+ 60 ECTS Credits

Master´s Degree in Big Data and Business Analytics + 60 ECTS Credits

Master´s Degree in Big Data and Business Analytics + 60 ECTS Credits

Do you want to turn large volumes of data into strategic decisions and lead data projects? Specialize in Business Analytics and Big Data, combining BI, architecture, and advanced analytics.

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Master´s Degree in Big Data and Business Analytics + 60 ECTS Credits

Verifiable program data for this master's degree

  • 60 ECTS Credits
  • Faculty of Innovación Tecnológica
  • Internships at real companies
  • STRUCTURALIA qualification

What will you achieve by completing this master's degree?

Big Data vs BI

Differentiate Big Data and Business Intelligence and understand the full spectrum of business analytics.

Robust data architectures

Design and evaluate resilient technology architectures for managing and processing massive data volumes.

Advanced analytics

Apply Big Data Analytics methodologies to extract value and identify predictive patterns.

Power BI and SQL

Master Power BI with DAX, advanced visualization, and relational databases with SQL for efficient queries.

Course curriculum

9 modules covering everything from Big Data and Business Intelligence to power BI modeling, DAX, and advanced visualization. Click to see full content.

01

Module 1. Introduction To Big Data

Didactic Unit 1. Data In Companies

  • Data information
  • Knowledge wisdom
  • Data Management (I)
  • Data Management (II)
  • Corporate performance management
  • Databases

Didactic Unit 2. From Business Intelligence To Big Data

  • Business intelligence
  • Datawarehousing
  • Big data
  • Hadoop Spark

Didactic Unit 3. Big Data Technology Architectures

  • Hadoop Ecosystem (I)
  • Hadoop Ecosystem (II)
  • Hadoop Ecosystem (III)
  • Spark Ecosystem
  • Installation and configuration of Big Data architectures

Didactic Unit 4. Big Data Analytics

  • Analytics
  • Main algorithms (I)
  • Main algorithms (II)
  • Machine Learning and Deep Learning
  • Internet Of Things
02

Module 2. Power Bi Course

Didactic Unit 1. Getting Started With Power Bi

  • Introduction to Power BI
  • Different types of Power BI: is it really free?
  • Let's dive in: simple first report
  • Power Query: data source
  • Data transformation

Didactic Unit 2. Data Modelling And Dax

  • Data modelling
  • Starting with DAX (I)
  • Getting started with DAX (II)
  • Mastering DAX (I)
  • Mastering DAX (II)

Didactic Unit 3. Data Visualisation

  • Table and matrix
  • Trends
  • How to filter your data properly
  • Scoreboards
  • Obtaining details

Unit Didactic Unit 4. Taking Power Bi To The Next Level

  • Understanding Power BI Service
  • Sharing content in Power BI Service
  • Comparing Power BI Service and Power Report Service
  • Integrating Python and R in Power BI Desktop
  • Introducing Bravo for Power BI Desktop
03

Module 3. Relational Databases. Sql. Design Of a Datawarehouse (big Data).

Didactic Unit 1. First Steps In Sql

  • Introduction to SQL
  • Database manipulation
  • Data types
  • Standardisation
  • Creating tables in SQL

Didactic Unit 2. Sql Commands

  • Manipulation of tables
  • Querying tables in SQL
  • Combining tables in SQL
  • Combinations of tables and views
  • Other SQL commands

Didactic Unit 3. Sql Functions

  • Functions for strings and numeric functions (I)
  • Numerical functions (II)
  • Date and time functions
  • Other functions
  • Loops, conditionals and triggers in SQL

Didactic Unit 4. Datawarehouse Design

  • Introduction to datawarehousing
  • Databases in a datawarehouse. Stage
  • Databases in a datawarehouse. ODS (I)
  • Data in a datawarehouse. ODS (II)
  • Databases in a datawarehouse. DDS
04

Module 4. Programming Languages. Python And R (big Data)

