Master in Data Science and Artificial Intelligence

For those profiles with no prior knowledge or less familiarity with the Python programming language, we offer this leveling prework that will give you the foundations you need to begin this master. You will learn this language and practice with dynamic, self-correcting practical material, with access to the IT Specialist Python (ITS-303) certification.

Introduction to programming Introduction to Python Data types Variables Basic input and output operations Basic operators Boolean values Conditional execution

Loops Lists Bitwise operations Lists Functions Tuples and dictionaries Exceptions

This first subject covers the fundamentals of programming in Python, an essential language for data analysis. From installing the software to creating functions and data structures, this module will give you the tools you need to work with data efficiently. The content includes:

Installation Basic tools Jupyter Notebook Data types Conditional structures Iterative structures

Functions Data structures Vector programming with Numpy Data manipulation with Pandas Basic plotting with Matplotlib

Statistics is one of the fundamental pillars supporting the structure of data science. In this topic, we will present the essential statistical concepts that are key to interpreting and effectively analyzing data. By understanding these fundamentals, you will gain the ability to discern patterns, identify meaningful correlations, and extract valuable information from complex data sets. This statistical knowledge provides a solid base for developing analytical skills and making informed decisions in the field of data science.

Descriptive statistics Probability Random variables Covariance and correlation

Sample and population Normal distributions Poisson distributions Other types of distributions

We will go deeper into the application of statistics in the field of data science. You will explore how to run statistical tests, interpret results, and use techniques to extract relevant information from data. Throughout the subject, the emphasis will be on how statistics becomes a vital tool for uncovering patterns, trends, and meaningful relationships within complex data sets. Practical cases illustrating the application of statistical concepts to solving real-world problems in data science will be covered.

Main statistical tests Statistical significance P-value Interpreting key metrics

Principal Component Analysis (PCA) Inferential statistics Linear regressions Logistic regressions

In this subject we will enter the world of Big Data, addressing the management and analysis of vast data sets. We will explore the challenges and opportunities inherent to Big Data, as well as the core technologies used to process it. By understanding the implications and applications of Big Data, you will be prepared to take on the challenges posed by the era of massive information and seize the opportunities this vast field of study and application offers.

The need for Big Data Introduction to Big Data What Big Data is not Big Data challenges

Data sources Data types Volume, velocity, and variety Storage technologies

Relational databases, also known as SQL databases, have long been the storage system model par excellence. This topic covers the design, implementation, and management of relational databases using SQL (Structured Query Language). Students will learn to structure data in tables, establish relationships between them, and run complex queries to retrieve and manipulate information. Through practical examples and case studies, we will explore how SQL databases are essential to ensuring integrity and efficiency in handling data in data science projects.

Introduction to relational databases Relationships between tables Designing database models

SQL language Data integrity

This topic focuses on the study and application of NoSQL databases, essential for handling and analyzing large volumes of data in various forms. As organizations generate and process data at an accelerating pace, NoSQL databases emerge as an effective solution for storing, managing, and retrieving unstructured and semi-structured information. Students will explore the different types of NoSQL databases, their architectures, and how they fit into the data science ecosystem.

Introduction to NoSQL NoSQL data models

Architecture and design Scalability and performance

In this topic we will go deeper into the use of databases. We will see the wide range of options available and how to determine which ones fit our needs in each case.

Advanced indexing Data partitioning Performance optimization

High availability Fault tolerance

In this section, we will explore the main architectures of Big Data systems, understanding how vast data sets are stored and processed on these platforms. We will look in detail at the structure and operation of these architectures, as well as the key technologies behind them. By understanding these fundamentals, you will have the knowledge to take on the challenges and seize the opportunities that today's Big Data environment offers.

Distributed storage systems Cloud storage systems NoSQL databases Apache Hadoop

Cloudera Apache YARN MapReduce Process orchestration

Cloud computing has revolutionized the way companies store, process, and access data. In this unit, we will explore the fundamental concepts of cloud computing, why it matters in data science, and its various applications.

Definition of cloud computing Service models: IaaS, PaaS, SaaS

Cloud providers Benefits and challenges

In this unit, we will explore the main tools in the Big Data ecosystem for managing and processing large volumes of data in Data Science and AI.

