Gain access to one of the most in-demand professions through practical training in big data, artificial intelligence and business intelligence. Train to manage data-driven projects and lead the technological transformation of companies with this program designed for an international audience.
The Master in Data Analytics for Business teaches you everything you need to know to analyse big data, manage projects, create business opportunities and improve digital solutions in companies.
Learn to use the most advanced tools to extract and process big data through predictive analysis and artificial intelligence. Understand each of the phases of data flow: collection, storage, processing, exploratory analysis, intelligent analysis (prediction, classification, clustering etc.) and preparation of reports.
The faculty, big data analytics professionals in leading companies, teaches you to manage, plan and execute projects through practical cases and supports you at all times so that you develop your full potential as a data analyst.
Increasingly, companies are looking for business analytics specialists who respond to new challenges in data analytics, artificial intelligence, big data, IoT and business intelligence. Even if you do not have a purely technical profile, the Master in Data Analytics for Business prepares you for joining one of the most in-demand sectors.
Why choose this program
Gain access to one of the most in-demand professions
The demand for professionals in big data will grow 23% in the next 10 years, according to a report by the US Bureau of Labour Statistics. The figure of the data analyst is already fundamental in companies and salaries are getting higher and higher.
Specialize in big data analysis and management
Extract and analyse big data from different sectors with current market techniques in data analytics. Perform predictive analytics with artificial intelligence and learn key strategies for interpreting data and managing projects.
Get Microsoft certified
You can take an elective course to prepare for the Microsoft Power BI Data Analyst Associate certification, thanks to the UPF-BSM agreement with Microsoft.
Learn about Amazon tools
Enjoy free access to material on data analytics tools from Amazon. You can obtain certifications for these two tools and give more value to your professional profile in data analytics.
Learn from leaders in the sector
Learn from professors specialized in Data Analytics, Machine learning and Business Analytics. Professionals from companies such as Amazon and Microsoft share with you their experience and knowledge in data and project management.
International recognition
Train at the No.1 Business School linked to a public university in Spain. The EQUIS international distinction endorses the quality of the institution.
Who is it for?
The Master in Data Analytics for Business is mainly directed at profiles with training in economics, business administration and management, marketing, mathematics, physics and engineering. It is also aimed at professionals from other fields who want to expand their knowledge to lead the management and interpretation of big data in companies.
Accreditations
The UPF Barcelona School of Management is the business school of Pompeu Fabra University, which ranks as the No.1 Ibero-American university and the 16th university in the world among universities which are less than 50 years old, according to the Times Higher Education ranking.
The EQUIS academic accreditation, the most prestigious international recognition for business schools, places UPF Barcelona School of Management among the elite of schools in this field.
The Master in Data Analytics for Business is an official master's degree and has the academic recognition of the Ministry of Education of the Government of Spain. The Quality Agency of the Catalan University System (AQU) has also institutionally accredited UPF-BSM. This accreditation certifies all the official master's degrees that we teach and recognizes the quality of our educational model in accordance with the criteria of the European Higher Education Area (EHEA).
UPF Barcelona School of Management is an accredited center by Amazon and Microsoft for training in their tools.
Curriculum
The Master in Data Analytics for Business is organized into three large modules focused on data extraction and visualization, artificial intelligence and project management.
You must take all compulsory subjects and choose four optionals subjects* or two optionals and professional internships. The range of courses available is wide and will allow you to adapt your training to your interests.
In addition, with the aim of offering more practical learning, we organize visits to technology companies that base their strategies and decisions on big data. So we get to know how the analysis, visualization and interpretation of data work to achieve an optimal business strategy.
* The details contained in these pages are for informational purposes only and may be subject to changes each academic year. The definitive guide will be available to people enrolled in the virtual space before the start of each subject.
Data analysis (Data Analytics)
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.
Artificial intelligence (AI)
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.
Project Management
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.
Internships
You can also undertake curricular internships in companies, which are validated as two elective subjects (6 ECTS credits).
Master’s Final Project
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.
Complementary activities
The Master in Data Analytics for Business also includes the possibility of participating in practical activities and activities for personal and professional growth such as:
- Training complements: Initial preparation course for participants who have the need to take them depending on their previous training: Introduction to Economics and Business, Tools for Data Analysis (Big Data Analytics) and Introduction to Python Programming.