Didactic Unit 1. Introduction To Python, Installation And Configuration Of The Development Environment

  • Introduction to Python
  • Features and applications
  • Installing Python
  • Setting up a development environment

Didactic Unit 2. Data Types, Variables, Operators And Expressions

  • Basic Python syntax
  • Variables and data types
  • Operators and expressions
  • Use of comments

Didactic Unit 3. Flow Control: Loops And Conditionals

  • Introduction to flow control
  • Conditional structures (if, elif, else)
  • Loops (for and while)
  • Loop control (break and continue)

Didactic Unit 4. Libraries For Data Analysis: Numpy, Pandas And Matplotlib

  • Data analysis with NumPy
  • Pandas
  • Matplotlib

Didactic Unit 5. Filtering And Data Extraction

  • How to use loc in Pandas
  • How to delete a column in Pandas

Didactic Unit 6. Pivot Tables

  • Pivot tables in pandas

Didactic Unit 7. Groupby And Aggregation Functions

  • The group of pandas

Didactic Unit 8. Merging Dataframes

  • Python Pandas merging data frames

Didactic Unit 9. Visualisation Of Data With Matplotlib And Seaborn

  • Matplotlib
  • Seaborn

Didactic Unit 10. R As a Tool For Big Data

  • Introduction to R
  • What do you need?
  • Data types
  • Descriptive and Predictive Statistics with R
  • R integration in Hadoop

Unidad Didáctica 11. Pre-procesamiento & Procesamiento de Datos

  • Data collection and cleansing (ETL)
  • Statistical inference
  • Regression models
  • Hypothesis testing

Didactic Unit 12. Data Analysis

  • Business Analytics Intelligence
  • Graph theory and social network analysis
  • Presentation of results
05

Module 5. Nosql Databases (big Data)

Didactic Unit 1. Introduction To Nosql Databases

  • Concept of NoSQL Databases
  • Advantages and disadvantages of NoSQL Databases
  • Main characteristics of NoSQL Databases

Didactic Unit 2. Types Of Nosql Databases

  • Documentary databases
  • Columns databases
  • Key-Value Databases
  • Network database

Didactic Unit 3. Mongodb: a Documentary Database

  • Introduction to MongoDB
  • MongoDB features and architecture
  • Data modelling in MongoDB
  • MongoDB queries and operations
  • Scalability and performance in MongoDB

Didactic Unit 4. Other Sgbd Nosql

  • Apache Cassandra
  • CouchDB
  • Redis
  • Amazon DynamoDB
  • NeoJS

Didactic Unit 5. Installation, Configuration And Use Of Mongodb

  • Design of the data structure
  • Configuration of the development environment
  • Installation and configuration of MongoDB
  • Creating and manipulating collections in MongoDB
  • Importing and exporting data in MongoDB

Unit Didactic Unit 6. Advanced Mongodb Queries And Operations

  • Indexes and query optimisation in MongoDB
  • Data aggregation in MongoDB
  • Transactions in MongoDB
  • Replication and high availability in MongoDB
  • Backup and recovery in MongoDB

Didactic Unit 7. Nosql Database Use Cases

  • Web and mobile applications
  • Big Data and data analysis
  • Internet of Things (IoT)
  • Recommender systems
  • Social media and social networks

Didactic Unit 8. Integration Of Nosql Databases With External Technologies

  • Introduction to data integration
  • Integration with programming languages (Python, Java, etc.)
  • Integration with Business Intelligence (BI) tools
  • Integration with cloud storage systems

Didactic Unit 9. Security And Privacy In Nosql Databases

  • NoSQL Database Security Concepts
  • Authentication and authorisation in MongoDB
  • Data Encryption in NoSQL Databases
  • Auditing and Access Control in NoSQL Databases
06

Module 6. Preparation Of Self-service Data

Didactic Unit 1. Data Preparation

  • Introduction
  • Data literacy
  • Working with data
  • Solutions and techniques for data processing
  • Data quality management