Hadoop Ecosystem Apache Hive Apache Spark

Apache Airflow Apache Kafka

In this unit, we will cover PySpark, a key technology for massive data processing within the Big Data ecosystem. PySpark is the Apache Spark interface for Python, allowing data scientists and analysts to harness the power of Spark in a familiar environment. Thanks to its distributed processing capabilities, PySpark makes it easier to manipulate, analyze, and model large volumes of data efficiently and at scale, integrating seamlessly with Data Science and Artificial Intelligence workflows. 

PySpark Relationship with Apache Spark Architecture and main components

Reading, transforming, and processing data at scale. Integration with Python libraries Use cases

In the real world, data varies in quality, format, and structure. It is therefore crucial to know how to identify and properly handle whether our data is structured, unstructured, or semi-structured. Students will learn to work efficiently with each type of data, enabling them to understand the unique characteristics of each one and apply the right strategies for processing and analyzing it. By mastering these skills, students will be better prepared to face the challenges and seize the opportunities that real-world data presents in data analysis and data science.

Main atomic types Qualitative and quantitative data Continuous and discrete data

Structured and unstructured data Time-series data

The ability to build your own datasets is a fundamental skill for expanding your capabilities as a data scientist. This unit explores the various techniques for collecting data, such as using APIs, web scraping, and extracting data from social media. You will learn to assess and select the most appropriate technique for each case, allowing you to obtain relevant, high-quality data for your analyses and data science projects. 

Data sources Social media Web Scraping

Real-time data Trends in data collection

Data preprocessing is considered one of the most important and labor-intensive stages in a data science pipeline. We will teach you how to prepare data for analysis, including cleaning, transformation, and normalization. You will also learn to assess data quality and correct errors. This skill is crucial for ensuring the reliability and accuracy of analyses and Machine Learning models.

Data quality Exploratory data analysis Outlier detection and treatment Handling missing values

Handling imbalanced problems Variable transformation Normalization of numerical variables

This unit provides context on the origins of advanced analytics, the initial challenges it faced, and its evolution to the present day. It explores the historical and conceptual foundations that have shaped the field of Advanced Analytics, as well as the technological and methodological innovations that have driven its development and expansion. You will discover the different types of problems you may face, along with their main characteristics and how to approach them. You will learn to validate the quality of your models from a critical, objective standpoint, allowing you to assess the effectiveness and accuracy of your results. By analyzing and evaluating model performance, you will be better prepared to make informed decisions and optimize your data analysis and predictive modeling processes.

Introduction to advanced analytics Knowledge extraction Supervised problems Unsupervised problems Semi-supervised problems Inference vs. prediction

Advanced data analytics processes Main applications Overfitting vs Underfitting Bias vs Variance Model validation

This unit takes a hands-on approach to the main machine learning techniques, from the fundamentals underpinning the field to the algorithms that currently represent the state of the art. We will explore the essential machine learning methodologies as well as the most advanced algorithms used across a range of applications. Through practical exercises and case studies, you will develop a deep understanding of how to apply these techniques to solve real-world problems and make the most of the potential of Machine Learning across different areas and sectors.

Introduction to machine learning Classification techniques Regression techniques

Clustering techniques Advanced algorithms State of the art

Deep Learning is a field that is transforming the limits of what artificial intelligence can do. In this unit, you will take a hands-on approach to the fundamentals driving how neural networks work, as well as their most advanced developments. You will explore the underlying principles of neural networks and dive deeper into advanced deep learning techniques and concepts. Through practical exercises and applied projects, you will acquire the skills needed to design, implement, and optimize deep learning models for a variety of real-world applications.

The intuition behind deep learning Simple neural networks Recurrent neural networks

Convolutional neural networks Transformers Generative adversarial networks

This unit takes a deeper look at the technical aspects and advanced applications of Generative Artificial Intelligence, covering the key tools, architectures, and methodologies for developing intelligent systems. It focuses on concepts such as RAGs (Retrieval-Augmented Generation), autonomous agents, frameworks like LangChain and LangGraph, and Fine-Tuning techniques for adapting models to specific needs. This unit is aimed at professionals and developers looking to understand and apply these technologies with a practical, in-depth approach.

Introduction to GenAI and its technology ecosystem. RAGs (Retrieval-Augmented Generation): concepts, architecture, and use cases. Autonomous agents in generative AI environments. LangChain and LangGraph: frameworks for building generative AI applications.

