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This program provides students with a strong foundation in mathematics, statistics, and data analysis tools. Courses include linear algebra, probability theory, mathematical analysis, numerical methods, machine learning, and data visualization. Practical projects and programming courses prepare students to model, analyze, and interpret data in real-world contexts. The curriculum also includes team projects, a final thesis, and an industrial placement, ensuring strong application of skills.

Applicants must have passed mathematics and English at secondary school. The English exam must be at an advanced level with a minimum score of 60% (or 30% at bilingual level). Additionally, candidates must have a second foreign language and one of the following subjects: physics, chemistry, or informatics.

The program is structured into five modules. The Preparatory Module (27 ECTS) includes core statistical and mathematical courses such as Generalized Linear Models, Statistical Machine Learning, and Time Series Analysis. The Speciality Module (27 ECTS) focuses on actuarial and financial topics like Life and Non-Life Insurance Mathematics, Computational Finance, and Risk Theory. The Elective Module (30 ECTS) offers advanced actuarial, economic, and computer science courses, along with practical training. Students may also take Optional Courses (6 ECTS) and participate in Study Abroad programs (6–15 ECTS). The curriculum concludes with a Master’s Thesis (30 ECTS).

Applicants must hold a bachelor’s degree with at least 24 ECTS in mathematics (including calculus, probability, statistics) and 6 ECTS in computer science. Admission is based on the previous study grade (40%), a motivation letter (30%), and an interview (30%). The motivation letter should explain goals, background, and specific interests in actuarial and financial engineering. The interview assesses field knowledge, motivation, problem-solving, and communication. English proficiency (B2 level) is mandatory, demonstrated by accepted international language tests.

This Master’s program combines data science, strategic decision-making, and digital innovation to prepare students for data-driven roles in business and technology. Core courses include Python programming, machine learning, data visualization, optimization, and digital experimentation. Students also explore topics like algorithmic governance, fintech, entrepreneurship, and sustainability through a wide range of electives. The program requires a total of 90 ECTS, including 33.5 ECTS of mandatory courses, 24.5 ECTS of electives, 2 ECTS of skill modules, and a 30 ECTS work project. An optional extension allows students to reach 120 ECTS for further academic or professional goals.

Applicants to the Master's programs must have completed their studies at an accredited or officially recognized institution in their home country. Since all academic activities are conducted in English, candidates must provide proof of English language proficiency at a B2 level or higher when applying. Portuguese is not required for life on campus. These programs are primarily designed for candidates with up to two years of professional experience, typically aged 23–24, though older applicants who align with the program's profile may also be considered.

This Master's program offers a highly flexible and specialized curriculum through a range of focused Areas of Expertise. Students can tailor their studies toward key fields such as Sustainability, Business and Data Analytics, International Development, Macroeconomics and Financial Markets, Public Policy, and Banking and Financial Regulation. Core training includes advanced micro- and macroeconomics, econometrics, and career development, while electives cover topics like machine learning, climate finance, health economics, blockchain, and political economy.

Applicants to the Master's programs must have completed their studies at an accredited or officially recognized institution in their home country. Since all academic activities are conducted in English, candidates must provide proof of English language proficiency at a B2 level or higher when applying. Portuguese is not required for life on campus. These programs are primarily designed for candidates with up to two years of professional experience, typically aged 23–24, though older applicants who align with the program's profile may also be considered.

This Bachelor's offers a rigorous foundation in programming, statistics, machine learning, and big data technologies. Students begin with courses in linear algebra, programming, data structures, and probability, progressing to advanced topics such as deep learning, text mining, and big data analysis. The program integrates practical skills through a capstone project and includes options in risk management, geospatial analytics, entrepreneurship, and leadership. Ethical and legal dimensions of AI are also addressed. With strong technical depth and business-oriented electives, graduates are prepared for roles across data-driven industries.

To apply for this Bachelor's through the special admission contest for international students, applicants must submit proof of secondary education, residency, and English proficiency. If not already holding a B2-level English certificate (e.g., IELTS ≥ 5.5 or TOEFL iBT ≥ 72), candidates must pass an online English exam. Those who succeed move on to an online Mathematics exam, where a minimum passing grade of 9.5 out of 20 is required. Finally, candidates must score at least 9.5/20 in an interview with the program coordinator. All steps are mandatory and conducted online.

This program provides a deep foundation in data science, combining essential technical and analytical skills with real-world business applications. Core subjects include data mining, machine learning, programming, and statistics, ensuring students can manage, analyze, and derive insights from complex data sets. In the second semester, it emphasizes business applications through courses like Business Intelligence and Business Process Management. Students can personalize their path with electives such as Big Data Analytics, Data Visualization, and Reinforcement Learning. The curriculum is complemented by a strong focus on digital transformation and ethical data use.

This Master’s program is designed for candidates with a background in data science, computer science, statistics, or related fields. It covers topics such as data mining, machine learning, business intelligence, digital transformation, and big data analytics, preparing students to solve real-world problems through data-driven methods. Applicants must hold a completed Bachelor’s or Master’s degree, demonstrate English proficiency, and show strong academic or professional achievements. Courses are delivered in English and emphasize both technical and business applications.