Didactic Unit 2. Data Preparation With Excel

  • Working with data in Excel
  • Data set (DATASET)
  • Data Cleasing with Excel
  • Data Wrangling with Excel
  • Data Blending in Excel

Didactic Unit 3. Data Preparation With Talend

  • Talend Data Preparation Desktop Installation
  • Working with data in Talend
  • Data Cleasing with Talend
  • Data Wrangling with Talend
  • Data Blending with Talend

Didactic Unit 4. Data Preparation With Dataprep By Trifacta

  • Registration in dataprep by Trifacta
  • Working with data with Dataprep by Trifacta
  • Data Cleasing with Trifacta
  • Data Wrangling with Dataprep by Trifacta
  • Data Blending with Dataprep by Trifacta
07

Module 7. Data Mining, Machine Learning And Deep Learning (big Data)

Didactic Unit 1. Supervised Learning (i)

  • Introduction
  • Linear, multiple and logistic regression (I)
  • Linear, multiple and logistic regression (II)
  • Support Vector Machine (SVM)
  • Decision trees

Didactic Unit 2. Supervised Learning (ii)

  • KNN (K-Nearest Neighbors)
  • Naive bayes
  • Evaluation of supervised models
  • Sample exercise
  • Proposed exercise

Didactic Unit 3. Unsupervised Learning

  • Introduction to clustering. purpose and metrics
  • K-means clustering
  • Hierarchical clustering, other techniques and examples
  • Principal Component Analysis (PCA)
  • Sample PCA exercise

Didactic Unit 4. Deep Learning

  • Artificial Neural Networks (ANN) (I)
  • Artificial Neural Networks (ANN) (II)
  • Artificial Neural Networks (ANN) (III)
  • Sample exercise
  • Proposed exercise
08

Module 8. Advanced Deep Learning

Didactic Unit 1. Supervised Deep Learning (i)

  • Introduction and Review of Artificial Neural Networks (ANN)
  • Convolutional Neural Networks (CNN). Introduction and use cases
  • CNN. Intuition
  • CNN. Mathematical description
  • CNN. Programming example with Python and TensorFlow
  • Exercise. Artificial vision with CNN

Didactic Unit 2. Supervised Deep Learning (ii)

  • Time Series Review
  • Recurrent Neural Networks (RNN). Introduction and use cases
  • RNN. Intuition
  • RNN. Mathematical description
  • RNN. Programming example with Python AND TensorFlow
  • Exercise. Time Series with RNN

Didactic Unit 3. Unsupervised Deep Learning (i)

  • Review of Recommender Systems
  • Deep Bolztmann Machines (DBM). Introduction and use cases [Video].
  • DBM. Intuition
  • DBM. Mathematical description
  • DBM. Programming example with Python and TensorFlow
  • Exercise. DBM Recommendation System

Didactic Unit 4. Unsupervised Deep Learning (ii)

  • Anomaly detection
  • Self-Organising Maps (SOM). Introduction and intuition
  • SOM. Mathematical description
  • AutoEncoders (AE). Introduction and intuition
  • AE. Mathematical description
  • Exercise. Anomaly detection with SOM and AE.
09

Module 9. Artificial Intelligence

Didactic Unit 1. Introduction To Artificial Intelligence

  • State of the art of artificial intelligence
  • Philosophy of artificial intelligence
  • Future of artificial intelligence
  • Project development processes with artificial intelligence
  • Data, your greatest asset

Didactic Unit 2. Types Of Artificial Intelligence

  • Machine learning
  • Deep learning
  • Transformers
  • Synthetic data generation
  • Hyperparameters in artificial intelligence models

Didactic Unit 3. Introduction To Machine Learning Algorithms

  • Linear regression
  • Non-linear Regression and Support Vector Machine (SVM)
  • Decision trees and random forests
  • Fuzzy logic and gradient descent
  • Recommender systems

Didactic Unit 4. Turnkey Project With Artificial Intelligence

  • Preparation of the working environment. Anaconda, Visual Studio Code and Python.
  • Data input and processing datasets
  • TensorHub, TensorFlow and Keras
  • Image processing
  • Artificial intelligence modelling

Teaching faculty

Teaching team with professional experience in Master´s Degree in Big Data and Business Analytics + 60 ECTS Credits.