- Visits to companies with a data-driven approach: During the course, we visit technology companies that base their strategies and business decisions on the extraction, visualization and interpretation of data and the application of artificial intelligence.
- Professional development program: sessions and workshops to improve your professional profile, learn how to address contracting companies and develop skills to grow in the world of work.
- UPF-BSM Inside: is a group of interdisciplinary subjects (applied data, communication, creativity, innovation and project management, sustainability and leadership among others) that, if you take this program, you can access at no additional cost. They are 100% online and you can take them throughout the academic year at your own pace, as they have been designed as self-study subjects.
Qualification obtained
Once the program has been completed, you will be awarded the Màster Universitari en Analítica de Dades per a Empreses/ Master in Data Analytics for Business - Máster Universitario en Analítica de Datos para Empresas/ Master in Data Analytics for Business, issued by Pompeu Fabra University.
Official Masters Diplomas: You must pay the amount stipulated in the DOGC (Official Gazette of the Generalitat de Catalunya) for the rights to issue the diploma. This rate varies annually and the one in force at the time of the application for the title will be applied.
Faculty
The teaching staff of the Master in Data Analytics for Business have experience both in university teaching and in the extraction and analysis of big data, artificial intelligence and project and company management.
In addition, during the course, specialists who hold senior positions in leading technology companies share their professional experience.
Academic directors
Faculty
Collaborating faculty
- Alexandra Albós
Data Scientist at Sanofi pharmaceuticals. Holds a Ph.D. in Biomedical Engineering from the University of Barcelona, and is also a professor in the Data Science Master's and Bachelor's programs at the Universitat Oberta de Catalunya (UOC). She has a Bachelor's and Master's degree in Biomedical Engineering from the University of Barcelona, and a Postgraduate degree in Artificial Intelligence (AI) with deep neural networks (Deep learning) from the Polytechnic University of Catalonia (UPC). She is also an author and/or co-author of multiple scientific articles (Google Scholar). - Ariadna Casasús
Has held various positions in multinational companies in the areas of strategy and marketing. She is currently part of the team at Link2market, a company dedicated to carrying out practical strategic projects to accelerate organizations. She has a degree in International Business and Marketing (ESCI-UPF), in Advertising and Public Relations (UOC), and has studied the Global Executive Master in Digital Business (ISDI) and the Master in Innovation and Digital Transformation (UOC). She is an associate lecturer in the Executive and Master's programs in Digital Marketing at the UOC and a collaborating professor at La Salle-Technova. - Adriana Martins
Specialist in Business Intelligence with a main focus on visualization tools and data processing. She has worked for top-tier companies in multiple sectors (banking, health, retail, public sector, education, etc.). She currently works at Minsait (Indra) in the Business Intelligence department for an IBEX35 bank. She holds a degree in Management Computer Engineering from the UPC, has studied the master's in Big Data Management, Technologies and Analytics (UPC), and the master's in Health Management (UB). - Natàlia Padilla
Postdoctoral researcher at Vall Hebron Institute of Research (VHIR). She also co-founded and manages the Python Barcelona association. She holds a PhD in Bioinformatics from UAB and has a vast experience in clinical bioinformatics and machine learning applied to biomedicine. She has collaborated with several prestigious institutions such as Instituto de Biología Molecular de Barcelona (CSIC) and Children’s Hospital of Philadelphia, USA. She has contributed with more than 20 scientific publications, such as Sheppard et al., Mechanism of KMT5B haploinsufficiency in neurodevelopment in humans and mice; Sci Adv. 2023 10.1126/sciadv.ade1463 and Padilla et al., BRCA1‐ and BRCA2‐specific in silico tools for variant interpretation in the CAGI 5 ENIGMA challenge; Hum Mutat. 2019 10.1002/humu.23802. - David Solans
Associate Researcher at Telefónica Research. PhD in Communication Sciences from Pompeu Fabra University. He has a Master's and Postgraduate degree in Data Science and a Degree in Computer Engineering from the Universitat de Barcelona. He is the author and/or co-author of multiple scientific articles and is the inventor of several patents published by the United States Patent Office. - Luca Telloli
Senior Data Engineering, Adevinta. Graduated in Computer Science from the University of Bologna. He has an MSc in Computer Science and Engineering from UC San Diego. He has worked as a research engineer at institutions such as the Barcelona Supercomputing Center and Yahoo! Research. He has teaching experience at the School of Engineering of the Pompeu Fabra University. - Marc Valdivia
Computer Engineer from the University of Barcelona. He participated in the computer vision research group with a publication in the ECCV. Specializing in the field of artificial intelligence, he currently leads the engineering team at the Spanish startup Piper. Previously, he was a professor in the “Big Data & Artificial Intelligence” master's program at the Barcelona Technology School.
