Máster Universitario en Análisis de Datos Masivos (Big Data)

Program Overview

The Master in Big Data at Universidad Internacional de La Rioja focuses on advanced knowledge in Data Science and Machine Learning technologies, preparing students for careers as Data Architects, Data Engineers, or Data Scientists. The program offers a comprehensive curriculum covering the entire data lifecycle and provides hands-on experience through state-of-the-art facilities and partnerships with industry leaders. The program is designed to be compatible with professional life and is available in both Spanish and English versions.

Program Outline

Degree Overview:

The Master in Big Data is a program designed for engineers, statisticians, mathematicians, and data analysts seeking to expand their knowledge in the field of Information and Communication Technologies (ICT).

Objectives:

• To equip students with advanced knowledge in Data Science and Machine Learning technologies.
• To develop expertise in managing large data volumes and infrastructure, including cloud computing, machine learning, and business process analysis.
• To prepare students for careers as Data Architects, Data Engineers, or Data Scientists.
• Includes modules on cloud computing, distributed systems, databases, data governance, business analytics, data processing, machine learning, and data visualization.
• Offers access to the AWS Academy for preparing for the AWS Certified Cloud Practitioner and AWS Certified Machine Learning Specialty certifications.

Structure:

• The program is structured into a comprehensive curriculum with both mandatory and optional modules.
• Students can choose between a practical or research track.
• The program culminates in the presentation of a final project that applies the acquired knowledge and utilizes cutting-edge tools.

Course Schedule:

• The program is offered in both online and on-campus formats.
• The on-campus program is delivered in Alcobendas, Madrid, and lasts for 10 months.
• The online program is available in two formats: 100% online and a combination of online and on-campus.

Individual Modules:

Mandatory Modules:

• Cloud Computing Architectures: Covers the fundamentals of cloud computing, including cloud service models, deployment models, and key cloud providers like AWS, Azure, and Google Cloud.
• Applied Advanced Statistics: Focuses on advanced statistical concepts and techniques relevant to data analysis, including hypothesis testing, regression analysis, and time series analysis.
• Distributed Systems Computing: Explores the principles and technologies behind distributed systems, including distributed databases, message queues, and distributed file systems.
• Next-Generation Databases: Introduces students to modern database technologies, including NoSQL databases, graph databases, and in-memory databases.
• Business Analytics: Explores the application of data analytics techniques to business problems, including customer segmentation, market analysis, and forecasting.
• Machine Learning: Introduces the concepts and algorithms of machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
• Final Master's Project: Requires students to apply their acquired knowledge and skills to a real-world data analysis project.

Optional Modules:

• Methodology and Design of Research: Provides an overview of research methodologies and techniques.
• Practicum: Offers practical experience in a real-world setting.
• Company Internship I: Provides an opportunity for students to gain practical experience in a company setting.
• Company Internship II: Offers a second opportunity for students to gain practical experience in a company setting.

Assessment:

Assessment Methods:

• The program utilizes a variety of assessment methods, including:
• Assignments: Practical exercises and projects to apply learned knowledge and skills.
• Presentations: Oral presentations to demonstrate students' ability to communicate their findings effectively.
• Case Studies: Real-world scenarios to analyze and solve data-related problems.
• Final Master's Project: A comprehensive project that demonstrates students' ability to apply their knowledge and skills to a real-world problem.

Assessment Criteria:

• Knowledge and Understanding: Demonstrating a deep understanding of the concepts and theories covered in the program.
• Application of Knowledge: Applying learned knowledge and skills to solve real-world problems.
• Analytical Skills: Analyzing data effectively and drawing meaningful conclusions.
• Communication Skills: Communicating findings clearly and effectively to both technical and non-technical audiences.
• Problem-Solving Skills: Identifying and solving data-related problems creatively and effectively.

Teaching:

Teaching Methods:

• The program employs a variety of teaching methods, including:
• Lectures:
Interactive lectures to deliver key concepts and theories.
• Workshops: Hands-on workshops to practice data analysis techniques and tools.
• Case Studies: Real-world scenarios to analyze and solve data-related problems.
• Guest Speakers: Industry experts to share their insights and experiences.
• Group Projects: Collaborative projects to develop teamwork and communication skills.

Faculty:

• The program is taught by a team of experienced faculty members with expertise in data science, machine learning, and related fields.
• Main Cluster:
With over 25 homogeneous processing nodes and 2TB of RAM.
• Secondary Cluster: Consisting of 4 server machines with high processing and memory capabilities.
• The program provides access to industry-standard tools and technologies, including Apache Hadoop, HDFS, Apache Spark, Hive, Impala, Amazon EC2, Cassandra, HBASE, Mahout, Spark, D3.js, and BigR.
• The program includes exclusive masterclasses delivered by professionals from top companies in Spain, such as Santander and Telefónica.
• Data Engineer: Develop and maintain data pipelines and systems.
• Big Data Analyst: Analyze large datasets to identify trends and patterns.
• Machine Learning Engineer: Develop and deploy machine learning models.
• Business Analyst: Use data to inform business decisions and improve processes.
• IT Manager: Oversee IT operations and data management.
• IT Architect: Design and implement IT systems and infrastructure.

Opportunities:

• The program provides students with the skills and knowledge needed to pursue careers in a wide range of industries, including:
• Finance:
Financial modeling, risk management, fraud detection.
• Healthcare: Patient data analysis, disease prediction, personalized medicine.
• Retail: Customer segmentation, targeted marketing, inventory management.
• Manufacturing: Predictive maintenance, quality control, supply chain optimization.
• Government: Policy analysis, public safety, social services.

Outcomes:

• Graduates of the program are well-prepared to enter the workforce and contribute to the growing field of Big Data.
• The program provides students with the skills and knowledge needed to succeed in a variety of data-related roles.

Other:

• The program has partnerships with prestigious companies, including Cajamar Data Lab, Vicomtech, Telefónica, Luís Simões, and Everis.
• Students can develop their final master's projects in collaboration with these companies, gaining real-world experience and mentorship from industry experts.
• The program emphasizes the importance of practical experience and provides opportunities for students to gain hands-on experience through internships and projects.
• The program is designed to be compatible with professional life, with weekend classes to accommodate working professionals.
• The program is taught in Spanish, but there is an English version available.