AR

Adrián Rodríguez Porres

Faculty of Gestión de Proyectos en Arquitectura

metodología BIM

VG

Vidal Gascón Culebras

Inteligencia Artificial aplicada

JM

Julia Molina Virués

Faculty of Gestión de Proyectos en Arquitectura

arquitectura sostenible

Charo Rey Zabalza

Charo Rey Zabalza

Faculty of Medioambiente y Sostenibilidad

economía circular

ÁS

Ángel Sanz Bernabé

control de calidad

Isabella Sánchez Bermúdez

Isabella Sánchez Bermúdez

Faculty of Dirección de Proyectos e Innovación Tecnológica

comunicación estratégica

Rogelio Delgado Mingorance

Rogelio Delgado Mingorance

Faculty of Ingeniería Industrial

gestión y dirección de proyectos

Miguel Ángel Aparicio Jiménez

Miguel Ángel Aparicio Jiménez

What jobs could you get?

With this master's degree you could apply for roles such as:

  • Chief Data Officer (CDO)
  • Big Data Solutions Architect
  • Strategic Big Data Consultant
  • Senior Data Scientist
  • Lead Data Engineer
  • Data Warehouse Consultant

Methodology

Our teaching methodology

Equipo diverso de ingeniería trabajando con portátiles en un laboratorio maker con impresoras 3D
Learn whenever you want

Progress at your own pace, from wherever you are,
with close support.

Realistic

The EDUCA LXP methodology avoids excessively theoretical knowledge and inefficient practical methods. The combination of constantly updated content with personalized guidance throughout the learning process makes EDUCA LXP a unique methodology.

Student First

The EDUCA LXP methodology and EDUCA EDTECH Group's training place the student at the center of the learning experience, drawing on their feedback. Their feedback drives our continuous improvement.

Artificial Intelligence

Personalized learning would not be possible without a precise combination of academic experience, technology research, and Artificial Intelligence. That is why we rely on in-house AI tools, tailored to each school in the group.

Active industry professionals

Our teaching team, besides being specialists in their field, has specific training in the technology tools that make up the EDUCA EDTECH ecosystem.

Rankings and recognitions

Assessed by independent online higher-education organizations.

Organization of American States (OAS)

Since 2010, Structuralia has run a scholarship program for master's degrees in Spanish and English together with the Organization of American States (OAS), aimed at outstanding professionals from the Americas as part of the Partnerships for Education and Training Program (PAEC), which has already benefited more than 3,500 students from the region.

APICE

Structuralia and the Pan-American Association of Educational Credit Institutions (APICE) offer a specialized training scholarship program for professionals from Latin America and the Caribbean in master's degrees specialized in the STEM sector.

Educa Edtech

The Educa Edtech Foundation was created to foster personal and professional growth while championing knowledge transfer as a way to change the world, overseeing the rollout of solutions and granting aid to those who deserve it.

Financial Magazine

This outlet's 2025 Ranking has included up to 10 Structuralia master's programs across several categories, among them: Data Science, Big Data, Artificial Intelligence, Urban Planning, Energy and the Environment.

Mundo Posgrado

In the latest edition of its regular annual rankings, Structuralia's master's programs made the Top 10 best master's degrees in Spain in the Renewable Energy and Integrated Management Systems categories.

Escudo Digital

The Master's program in Cybersecurity and Information Risk is included in the Top 25 best cybersecurity training programs according to "Escudo Digital".

Our numbers

92%of our students would recommend us
30+countries with active students
60%of our faculty are active industry professionals
15 yearstraining specialists in engineering and architecture

STRUCTURALIA Scholarships

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