Master in Data Science & Artificial Intelligence

6h.

Presentation of the curriculum, work tools, program operation and presentation of the group.

48h.

This module focuses on introducing the fundamentals of programming using Python, a widely used and versatile language that is ideal for beginners and professionals alike. Python is used in web applications, data analysis, artificial intelligence, task automation and much more. Its simple and readable syntax makes it easy to learn and develop, providing a solid foundation for the rest of the bootcamp.

Modules:

• Python Introduction and Basic Features
• Data Types, Variables and Text Manipulation
• Python Data Structures
• Random Data Generation
• Flow Control Structures
• Python Functions
• Date and Time Manipulation
• Lambda functions
• Regular expressions
• Working with JSON Data

30h.

This module provides a comprehensive introduction to the world of databases, from basic modeling principles to practical implementation using SQL. Through this course, students will learn to define, manipulate and manage data within structured database systems, using SQL as the primary tool. The objective 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 Modeling
• Introduction to the SQL Standard: Data Definition Language (DDL) and Data Manipulation Language (DML)
• Advanced SQL Standard: Subqueries and Common Table Expressions (CTEs)
• SQL Scripting

39h.

This module focuses on data transformation and data modeling, essential techniques for turning raw data into valuable information in business contexts. Through methodologies such as ETL, ELT, and EL, and using modeling standards such as dimensional modeling 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. Transformation of Data
   • Modeling Techniques
   • ETL/ELT/EL
   • Data Flow Orchestrators
2. Data Exploitation
   • APIs creation
   • Data Applications

30h.

Exploratory data analysis (EDA) is a crucial step in any analysis or modeling project. It helps us understand the structure, content and relationships within the data, which facilitates preparation for the development of Machine Learning models.

Modules:

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

36h.

The module is designed to provide students with an in-depth understanding of how to transform complex data into clear, effective and actionable visualizations. This course addresses both the fundamental principles of data visualization and the advanced techniques needed to create charts and dashboards that support data-driven decision making.

Modules:

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

36h.

Advanced data visualization is essential to convert complex information into clear insights that aid 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 Data World
• Key Elements of a Good Data Narrative
• Tools for Effective Dashboard Implementation: Power BI
• Python Integration for Preprocessing and Visualization

39h.

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:

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

27h.

This module is a cornerstone, as it provides the fundamental tools to understand and analyze data accurately and rigorously. In this module, we 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
• Statistics Fundamentals
  • Descriptive Statistics
  • Probability Distributions
• Linear Algebra
• Probability
  • Fundamental Concepts
  • Estimation Methods

27h.

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

Modules:

• Advanced Algorithms
• Support Vector Machines (SVM)
• Stochastic Gradient Descent
• Ensemble algorithms: AdaBoost, XGBoost, among others.
• Model Optimization
• Hyperparameter setting
• Feature selection
• Regularization
• Cross-validation
• Time Series Analysis
• Introduction to time series analysis
• Modeling and trends
• ARIMA and SARIMA models
• Networks
• Fundamental concepts of networks
• Learning network representations
• Link classification and prediction
• Reinforcement Learning
• Concept of reinforcement learning
• States, actions and rewards
• Reinforcement learning algorithms
• Anomaly Detection and Unbalanced Data Learning
• Identification of outlier observations using statistical methods, clustering, and supervised learning
• Techniques for handling unbalanced data, such as additional data collection, synthetic generation and modification of algorithms

27h.

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

Modules:

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

27h.

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

Modules:

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

27h.

Distributed processing has revolutionized 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, leveraging the power of computing clusters, 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 optimized structures that allow efficient manipulation of tabular data. Here it is important to know:
• Data loading from multiple sources.
• Column filtering and selection.
• Aggregations and transformations.
• Spark SQL: This Spark module provides an interface that allows you to use SQL queries to manipulate data, making it easier to analyze and obtain valuable information.
• Data Cleaning and Preparation: Prior to any analysis, the data must be ready for use:
• Null value detection and treatment.
• Missing data management.
• Data type conversion.
• Data standardization.
• Data Transformation and Enrichment:
• Date and time operations to correctly handle temporal data.
• String manipulation for formatting and transforming textual data.
• Creation of new columns to provide additional information for analysis.

9h.

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
• Artificial Intelligence Fundamentals

9h.

It provides a detailed understanding of the cloud adoption journey, including the technical, strategic and management aspects involved. Students will be guided through fundamental and advanced cloud computing concepts, effective migration strategies, and techniques for optimizing 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 Cloud Infrastructure Components
• Cloud Migration Planning and Strategies
• Design and Architecture of Cloud Solutions
• Security and Compliance Management in the Cloud
• Cloud Operations Management and Optimization
• Innovation and Advanced Cloud Services

9h.

This comprehensive module teaches how to strategically manage and use data to foster innovation in diverse organizational 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 capitalize on emerging opportunities in the technological and business environment.

Modules:

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

9h.

This module provides a comprehensive overview of data governance, highlighting its importance in managing and protecting data assets within an organization. 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 program that supports the organization's strategic and operational objectives.

Modules:

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

9h.

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

9h.

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:

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

3h.

• Team building.
• Choice of topic for final project.
• Assigning tutors.
• Project development with assigned tutor.
• Project delivery.
• Presentation of final project before a panel of experts.