Inicio > Àrea de Ciències i Tecnologia > University Microcredential Introduction to digital content and artificial intelligence in tourism

1a Edició - Codi 24817030

Microcredenciales FSE
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[REQUISITOS_OTROS] => [ARG_VENTA] => To provide attendees with - an overview of the IT tools and information management services that are required for effective and competitive information management - a thorough understanding of AI tools, which can be applied to tourism business processes and data. - An overview of the landscape and possible applications of AI to tourism management. [ARG_VENTA2] => [AÑO_CURSO_DESC] => Curs 2024/2025 [MODALIDAD_EVALUACION] => [MODALIDAD_EVALUACION2] => [OBSERVACION_MATRICULA_1] => [OBSERVACION_MATRICULA_2] => [OBSERVACION_MATRICULA_3] => [SALIDA_PROFESIONAL] => Technical digital content managers, AI consultants in tourism, tourism resource management, hotel resource management [CRITERIO_ADMISION] => Candidates will be accepted on a first-come, first-served basis. In the event that there are more applications than places, the candidate's CV, previous ICT profile and English proficiency will be reviewed, and the faculty will rank the candidates in order based on these criteria. [CRITERIO_ADMISION2] => [CRITERIO_ADMISION3] => [FORMACION_APRENDIZAJE] => 1. Technical knowledge: Participants are provided with technical knowledge of the tools and techniques used in data management, analysis and visualisation, as well as an understanding of the fundamental concepts of artificial intelligence. 2. Analytical skills: The seminar develops analytical skills in participants so that they can identify patterns, trends and correlations in data, and use artificial intelligence models to make predictions and informed decisions. 3. Critical thinking: The seminar will help develop participants' critical thinking by discussing ethics in artificial intelligence and data privacy, and by analysing the biases and limitations of artificial intelligence models. 4. Creative thinking: The seminar aims to encourage creative thinking by challenging participants to seek innovative solutions to problems and encouraging them to explore new techniques and tools. [FORMACION_APRENDIZAJE2] => [FORMACION_APRENDIZAJE3] => [ANO_CURSO_DESC] => Curs 2024/2025 [programa] => Array ( [0] => Array ( [CODIGO_CURSO] => 24817030 [AÑO_CURSO] => 36 [CODIGO] => 7 [NOMBRE_MATERIA] => Introduction to AI [NOMBRE_MATERIA_VAL] => Introduction to AI [DESCRIPCION] => programa || programa2 || programa3 [DESCRIPCION1] => Fundamentals of AI: 1. Introduction to AI 2. Types of AI 3. Basic Algorithms and Techniques Machine Learning and Practical Applications 1. Supervised and Unsupervised Learning 2. Neural Networks and Deep Learning 3. Ethics and the Future of AI [DESCRIPCION2] => [DESCRIPCION3] => [DESCRIPCION1_VAL] => Fundamentals of AI: 1. Introduction to AI 2. Types of AI 3. Basic Algorithms and Techniques Machine Learning and Practical Applications 1. Supervised and Unsupervised Learning 2. Neural Networks and Deep Learning 3. Ethics and the Future of AI [DESCRIPCION2_VAL] => [DESCRIPCION3_VAL] => [ORDEN] => 1 ) [1] => Array ( [CODIGO_CURSO] => 24817030 [AÑO_CURSO] => 36 [CODIGO] => 1 [NOMBRE_MATERIA] => Programming in AI [NOMBRE_MATERIA_VAL] => Programming in AI [DESCRIPCION] => programa || programa2 || programa3 [DESCRIPCION1] => Introduction to Python and R 1. Python Fundamentals 2. R Fundamentals Advanced Programming in Python and R 1. Python Functions and Modules 2. Functions and Packages in R Data Manipulation and Analysis 1. Python Data Manipulation 2. Data Manipulation in R Introduction to Machine Learning 1. Machine Learning in Python 2. Machine Learning in R Practical Projects and Applications 1. Python Project 2. Project in R Practical Activities - Exercises and practical examples during each section - Group discussions on case studies - Mini-projects to apply the concepts learnt [DESCRIPCION2] => [DESCRIPCION3] => [DESCRIPCION1_VAL] => Introduction