Master in Data Analytics for Business

Students will be able to understand the concepts of big data through different tools for storing and processing large amounts of data, such as Apache Spark, and using platforms such as AWS or Microsoft Azure.

This subject will complement the advanced visualization training with business intelligence tools which are widely used in data analytics projects.

In this subject, students will develop the technical knowledge necessary to visualize large amounts of data using not only the most commonly used tools, but also Python. After being introduced to this programming language, you will be taught how to display data using tables, descriptors or univariate or multivariate graphs in different areas of application.

This subject will show a recent model for data storage, distinct from that of relational databases: non-sql databases. The main differences between the two models will be emphasized, so that students can select the best approach according to the use case. It will include practical management exercises for these types of platforms.

This course focuses on one of the most important phases of data analytics: exploratory data analysis. Statistical, visualization and more advanced methods will be shown that will help to understand a data set, hypothesize about it, and clean and prepare the data for experimentation and analysis, especially by artificial intelligence algorithms.

This course will give an overview of how to orchestrate people, processes and technology to turn data into a strategic asset for the company. In particular, specific examples of ensuring the quality and traceability of data processing will be given, using solutions such as the popular Data Build Tool (DBT).

This subject will be offered as an elective with the aim of completing students’ statistical knowledge, which may be useful for data analysis processes, such as basic statistics or hypothesis testing.

Students will learn how to manage and execute a health-oriented data analytics project, including data collection (sick cases vs. control cases), machine learning problems for prediction or classification, patient clustering, and prediction based on time series analysis. This is a topic that brings together knowledge of several compulsory subjects in order to apply it to the field of health, which is increasingly digitized.

This subject will have two basic modules: marketing and human resource management. Students will be able to apply the knowledge of various subjects to: (1) perform customer segmentation and other data analytics techniques for designing or improving marketing campaigns; (2) learn data analytics techniques for people management.

Knowledge of data analytics for decision-making will be given with respect to: (1) forecasting demand in order to be able to adequately plan supply; (2) optimizing the storage process (building an efficient and effective warehouse); and (3) optimizing transport routes.

Students will be given the training and educational material needed to be able to gain Microsoft Power BI Data Analyst Associate certification (free of charge).

This subject presents introductory content on artificial intelligence, its origins, its different branches, its explosion hand in hand with the evolution of computer technology, and its main applications, benefits and risks.

With this subject, students will be able to set out a machine learning problem, develop algorithms that solve it, and evaluate the result. Special emphasis will be placed on the different supervised (regression, classification) or unsupervised (clustering) machine learning algorithms, in order to properly select them in a real project and put them into production. Usual metrics such as accuracy, recall or F1, as well as the detection of biases, will have a bearing on the evaluation of these systems.

This course will give students a very practical insight into artificial intelligence, by solving real-world problems. The challenges to be solved will be proposed by companies and the results presented in an event open to the public.

You will learn in detail how the most commonly used algorithms for building Generative AI systems are implemented today. You will build your own prototypes with large language models.

Students will learn Project Management skills in order to be able to lead projects in a demanding and ever-changing environment. The fundamental principles of this discipline are taught, along with the most common techniques and tools. In addition, we will see how the Agile methodology is integrated into project management. The disciplines, tools and techniques of Project Management will be explained allowing the student to drive projects forward. Students will learn the principles of Agile methodology in order to manage projects in a flexible way.

Data analytics processes, especially if we deal with personal data, must comply with the General Data Protection Regulation. In addition, we must process this data from an ethical point of view. In this subject, students will be made aware of these two dimensions, giving them the tools needed to design and execute projects legally and ethically, and minimizing possible biases.

Learn to lead teams with a high level of involvement and motivation, and able to work in a demanding environment. Gain skills and manage tools to set control objectives, build agile organizations and implement a culture of change that drives the transformation that is needed.

The subject provides students with the necessary skills to understand the process of formulating strategy in the current business environment, as well as the process of launching a start-up. The course includes the methodologies and tools needed for interpreting and formulating business strategy as well as analysis of 21st century business models.

This subject focuses on knowing the main management systems that can be integrated into a company to improve and certify sustainability standards. It provides knowledge of criteria and critical capacity enabling management systems to be identified that best suit the specific needs of a company. In addition, the theoretical and practical knowledge needed to implement management systems that combine process quality, environmental efficiency and occupational safety will be given.

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.