Master´s Degree in Continuing Education in Big Data and Business Analytics
Inglés
Créditos 60 ECTS
PRESENTACIÓN
As a Big Data and Analytics Expert, the student will be able to provide qualified consulting to companies toward an improved decision-making resulting from gathering, analyzing, and interpreting large volumes of data.
Objetivos
The main purpose of this program is to help the student differentiate concepts such as Big Data, Business Intelligence, and the entire field of Analytics in a world where everything is called “Big Data”.
Acquire a global vision of Big Data & Analytics.
Identify strategies and business opportunities
Understand the type and implications of the required technology
Learn the profile of the right professionals
Gain the ability to communicate with Big Data & Analytics
Gain an overall understanding of the tools on the market
Understand and develop technical and scientific complexity
Manage technical aspects of BI/Big Data projects and work teams
Manage legal issues related to the use of data
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
- 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 Power BI
- Different types of Power BI
- First simple report
- Power Query. Data Sources
- Data Transformation
- Data Modeling
- Starting with DAX (I)
- Starting with DAX (II)
- Getting proficient in DAX (I)
- Getting proficient in DAX (II)
- Table and Matrix
- Trends
- How to properly filter your data
- Bookmarks
- Drill through
- Understanding Power BI Service in depth
- Sharing content in Power BI Service
- Comparing Power BI Service and Power BI Report Server
- Integrating Python and R in Power BI Desktop
- Introducing Bravo for Power BI Desktop
- 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
- 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
- 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
Másteres Destacados
Da el siguiente paso en tu carrera
Tu formación ya cuenta. Ahora conviértela en un título universitario.
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.