Online Master in Data Analytics for Business

Students will understand the concept of big data by exploring different tools for storing and processing large volumes of data—such as Apache Spark—using platforms like AWS or Microsoft Azure.

This course complements advanced visualization training with business intelligence tools widely used in data analytics projects.

Students develop the technical skills needed to visualize large datasets using not only commonly used tools but also Python and its most popular libraries. After an introduction to the language, they learn how to present data through tables, descriptive statistics, and univariate or multivariate charts across different application domains.

This course presents a data storage paradigm different from relational databases: NoSQL databases. It emphasizes the key differences between both paradigms so students can select the best approach for each use case, and includes hands-on exercises with these platforms.

Focused on one of the most important phases of data analytics, this course covers statistical methods, visualization techniques, and more advanced approaches to understand a dataset, pose hypotheses, and clean and prepare data for experimentation and analysis—especially for use by AI algorithms.

An overview of how to orchestrate people, processes, and technology to turn data into a strategic business asset. The course includes concrete examples of data processing to ensure data quality and lineage/traceability, using solutions such as the popular Data Build Tool (dbt).

An introductory overview of AI—its origins, branches, rapid growth alongside advances in computing, and its main applications, benefits, and risks.

Students learn to frame a machine learning problem, develop algorithms to solve it, and evaluate the results. The course focuses on supervised (regression, classification) and unsupervised (clustering) algorithms, selecting the right approach for real projects and deploying models to production. Evaluation covers not only common metrics such as precision, recall, and F1, but also bias detection.

An advanced course that delves into model design and optimization. It takes a highly practical approach while providing the theoretical foundations needed to understand how these algorithms work.

A hands-on course solving real-world problems. Students work in teams on challenges proposed by companies.

Students learn core Project Management knowledge to lead projects in high-change, high-demand environments. The course covers the fundamental principles of the discipline along with the most widely used techniques and tools. It also explores how Agile methodology integrates into project management. Domains, tools, and techniques of Project Management are explained to drive projects forward, and students learn Agile principles to manage flexible projects.

Lead teams with high levels of commitment, motivation, and performance. Acquire skills and use tools to set control objectives, build agile organizations, and implement a culture of change that drives necessary transformations.

Equips students with the skills needed to understand the strategy formulation process in today’s business environment, as well as the process of launching a startup. The course includes the methodologies and tools required to interpret and formulate business strategy and to analyze 21st-century business models.

Provides the skills to apply data analysis tools to financial decision-making. Through a practical approach, the course covers widely used methodologies in financial data analytics—from data collection and cleaning to modeling and interpretation.

This course focuses on the main management systems that can be integrated into a company to improve and certify sustainability standards. It provides criteria and critical judgment to identify the systems that best fit a company’s specific needs. It also offers the theoretical and practical knowledge needed to implement management systems that combine process quality, environmental efficiency, and workplace safety.

Data analytics processes—especially when handling personal data—must comply with the General Data Protection Regulation (GDPR). Beyond legal compliance, data must also be treated ethically. This course raises awareness of both dimensions and equips students with the tools to design and execute projects within legal and ethical boundaries while minimizing potential biases.

You will learn in detail how to implement the algorithms most commonly used today to build generative AI systems. You will also build your own prototypes with large language models.

Students learn how to manage and execute a data analytics project in healthcare, including data collection (case vs. control), machine learning problems for prediction or classification, patient clustering, and time-series–based forecasting. This course integrates knowledge from several core courses and applies it to the increasingly digital health sector.

Two core modules—marketing and people management. Students apply concepts from multiple courses to (1) perform customer segmentation and other data analytics techniques to design or improve marketing campaigns; and (2) learn analytics techniques for HR management.

Tools and methods for decision-making on (1) demand forecasting to plan procurement, (2) warehouse optimization (designing efficient and effective storage), and (3) route optimization for transportation.

Offered as an elective to round out students’ statistical knowledge useful in data analytics processes, including basic statistics and hypothesis testing.

Training and study materials to prepare students for the Microsoft Power BI Data Analyst Associate certification.

You can also undertake curricular internships in companies, which are validated as two elective subjects (6 ECTS credits).

You work on the final master's project (TFM) throughout the entire master's degree. You must demonstrate and put into practice the knowledge you have acquired throughout each of the modules of the master's degree.