to Python and R 1. Python Fundamentals 2. R Fundamentals Advanced Programming in Python and R 1. Python Functions and Modules 2. Functions and Packages in R Data Manipulation and Analysis 1. Python Data Manipulation 2. Data Manipulation in R Introduction to Machine Learning 1. Machine Learning in Python 2. Machine Learning in R Practical Projects and Applications 1. Python Project 2. Project in R Practical Activities - Exercises and practical examples during each section - Group discussions on case studies - Mini-projects to apply the concepts learnt [DESCRIPCION2_VAL] => [DESCRIPCION3_VAL] => [ORDEN] => 2 ) [2] => Array ( [CODIGO_CURSO] => 24817030 [AÑO_CURSO] => 36 [CODIGO] => 6 [NOMBRE_MATERIA] => ML algotithms and examples. Generative AI [NOMBRE_MATERIA_VAL] => ML algotithms and examples. Generative AI [DESCRIPCION] => programa || programa2 || programa3 [DESCRIPCION1] => Introduction to Machine Learning Algorithms: 1. Machine Learning Fundamentals 2. Data Preprocessing Classification Algorithms: 1. Classification in Python 2. Classification in R Day 3: Regression Algorithms: 1. Regression in Python 2. Regression in R Clustering Algorithms: 1. Clustering in Python 2. Clustering in R Introduction to Generative AI: 1. Basic Concepts of Generative AI 2. Implementation of GANs in Python Natural Language Processing (NLP): 1. NLP Fundamentals 2. NLP Models in Python Practical Projects in Python: 1. Classification Project 2. Generative AI Project Practical Projects in R: 1. Clustering Project 2. NLP Project Practical Activities: - Exercises and practical examples during each section - Group discussions on case studies - Mini-projects to apply the concepts learnt [DESCRIPCION2] => [DESCRIPCION3] => [DESCRIPCION1_VAL] => Introduction to Machine Learning Algorithms: 1. Machine Learning Fundamentals 2. Data Preprocessing Classification Algorithms: 1. Classification in Python 2. Classification in R Day 3: Regression Algorithms: 1. Regression in Python 2. Regression in R Clustering Algorithms: 1. Clustering in Python 2. Clustering in R Introduction to Generative AI: 1. Basic Concepts of Generative AI 2. Implementation of GANs in Python Natural Language Processing (NLP): 1. NLP Fundamentals 2. NLP Models in Python Practical Projects in Python: 1. Classification Project 2. Generative AI Project Practical Projects in R: 1. Clustering Project 2. NLP Project Practical Activities: - Exercises and practical examples during each section - Group discussions on case studies - Mini-projects to apply the concepts learnt [DESCRIPCION2_VAL] => [DESCRIPCION3_VAL] => [ORDEN] => 3 ) [3] => Array ( [CODIGO_CURSO] => 24817030 [AÑO_CURSO] => 36 [CODIGO] => 5 [NOMBRE_MATERIA] => Commercial tools for ML: examples [NOMBRE_MATERIA_VAL] => Commercial tools for ML: examples [DESCRIPCION] => programa || programa2 || programa3 [DESCRIPCION1] => Introduction 1. Overview of Trading Tools 2. Free vs. Commercial Tools Google Colab 1. Exploring Google Colab 2. Modelling and Deployment in Google Colab Microsoft Azure ML Free Tier 1. Introduction to Microsoft Azure ML Free Tier 2. Modelling and Deployment on Azure ML Free Tier AWS Free Tier 1. Getting to know AWS Free Tier 2. Modelling and Deployment on AWS Free Tier Practical Activities - Exercises and practical examples during each section - Group discussions on case studies - Mini-projects to apply the concepts learnt [DESCRIPCION2] => [DESCRIPCION3] => [DESCRIPCION1_VAL] => Introduction 1. Overview of Trading Tools 2. Free vs. Commercial Tools Google Colab 1. Exploring Google Colab 2. Modelling and Deployment in Google Colab Microsoft Azure ML Free Tier 1. Introduction to Microsoft Azure ML Free Tier 2. Modelling and Deployment on Azure ML Free Tier AWS Free Tier 1. Getting to know AWS Free Tier 2. Modelling and Deployment on AWS Free Tier Practical Activities - Exercises and practical examples during each section - Group discussions on case studies - Mini-projects to apply the concepts learnt [DESCRIPCION2_VAL] => [DESCRIPCION3_VAL] => [ORDEN] => 4 ) [4] => Array ( [CODIGO_CURSO] => 24817030 [AÑO_CURSO] => 36 [CODIGO] => 3 [NOMBRE_MATERIA] => Computer network architecture [NOMBRE_MATERIA_VAL] => Computer network architecture [DESCRIPCION] => programa || programa2 || programa3 [DESCRIPCION1] => [DESCRIPCION2] => [DESCRIPCION3] => [DESCRIPCION1_VAL] => [DESCRIPCION2_VAL] => [DESCRIPCION3_VAL] => [ORDEN] => 5 ) [5] => Array ( [CODIGO_CURSO] => 24817030 [AÑO_CURSO] => 36 [CODIGO] => 2 [NOMBRE_MATERIA] => Web servers and services [NOMBRE_MATERIA_VAL] => Web servers and services [DESCRIPCION] => programa || programa2 || programa3 [DESCRIPCION1] => Introduction to Web Servers and Web Services 1. What are Web Servers and Web Services? 2. Basic Web Server Components Basic Web Server Configuration 1. Apache Installation and Configuration Installation and Configuration of Nginx - Installation of Nginx on different operating systems - Basic Configuration and Configuration Files - Serving Static Web Pages Web Services and Dynamic Applications 1. Introduction to Dynamic Web Applications 2. Configuring Servers for Dynamic Applications Web Services in the Cloud 1. Introduction to Cloud Computing 2. Configuration of Web Services in the Cloud Management and Maintenance of Web Servers 1. Monitoring and Maintenance of Servers 2. Scalability and High Availability Practical Projects and Case Studies 1. Complete Web Server Configuration Project 2. Case Studies and Group Discussion Practical Activities - Exercises and practical examples during each section - Group discussions on case studies - Mini-projects to apply the concepts learnt [DESCRIPCION2] => [DESCRIPCION3] => [DESCRIPCION1_VAL] => Introduction to Web Servers and Web Services 1. What are Web Servers and Web Services? 2. Basic Web Server Components Basic Web Server Configuration 1. Apache Installation and Configuration Installation and Configuration of Nginx - Installation of Nginx on different operating systems - Basic Configuration and Configuration Files - Serving Static Web Pages Web Services and Dynamic Applications 1. Introduction to Dynamic Web Applications 2. Configuring Servers for Dynamic Applications Web Services in the Cloud 1. Introduction to Cloud Computing 2. Configuration of Web Services in the Cloud Management and Maintenance of Web Servers 1. Monitoring and Maintenance of Servers 2. Scalability and High Availability Practical Projects and Case Studies 1. Complete Web Server Configuration Project 2. Case Studies and Group Discussion Practical Activities - Exercises and practical examples during each section - Group discussions on case studies - Mini-projects to apply the concepts learnt [DESCRIPCION2_VAL] => [DESCRIPCION3_VAL] => [ORDEN] => 6 ) [6] => Array ( [CODIGO_CURSO] => 24817030 [AÑO_CURSO] => 36 [CODIGO] => 4 [NOMBRE_MATERIA] => Database management and security [NOMBRE_MATERIA_VAL] => Database management and security [DESCRIPCION] => programa || programa2 || programa3 [DESCRIPCION1] => Introduction to Databases 1. What is a Database? 2. Components and Architecture of a Database Database Design and Modelling 1. Relational Database Design 2. NoSQL Database Design Database Management 1. Relational Database Administration 2. NoSQL Database Administration Cybersecurity Fundamentals 1. Introduction to Cybersecurity 2. Basic Security Measures Database Security Database Security Fundamentals 2. 2. Advanced Security Practices Practical Projects and Case Studies 1. Relational Database Management Project 2. NoSQL Database Management Project Practical Activities - Exercises and practical examples during each section - Group discussions on case studies - Mini-projects to apply the concepts learnt [DESCRIPCION2] => [DESCRIPCION3] => [DESCRIPCION1_VAL] => Introduction to Databases 1. What is a Database? 