Fine-Tuning techniques and adapting models to specific tasks. Integrating generative AI into complex systems. Best practices and considerations around efficiency, security, and scalability.

A complete view of the machine learning model production cycle: deployment, monitoring, and maintenance in production.

In this topic, you will understand that mastering the creation of advanced models capable of extracting valuable information is not the only critical skill: it is also essential to know how to communicate that knowledge through compelling, informative data visualizations. Effective visualizations not only make data easier to understand, they also highlight important patterns, trends, and relationships in a clear and accessible way.

Advanced charts Multivariable visualization Text visualization

Time series visualization Design and aesthetics Tips for building better charts

Through hands-on examples and case studies, you will develop the skills to identify patterns, trends, and improvement opportunities in business data, enabling you to make informed, strategic decisions that drive organizational success.

Introduction to Business Intelligence Calculating key metrics Dashboards Functions and benefits

KPI Advanced components Tableau Power BI

Tableau is a powerful data visualization tool that lets you create interactive visualizations and dynamic dashboards from complex datasets. In this topic, we will introduce the fundamentals of Tableau and how to use it to analyze and communicate data effectively.

Interface Advantages and disadvantages Types of visualizations

Building dashboards Data import Building visualizations

Power BI is a Microsoft data analytics platform that allows users to visualize and share data intuitively and effectively. In this topic, we will cover the fundamentals of Power BI and how to use it to create interactive reports, dynamic dashboards, and advanced data analysis.

Interface Advantages and disadvantages Types of visualizations

Building dashboards Data import Building visualizations

In this topic, you will discover how to communicate the results of your data analyses effectively. You will learn to create compelling stories that convey your ideas to the audience clearly and concisely. You will explore techniques for structuring and presenting information coherently and persuasively, using visualizations, narratives, and practical examples.

Definition The importance of storytelling Narrative structure Reports

Tips Selecting meaningful data Identifying the audience Building effective storytelling

Hands-on masterclass on building agents with Claude Code: designing and configuring an orchestrator agent capable of supporting data collection, modeling and analysis, algorithm development, and fine-tuning, applied to the real workflows of the master.

With the rapid advances in artificial intelligence, legislation in this field is becoming increasingly relevant. This topic presents the foundations that explain the importance of this area and analyzes the current state of artificial intelligence legislation. It explores the ethical, legal, and social challenges that arise with the development and implementation of AI systems, as well as the evolving regulations and policies designed to address them.

Privacy Data protection and LOPD Data processing

Data retention and deletion Exercising rights

The rise of artificial intelligence is driving increasingly intensive use of computing resources. This, along with other factors, makes sustainability a topic that matters more and more in the world of data science. In this topic, you will learn about the main ethical challenges we face in the development and implementation of artificial intelligence. You will explore issues such as privacy, algorithmic bias, transparency, and accountability in the design and use of AI systems. By understanding these ethical challenges, you will be better prepared to advocate for ethical and responsible practices in the development and application of artificial intelligence, contributing to a fairer, more equitable future for everyone.

The importance of sustainability Current state Explainability of artificial intelligence Bias and Fairness Challenges

Data ethics Exploring negative scenarios Governance and compliance Sustainability

You will learn how these technologies can benefit companies by improving their processes and supporting well-founded decision-making. You will review case studies and practical examples that illustrate how data science and artificial intelligence can be applied in sectors such as healthcare, e-commerce, finance, manufacturing, and many others.

Current situation and level of adoption Evolution of the sector

Applications across different sectors The future of the industry

This topic covers the life cycle for planning and executing artificial intelligence projects. You will learn to define project objectives, select the right tools, and manage the team effectively. You will explore the different stages of the project life cycle, from conception through implementation and maintenance, focusing on best practices to ensure the project's success. You will also learn to use methodologies such as Scrum and Kanban to work efficiently and adaptively. You will explore the core principles and practices of these methodologies, as well as their application in artificial intelligence projects. You will gain skills to manage teams, set priorities, and adapt to change in an agile and effective way, enabling you to deliver AI projects successfully in dynamic, demanding environments.

Solution design Resource selection and management Budgets Planning Introduction to agile methodologies Advantages and limitations Kanban

Execution Control and monitoring Project closure Real-world experiences Scrum Adopting agile methodologies