Specialists and professionals:
- Patricia Heredia
CEO MiniVinci & YouTuber at ValPat - Tommaso Meneghini
D+A Global Data Governance @PepsiCo - Belén Arribas
President of IFCLA (International Federation of Computer Associations)
Methodology
The Master in Data Analytics for Business combines different teaching methodologies to offer you a unique and complete learning experience.
The common methodology in all the subjects will be learning by doing, in which we work with practical cases, challenges and simulations.
We also carry out visits to companies and have master classes given by senior managers, as well as challenges launched by leading companies from different fields that allow us to discover the reality of data analytics and big data.
Combining theory and practice
The subjects combine the theoretical bases with a practical approach. This methodology allows you to consolidate the key concepts in the extraction, visualization and management of databases for directing projects in companies.
Learn through hands-on simulations
Boost your learning through practical simulations, group dynamics, presentations, discussions and interactive activities.
Participate in challenges from real companies
Professionals from data-driven companies propose challenges based on real cases so that you can immerse yourself in the current work environment.
Workshops with professionals from the sector
On each course, we invite professionals from large companies to share their experience and knowledge of advanced big data analytics. Overcome real challenges related to business intelligence and business analytics and open the doors to your professional future.
Tutorials and monitoring
You have the monitoring of the academic management team, which offers you support whenever you need it and ensures your progress.
Evaluation
You must pass all the subjects, the evaluation of which depends on the corresponding teacher, in order to obtain the qualification. This may consist of continuous evaluation, the carrying out of a project, exercises, overcoming a challenge, analysing data, final exam, etc. You must also pass the final master's project (TFM), which you have to present and defend in front of a panel.
Regular class attendance and passing the practical exercises and compulsory assignments are part of the evaluation system. The teachers who commission them mark their conditions of delivery and preparation.
All evaluation activities are related to each other so that they follow a logical scheme.
Tools
Project-oriented learning and the combination of lectures and active methodologies such as case studies, flipped learning, solving real problems and professional simulations allow the student to connect theory and practice, acquire advanced skills and achieve learning which is transferable to work.
You will have:
- Master's Final Project (TFM) or Postgrad Final Project (TFP)
- A personal mentor to monitor your final project (TFM or TFP)
- Digital resources to achieve transversal skills
- Interdisciplinary activities and workshops
Professional Future
The Master in Data Analytics for Business trains you in the fundamental aspects of data analytics so that you can develop and promote data-driven projects in companies of any sector. Extract, process and interpret large databases and take advantage of their potential in order to optimize business strategies.
Student profile
In the master's degree, you share a class with profiles that come from business management and management as well as from more technical fields. Thanks to the diversity and participation of the group, the subjects and the class dynamics are a real source of learning. The value of this master's degree is not only in the teaching team, but also in the exchange of experiences and knowledge between students, which is enhanced throughout the course.
Career opportunities
The Master in Data Analytics for Business trains you for data-driven positions, focused on data analytics, visualization and big data management. These may be positions in technology companies or start-ups, but also in companies from various sectors that increasingly incorporate profiles into their teams that know how to manage, visualize and analyse large amounts of data.
In addition, the master's degree gives you the option of undertaking curricular internships in companies that promote your professional future.
Once the master's degree is finished, you can access a wide variety of positions such as:
- Data Analyst
- Advanced Analytics Consultant
- Business Analyst
- Head of Business Development
- Data Scientist
- Data Manager
- Business Intelligence Engineer
- Data Engineer