Master's Degree in Artificial Intelligence and Big Data
English
Credits: 60 ECTS
PRESENTATION
Objectives
The overall objective of this program is to guide students toward developing a new technological profile and acquiring the management and software project development skills necessary in this changing world. The overall objective consists of the following specific objectives:
- Understand what Big Data is, why it is important, what it is used for, and the technological elements that underpin the concept.
- Understand intelligent systems capable of responding to current demands.
- Understand and design the architecture behind Big Data and Artificial Intelligence.
- Develop ideation and management methodologies for AI projects.
- Identify the factors that make an AI solution a viable project.
Methodology
At Educa PHAROS, we use a highly practical methodology focused on the direct application of knowledge in the student’s professional environment, combining theoretical content with real-world cases, digital tools, and support from a team of specialized instructors.
Program
The State of the Art in Artificial Intelligence
Philosophy of Artificial Intelligence
The Future of Artificial Intelligence
Project Development Process Using Artificial Intelligence
Data: Your Greatest Asset
Machine learning
Deep learning
Transformers
Generation of synthetic data
Hyperparameters in Artificial Intelligence Models
Linear Regression
Nonlinear regression and support vector machines (SVM)
Decision trees, random forests
Fuse logic and gradient down
Recommendation systems
Setting Up the Development 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
Sample 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)
Sample 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)
Recommendation systems
Recommendation systems. Metrics
RBM exercise
Self-organizing maps (SOM)
SOM exercises
Autoencoders (AE)
AE exercises
Proposed exercise
The Fourth Industrial Revolution
Digital Transformation in Companies
Fundamentals 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 Startup: Basic Concepts
Lean Startup. Tools
Scrum. Introduction
Scrum. Roles
Scrum: Ceremonies and Artifacts
Introduction
Project Ideation
Project Implementation
Provide guidance 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 Situation in a Booming Sector
Financing
Featured Startups
The Future of the AI Ecosystem
Starting an AI Company
Ethics. General Remarks
Examples of bias
Global Initiatives
Public Institutions and Regulations
AI in the SDGs
Data, information, knowledge, wisdom
Data Management (i)
Data Management (ii)
Corporate Performance Management
Databases
Business Intelligence
Data Warehousing
Big data
Hadoop
Spark
Hadoop Ecosystem (I)
Hadoop Ecosystem (II)
Hadoop Ecosystem (III)
Spark ecosystem
Installation and Configuration of Big Data Architectures
Educa PHAROS is a training solution for businesses that centralizes learning, simplifies user management, and allows you to track progress through dashboards and reports.
It is designed for organizations that want to develop internal skills, standardize role-based training, and have traceability and learning metrics.
It includes learning paths by profile, user and permission management, progress reports, certification, and access to masterclasses or live sessions (depending on the plan).
It is based on best practices in security, access control, learning traceability, and implementation support. Customize this text according to your commitments (SLA, compliance, etc.).
Educa PHAROS is a next-generation training model that places a company’s human capital at the forefront. Through a platform that adapts to each company’s corporate identity and offers a total of more than 900 courses, it provides tailored training for each organization. The unlimited flat-rate plan provides each company with the number of courses that best suits its needs, as well as the ability to determine which employees will have access.
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