Máster en Data Science

Program Overview

The Máster en Data Science is a bootcamp-style program that teaches students the skills needed to develop data-driven projects using in-demand technologies. It provides a foundation in programming, databases, and data science concepts, and covers advanced analytics, visualization, and productization. The program is led by Making Science, a digital marketing and technology consultancy, and features real-world case studies, projects, and masterclasses by industry professionals. Graduates are prepared for careers in data science, a field with high demand and growth.

Program Outline

Degree Overview:

The Máster en Data Science is a program designed to teach students how to develop data-driven projects using the most popular and in-demand technologies. It is a bootcamp suitable for professionals in big data, data science, or business intelligence, as well as those interested in transitioning their careers. The program provides the theoretical foundation to support the results and decisions derived from data analysis, and equips students with the skills to complete projects from start to finish. It also introduces the typical workflow of a data team, enabling seamless integration into existing teams.

Outline:

• UA1.
Intro a la programación y a Python
• UA2.
Funciones
• UA3.
Estructuras de datos
• UA4.
Bucles e iteraciones
• UA5.
Ficheros. Control de excepciones
• UA6.
Orientación a objetos
• Module 2.
• Complemento formativo – Bases de datos (6 ECTS): Introduces relational databases and SQL for data acquisition and transformation.
• UA1.
Introducción a las bases de datos
• UA2.
SQL. Lenguaje de manipulación de datos
• UA3.
SQL. Lenguaje de definición de datos
• Bases de datos NoSQL.
MongoDB
• Module 3.
• Introducción y fundamentos de la ciencia de datos (2 ECTS): Introduces the concepts of Big Data and Data Science, their origins, the need for analysis, and the rise of these disciplines.
It also lays the mathematical and statistical foundations for understanding the types of analysis used in Data Science projects.
• UA1.
Introducción al Big Data y al Data Science
• UA2.
Fundamentos de matemáticas y estadística
• Module 4.
ETL.
• UA1.
Trabajar con datos y bases de datos
• UA2.
Ecosistema HADOOP
• Sistema de archivos HDFS
• Paradigma MapReduce
• Apache HIVE
• Gestor de recursos YARN
• UA3.
Python para Análisis de Datos
• ETLs con python
• Pandas, Numpy y librerías de tratamiento de datos
• pySpark
• Module 5.
• Análisis de datos y machine learning (6 ECTS): Covers advanced analytics, including various types of Machine Learning, Deep Learning, and time series analysis, along with data cleaning and transformation techniques.
• UA1.
Machine Learning – Aprendizaje Supervisado
• Introducción al ML
• Técnicas de aprendizaje supervisado: Clasificación
• Técnicas de aprendizaje supervisado: Regresión
• UA2.
Machine Learning – Aprendizaje no Supervisado
• Técnicas de aprendizaje no supervisado: Clustering
• Técnicas de aprendizaje no supervisado: Sistemas de recomendación
• Técnicas de aprendizaje no supervisado: Extracción de variables
• UA3.
Series temporales
• Modelos ARIMA
• Module 6.
• Redes neuronales (2 ECTS): Explains and applies artificial neural networks.
• UA1.
Deep Learning
• Introducción al Deep Learning
• Técnicas de Redes neuronales
• UA2.
Procesamiento del Lenguaje Natural (NLP)
• Técnicas de procesamiento del lenguaje natural
• Module 7.
• Visualización de datos (4 ECTS): Focuses on data visualization, from theoretical foundations to creating graphs and dashboards using industry-leading tools.
• UA1.
Fundamentos de la Visualización de Datos
• ¿Qué es la visualización de datos?
• Tipos de visualizaciones
• Storytelling
• ¿Qué es el Business Intelligence?
• Herramientas de Visualización de Datos
• UA2.
CARTO
• Creación de mapas interactivos
• Analítica geográfica
• UA3.
Power BI y TABLEAU
• Ingesta y tratamiento de datos
• Modelado Avanzado de datos
• Creación de cuadros de mando
• UA4.
Visualización de grandes volúmenes de datos
• Técnicas y herramientas para visualizar Big Data
• MongoDB charts
• Kibana
• Module 8.
• Plataformas cloud y productivización (2 ECTS): Covers productizing proof-of-concept projects, including code security and cloud execution.
• UA1.
Productivización del código
• Github y git
• Testing y seguridad del código
• Entornos virtuales
• Docker
• UA2.
Data Science en la nube
• Proveedores de cloud: Amazon Web Services, Microsoft Azure, Google Cloud
• AWS en detalle
• Module 9.
• Proyecto grupal (6 ECTS): Develops an end-to-end data project based on a potential real-world business case.
• Búsqueda de caso de uso
• Análisis de fuentes de datos públicos / privados
• Diseño de la infraestructura y flujos de datos
• Ingesta y limpieza de los datos en la base de datos adecuada
• Diseño e implementación de las analíticas que posibiliten el caso de uso
• Diseño y construcción de los cuadros de mando del producto
• The project is developed throughout the course, applying knowledge from each module to the chosen use case.
• Module 10.
• Mentoring profesional (2 ECTS): Masterclasses by active data professionals, providing insights into real-world use cases in various industries.
• Module 11.
• Especialización – Analítica web (3 ECTS): An alternative to company internships, focusing on web data analysis.
• Module 12.
• Especialización – Gestión de proyectos (3 ECTS): An alternative to company internships, focusing on team participation and management in modern companies.
• Module 13.
• TFM (14 ECTS): Master's thesis.
Students delve deeper into their learning by completing a project from start to finish in a real-world use case.
• Module 14.

Teaching:

The program uses a Bootcamp format, combining digital and flexible content (30%) with practical and experiential learning on campus (70%). The program emphasizes "learning by doing" through real-world case studies and projects. The faculty consists of active professionals managing projects across various sectors and top-level academics in portfolio, program, and project management.

Careers:

The program prepares students for careers in data science, a field with high demand and growth. Graduates are equipped to work in sectors such as banking, telecommunications, and research.

Other:

The program is offered in Spanish and is located in Alcobendas. It is a 14-week program with a 70% mix of content, including digital, flexible, practical, and experiential learning.