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.
Objectives
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
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
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
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
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