Master´s Degree in Continuing Education in Artificial Intelligence and Big Data
Inglés
Créditos 60 ECTS
PRESENTACIÓN
Big Data is defined as the process of collecting large heterogeneous amounts of data for analysis (sometimes in real time). This data set is so large and complex that traditional processing means are ineffective, hence, the new forms and computer applications capable of managing and processing all this amount of data must be developed. Big Data was born as a response to these challenges, and above all, so that through data analysis, we may be able to attract more customers, avoid losing them, and improve our operational processes.
This program aims at guiding students in their understanding of the technologies behind Big Data and Artificial Intelligence. As a matter of fact, Artificial Intelligence applied to the real world requires extraordinary vision, and this course intends to show the path to understanding the technology, its applications, and the execution of advanced AI project management models.
Objetivos
The overall objective of this program is to guide the student towards developing a new technological profile and acquiring the management and software project development skills necessary in this changing world. The global objective consist of the following specific objectives:
- Understand what Big Data is, why talk about Big Data, what it is for and the technological elements that support the concept.
- Understand intelligent systems capable of responding to current demands.
- Know and design the architecture behind Big Data and Artificial Intelligence
- Develop ideation and management methodologies for AI projects
- Identify the factors that convert an AI solution into a viable project.
Metodología
En Educa PHAROS trabajamos con una metodología eminentemente práctica, orientada a la aplicación directa de los conocimientos en el entorno profesional del alumno, combinando contenidos teóricos con casos reales, herramientas digitales y el acompañamiento de un equipo docente especializado.
Programa
- State of the art of artificial intelligence
- Philosophy of artificial intelligence
- Future of artificial intelligence
- Project development process with artificial intelligence
- Data, your greatest asset
- Machine learning
- Deep learning
- Transformers
- Generation of synthetic data
- Hyperparameters in artificial intelligence models
- Linear regression
- Non-linear regression and support vector machines (SVM)
- Decision trees, random forests
- Fuse logic and gradient down
- Recommendation systems
- Preparation of the working environment: Anaconda, Visual Studio Code and Python
- Input dataset and data preprocessing
- TensorHub, TensorFlow and Keras
- Image processing
- Generation of artificial intelligence models
- Introduction
- Simple, multiple and logistic linear regression (I)
- Simple, multiple and logistic linear regression (II)
- Support vector machines (SVM)
- Decision trees
- KNN (k-nearest neighbors)
- Naive Bayes
- Evaluation of supervised models
- Example exercise
- Proposed exercise
- Introduction to clustering. purconsider and metrics
- K-means clustering
- Hierarchical clustering, other techniques and examples
- Principal component analysis (PCA)
- PCA example exercise
- Artificial Neural Networks (ANN) (I)
- Artificial Neural Networks (ANN) (II)
- Artificial Neural Networks (ANN) (III)
- Example exercise
- Proposed exercise
- Introduction
- Review. Artificial neural network (ANN)
- Review. ANN exercises
- Convolutional Neural Networks (CNN)
- CNN Exercises
- Natural language processing (I)
- Recurrent neural networks (RNN) (I)
- Recurrent neural networks (RNN) (II)
- Natural language processing (II)
- RNN Exercise
- Boltzmann Machines (BM)
- Restricted Boltzmann Machines (RBM)
- Recommender systems
- Recommender systems. metrics
- RBM exercise
- Self-organizing maps (SOM)
- SOM exercises
- Autoencoders (AE)
- AE exercises
- Proposed exercise
- The fourth industrial revolution
- Digital transformation in companies
- Fundaments and key points
- Benefits
- Enabling technologies
- Big data
- Cloud computing
- Blockchain
- Artificial intelligence
- Augmented and virtual reality
- BIM
- Collaborative robots
- Additive manufacturing
- Hyperconnectivity
- IoT
- Manufacturing execution system (MES)
- Process integration and efficiency
- Use cases
- New methodologies. agile, lean startup or design thinking
- Business change management
- Introduction
- Key elements in AI project management
- AI project characteristics
- Introduction to the main agile and ideation methodologies
- Methodology integration
- Introduction
- Phase I. Empathize
- Phase II. Define
- Phase III. Devise
- Phase IV. Prototype
- Lean start-up. Basic concepts
- Lean start-up. Tools
- Scrum. Introduction
- Scrum. Roles
- Scrum. Ceremonies and artifacts
- Introduction
- Project ideation
- Project implementation
- Advise on implementing methodologies
- Summary and conclusions
- Financial sector
- Retail sector
- Industrial sector
- Agricultural sector
- Health sector
- Logistics and operations
- Marketing
- Sales and customer service
- Finance and control
- People analytics
- Current scenario of a booming sector
- Financing
- Featured start-ups
- Future of the AI ecosystem
- Starting an AI company
- Ethics. General remarks
- Bias examples
- Global initiatives
- Public Institutions and regulations
- AI in the SDGs
- Data information knoledge wisdom
- Data management (i)
- Data management (ii)
- Corporate performance management
- Databases
- Business intelligence
- Datawarehousing
- Big data
- Hadoop
- Spark
- Hadoop ecosystem (I)
- Hadoop ecosystem (II)
- Hadoop ecosystem (III)
- Spark ecosystem
- Installation and configuration of big data architectures
- Analytics
- Main algorithms (I)
- Main algorithms (II)
- Machine learning and deep learning
- Internet of things
- Introduction to SQL
- Database manipulation
- Data types
- Normalization
- Creating tables in SQL
- Table manipulation
- SQL table query
- Table joining in SQL
- Table combinations and views
- Other SQL commands
- String functions and numeric functions (I)
- Numeric function (II)
- Date and time functions
- Other functions
- Loops, conditionals and triggers in SQL
- Data warehousing introduction
- Databases in a data warehouse. Stage
- Databases in a data warehouse. ODS (I)
- Databases in a data warehouse. ODS (II)
- Databases in a data warehouse. DDS
- Introduction
- Polyglot persistence
- ACID model
- New trends
- Comparison between SQL and NOSQL
- Data models
- Aggregation models
- Key-value aggregation models
- Document-oriented data models
- Column-oriented aggregation models
- Graph data model
- Distributed databases
- Strategies for the design of distributed DBS
- NOSQL database design
- Hadoop distributed file system (HDFS)
- Example of a NOSQL aggregation database
- Riak. Example of a key-value database
- MongoDB. Example of a document database
- Neo4J. Example of a graph NOSQL database
- HBASE. Example of a columnar database
Másteres Destacados
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Educa PHAROS es una solución formativa para empresas que centraliza el aprendizaje, facilita la gestión de usuarios y permite medir el progreso mediante paneles y reportes.
Está dirigido a organizaciones que desean desarrollar habilidades internas, estandarizar la formación por roles y disponer de trazabilidad y métricas de aprendizaje.
Incluye itinerarios por perfiles, gestión de usuarios y permisos, reportes de progreso, certificación, y acceso a masterclass o sesiones en vivo (según plan).
Se apoya en buenas prácticas de seguridad, control de accesos, trazabilidad del aprendizaje y soporte de implementación. Personaliza este texto según tus compromisos (SLA, compliance, etc.).
Educa PHAROS es un modelo formativo de nueva generación que posiciona al capital humano de la empresa a la vanguardia. A través de una plataforma que se adapta a la imagen corporativa de cada empresa y con un total de más de 900 cursos se consigue una formación específica para cada organización. La tarifa plana ilimitada, proporciona a cada empresa el número de cursos que se ajuste a sus necesidades y también la posibilidad de determinar qué empleados podrán tener acceso.