2. Components and Architecture of a Database Database Design and Modelling 1. Relational Database Design 2. NoSQL Database Design Database Management 1. Relational Database Administration 2. NoSQL Database Administration Cybersecurity Fundamentals 1. Introduction to Cybersecurity 2. Basic Security Measures Database Security Database Security Fundamentals 2. 2. Advanced Security Practices Practical Projects and Case Studies 1. Relational Database Management Project 2. NoSQL Database Management Project Practical Activities - Exercises and practical examples during each section - Group discussions on case studies - Mini-projects to apply the concepts learnt [DESCRIPCION2_VAL] => [DESCRIPCION3_VAL] => [ORDEN] => 7 ) ) [professors] => Array ( [0] => Array ( [DNI] => uni38256 [NOMBRE_PERSONA] => Miguel [APELLIDOS] => García Pineda [PDI] => 1 [DEPARTAMENTO_FACULTAD] => Departament d'Informàtica. Universitat de València [CARGO_FACULTAD] => Profesor/a Titular de Universidad [NPI] => M3248 [EMAIL_FACULTAD] => migarpi@uv.es [CARGO_EMPRESA] => [DIRECCION_URL_POSTGRADO] => [URL_LINKEDIN_POSTGRADO] => ) [1] => Array ( [DNI] => uni80065 [NOMBRE_PERSONA] => Carlos [APELLIDOS] => Hernani Morales [PDI] => 7 [DEPARTAMENTO_FACULTAD] => [CARGO_FACULTAD] => [NPI] => T1902 [EMAIL_FACULTAD] => hernani@uv.es [CARGO_EMPRESA] => Técnico/a Superior U.V.. Universitat de València [DIRECCION_URL_POSTGRADO] => [URL_LINKEDIN_POSTGRADO] => ) [2] => Array ( [DNI] => uni96985 [NOMBRE_PERSONA] => Fernando [APELLIDOS] => Mateo Jimenez [PDI] => 1 [DEPARTAMENTO_FACULTAD] => Departament d'Enginyeria Electrònica. Universitat de València [CARGO_FACULTAD] => Profesor/a Titular de Universidad [NPI] => I6169 [EMAIL_FACULTAD] => fermaji@uv.es [CARGO_EMPRESA] => [DIRECCION_URL_POSTGRADO] => [URL_LINKEDIN_POSTGRADO] => ) [3] => Array ( [DNI] => emp502210 [NOMBRE_PERSONA] => Carlos [APELLIDOS] => Nácher Collado [PDI] => 4 [DEPARTAMENTO_FACULTAD] => [CARGO_FACULTAD] => [NPI] => [EMAIL_FACULTAD] => [CARGO_EMPRESA] => Investigador Pas Universitat de Valencia [DIRECCION_URL_POSTGRADO] => [URL_LINKEDIN_POSTGRADO] => ) [4] => Array ( [DNI] => uni79953 [NOMBRE_PERSONA] => Oscar José [APELLIDOS] => Pellicer Valero [PDI] => 6 [DEPARTAMENTO_FACULTAD] => [CARGO_FACULTAD] => [NPI] => N8178 [EMAIL_FACULTAD] => ospeva@uv.es [CARGO_EMPRESA] => Investigador/a Doctor/a U.V. Junior. Universitat de València [DIRECCION_URL_POSTGRADO] => [URL_LINKEDIN_POSTGRADO] => ) [5] => Array ( [DNI] => uni99508 [NOMBRE_PERSONA] => Sonia [APELLIDOS] => Pérez Díaz [PDI] => 3 [DEPARTAMENTO_FACULTAD] => [CARGO_FACULTAD] => [NPI] => [EMAIL_FACULTAD] => [CARGO_EMPRESA] => Catedrático/a de Universidad. Universidad de Alcalá de Henares [DIRECCION_URL_POSTGRADO] => [URL_LINKEDIN_POSTGRADO] => ) [6] => Array ( [DNI] => uni55893 [NOMBRE_PERSONA] => Joan [APELLIDOS] => Vila Francés [PDI] => 1 [DEPARTAMENTO_FACULTAD] => Departament d'Enginyeria Electrònica. Universitat de València [CARGO_FACULTAD] => Profesor/a Titular de Universidad [NPI] => H1797 [EMAIL_FACULTAD] => vifranjo@uv.es [CARGO_EMPRESA] => [DIRECCION_URL_POSTGRADO] => [URL_LINKEDIN_POSTGRADO] => ) [7] => Array ( [DNI] => uni74629 [NOMBRE_PERSONA] => Yolanda [APELLIDOS] => Vives Gilabert [PDI] => 1 [DEPARTAMENTO_FACULTAD] => Departament d'Enginyeria Electrònica. Universitat de València [CARGO_FACULTAD] => Ayudante/a Doctor/a [NPI] => N7731 [EMAIL_FACULTAD] => yovigi@uv.es [CARGO_EMPRESA] => [DIRECCION_URL_POSTGRADO] => [URL_LINKEDIN_POSTGRADO] => ) ) [direccio] => Array ( [0] => Array ( [0] => Array ( [DNI] => uni25694 [NOMBRE_PERSONA] => José Rafael [APELLIDOS] => Magdalena Benedito [PDI] => 1 [DEPARTAMENTO_FACULTAD] => Departament d'Enginyeria Electrònica. Universitat de València [CARGO_FACULTAD] => Profesor/a Titular de Universidad [NPI] => H5145 [EMAIL_FACULTAD] => rmagdale@uv.es [CARGO_EMPRESA] => [DIRECCION_URL_POSTGRADO] => [URL_LINKEDIN_POSTGRADO] => ) ) ) )

University Microcredential Introduction to digital content and artificial intelligence in tourism

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 Metodologia
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Metodologia

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