Master’s in Business Analytics

In partnership with
The Program

Master’s in Business Analytics at Nova SBE

The world is increasingly dependent on algorithms and data to inform and support decision-making. Organizations, private and public, for-profit and not-for-profit, are keen to introduce data-driven systems and methods to support management functions but lack the knowledge and the people to integrate the new technologies and adjust their systems and culture to leverage them.

These organizations need translators – people who understand organizations and the managerial problems they face, how to use technology and leverage data to solve them, and how to set up internal and external systems that enable the required ecosystem.

The program curriculum is innovative and corresponds to the needs reported by all types of organizations, at a national or international level, and involves a specialized and research-active teaching staff, most of them belonging to the school’s Research Unit, which was ranked Excellent in Research by Portugal’s Science and Technology Foundation.



The Master’s in Business Analytics aims at the development of solid technical, organizational, leadership, critical thinking and communication competencies for future managers and leaders of organizations (public or private, for-profit or not-for-profit) in a context increasingly dependent on algorithms and hybrid (human-machine) systems for decision making. It is therefore focused on the education of translators, i.e., people who understand organizations and the managerial problems they face and know how to use technology and leverage data to solve them.


Who is it for?

With a collaboration with the Department of Computer Science of NOVA School of Science and Technology, the program brings together the technical and personal competencies of management with technology. 

This multidisciplinarity is key to attract applicant profiles with a first cycle educational background in Sciences, Technology, Engineering or Mathematics, but also Economics, Finance or Management. 

Applications Fall Intake 2022/23

Applications for the Fall intake 2022/2023 are open until 23 May 2022.

Are you ready for this unique learning experience?

For more information, please contact us by emailing or calling +351 213 801 699.

Project-Based Learning (PBL) is a unique learning experience. It allows students to work on a real-world data challenge from the very beginning of the program, supported by a team of professional mentors, where they can apply their knowledge and tools as they learn them in class.

Students are expected to learn and grow with their partner organizations, bridging the gap between the business challenge and the data science solution. This graduate-level training is designed for students who aim to build and deploy tools for data curation, and develop their technical skills.

Students will be part of an interdisciplinary team which will be assigned a single data science project, that will last 18 months (3 semesters). After this, students will be invited to present their main findings and methodology used in their Master’s thesis.

  • Selection Criteria

STEP 1 – Grade on the Technical Quiz: A Quiz, during T1, to evaluate students’ skills in statistics and programming; 

STEP 2 – Grade “Project Scoping” Course (Elective): Intermediate/ Final grade on the Project Scoping course. Given that this course is not mandatory, if you want to apply for Project-Based Learning you must select this course;

STEP 3 – Soft Skills & Interview (if needed)

  • Advantages For Students
  1. Get 18 months of work experience with a data-driven organization;

  2. Improve your skills with a real-world data challenge;

  3. Learn with your peers and Data Science Professionals;

  4. Understand how to use data to solve world pressing challenges;

  5. Improve your chances to get hired and to connect with business professionals.

  • How does it work

-Each project will be developed by a team of 3-5 Master students from diverse backgrounds, for 18 months;
-Each team will be supported by a Data Science Mentor, a senior professional with a strong technical background that will uphold the team’s major decisions;
-Each team will also be supported by a Project Manager/Translator, a senior professional with a strong experience managing technical projects. The Project Manager will contribute to the team’s understanding of the partner’s needs, facilitate interactions between all the parties and make sure the project is performed according to the timeline;
-Data Science Projects will be developed together with a Project Partner (Company/NGO/Government Agency), that will share a data challenge to be scoped and developed by the team.

  • Examples of Potencial Data Challenges

-Identifying children at risk of not being vaccinated to improve vaccination rates (Health);
-Defining a data-driven approach to sustainable tourism management (Tourism);
-Predicting long-term unemployment to support people reenter the workforce (Employment);
-Predicting credit default risk in the banking sector (Banking);
-Reducing corruption in public procurement processes (Government Transparency);
-Improving incident response to optimize traffic safety (Transports).

* Available Data Challenges for 2021/ 2022 will be shared with Students at the beginning of the program.

Week 1 

  • Discovery Week

1st Semester (30 ECTS)

  • Mandatory Courses
  • Electives
  • Professional Development Modules
  • Study Trips
  • Career Development Program - Mastering Your Career
  • Mentoring Program
  • Regular Seminars, Networking Events, Business Forum, Conferences

2nd Semester (30 ECTS)

  • Mandatory Courses
  • Electives
  • Professional Development Modules
  • Study Trips
  • Regular Seminars, Networking Events, Business Forum, Conferences

3rd Semester (30 ECTS)

  • Work Project
  • Regular Seminars, Networking Events, Business Forum, Conferences


In addition to the regular program, international programs such as the CEMS MIM or Exchange may require an additional semester.

* Please note that for the academic year of 2021/2022 this information may be subject to changes.

  • Advanced Data Analysis
  • Digital Markets
  • Data Curation
  • Modelling Business Decisions
  • Data Visualization
  • Data Ecosystems and Governance in Organizations
  • Machine Learning

* Please note that for the academic year of 2021/2022 this information may be subject to changes.

  • Advanced Programming for Data
  • Big Data Analysis
  • Digital Marketing
  • Digital Strategy and Transformation
  • E-commerce
  • Empirical Methods for Finance
  • Financial Econometrics
  • Fintech Ventures
  • Innovation Management
  • Marketing Analytics
  • Modeling Business Decisions
  • Open Innovation
  • Project Scoping (mandatory to apply for Project-Based Learning)
  • Product Design and Development
  • Technology Strategy
  • WEB and Cloud Computing

* Please note that for the academic year of 2021/2022 this information may be subject to changes.

  • Quantitative skills

Learning to analyze data.

  • Ethics

Thinking about ethical challenges.

  • Corporate links/internationalization

Building knowledge about business environment and international careers.

  • Communication and creativity

Thinking outside the box and improving communications skills.


* Please note that for the academic year of 2021/2022 this information may be subject to changes.

What’s at the finish line?

The Work Project

As part of the degree requirement, students must carry out a work project (thesis) in their final semester. There are three formats available:

Field Lab
Students focus on a specific theme or topic of more applied nature to propose solutions to solve an existing local real-world problem in a specific setting (group setting).

Directed Research
Students showcase their knowledge and expertise by investigating a research problem of scientific nature in a relevant area of interest.

Directed Research Internship
Students carry out a problem-based issue to help address an identifiable problem in a company setting relevant to the course of study.

ECTS this, ECTS that

How does the grading system work?

Grading is based upon the European Credit Transfer System (ECTS), a grading scale developed to better understand and compare grades given, according to different national systems. 

We did the math for you and the awarding of ECTS grades breaks down as follows:

33.5 ECTS
in Mandatory Courses including the Mastering Your Career Activities
(Bridging courses not included)

24.5 ECTS
in Elective Courses (minimum)

in Professional Development Modules

in the Work Project

90 ECTS Total do graduate

You need a minimum of 90 ECTS to complete your Master’s, but if you need 120 ECTS to proceed with your studies, you can extend your Master’s by one semester with courses (28 ECTS) and modules (2 ECTS).