execute complex projects. Through the study of predictive and agile methodologies, students will learn to adapt to dynamic environments and manage projects that respond to stakeholder needs and business objectives. This module combines academic theory and proven project management techniques, preparing students to face real project management challenges.

Modules:

• Project Management Fundamentals
• Predictive and Agile Project Methodologies
• Project Planning and Implementation
• Leadership and Project Team Management
• Digital Adaptation and Transformation in Project Management
• Project Management in Complex Environments

This course explores the fundamental ethical principles applied to data management in the digital age. It will address complex issues such as privacy, confidentiality, autonomy and consent in the context of the growing use of data and analytics technologies. Through a combination of philosophical theory and case studies, students will learn to navigate and apply ethical frameworks in real-world situations related to data management, ensuring responsible and fair decisions in professional settings.

Modules:

• Fundamentals of Data Ethics
• Values in the Data Age
• Ethics in Digital Democracy
• Ethics and Responsibility in Generative AI
• Contemporary Issues in Data Ethics

Esta asignatura ofrece diferentes talleres para que los estudiantes exploren la aplicación de los datos a partir de casos de éxito de empresas y profesionales de diferentes industrias.

Team building.

• Choice of topic for final project.
• Assignment of tutors.
• Development of the project with an assigned tutor.
• Project delivery.
• Presentation of the final project before a panel of experts.

*Modulo sujeto a aprobación de Cualificam.

Connect with the work and professional environment to develop the skills, abilities and competences acquired in the curricular development Integrate the knowledge acquired in the training programme to the job position where the student is established. To value the performance and capacity to develop in a job position.