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.
Objectives
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.
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 case studies, digital tools, and the guidance of a specialized teaching team.
Program
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, 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 progress to be tracked 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 tracking, 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 image and offers a total of more than 900 courses, it provides training tailored to each organization’s specific needs. 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.
Digital Transformation and Smart Infrastructure 
English 
Credits: 60 ECTS
Leadership and Management 
Spanish
60 ECTS