修士 in 数学・データサイエンス in Europe

This master’s program combines courses in data analysis, informatics, statistics, and social science to prepare students for analyzing complex societal phenomena. Core subjects include machine learning, data ethics, critical inquiry, and computational statistics. Students also explore topics like economic complexity, applied network science, and causal inference. The curriculum includes elective modules such as survey analytics, geography in data science, and big data applications. A two-part thesis consultation supports students in preparing their final research projects. This program is ideal for students seeking to bridge data science with the social scienc

Applicants must complete a two-part admission process: a written exam (Type A) worth 60 points, assessing language proficiency, quantitative analytical skills, and logical reasoning; and an oral interview worth 30 points, focusing on the applicant’s motivation and aptitude for the program.

This program offers three specializations: Data Science, Official Statistics, and Health and Population Analytics. All tracks provide rigorous training in statistical modeling, computational tools, and data analysis techniques. Students explore fields such as supervised learning, Bayesian inference, big data analytics, socio-demographic statistics, public health, and statistical software. Each path includes mandatory coursework, a range of elective modules, internships or thesis options, and training in statistical programming languages. The curriculum integrates applied statistics with real-world data science and research applications across social and health domains.

To access this program, candidates must hold a degree in statistics or a related field, or demonstrate sufficient prior coursework in probability, statistics, and mathematics. The curriculum focuses on quantitative methods for data analysis and interpretation, blending mathematical and statistical theory with applications in economics, actuarial science, and data-driven decision-making. Core topics include probability, econometrics, financial and actuarial mathematics, and risk modeling. Students may also pursue electives in areas like programming and data science, and complete a final research project or internship. EU students typically pay around €1,200/year based on family income. Non-EU students usually pay a fixed fee of about €1,400/year. Check with the university for your exact fee.

This master's program provides a solid foundation in biostatistics, combining medical sciences, statistical theory, and applied data analysis. The first semester focuses on essential medical and mathematical subjects, including anatomy, biochemistry, probability, and statistical programming. It also introduces students to clinical trials and socio-medical quantitative methods. In the second semester, the curriculum expands to advanced data analysis techniques such as linear models, nonparametric methods, categorical data analysis, and causal inference, alongside continued training in clinical trials. Supplementary courses cover digital health, research skills, and emergency response preparedness.

To apply for this master's program, candidates must hold a bachelor's degree or a second-level professional higher education in areas such as economics, psychology, sociology, cultural studies, biology, physics, mathematics, statistics, programming, medicine, nursing, dentistry, pharmacy, or public health. Applicants are also required to submit a certificate proving English language proficiency and must pass an entrance examination in the form of a test.

This two-year master’s program blends economics, data science, and public policy, offering a rigorous foundation in micro- and macroeconomics, quantitative analysis, and policy evaluation. Students complete core courses and choose electives in areas like machine learning, big data, sustainability, and international finance. Specialized tracks support different goals, including data analytics, policy practice, or doctoral research preparation. Coursework includes hands-on training in coding (Python, R), causal inference, and econometrics. Language electives and professional development are also available. The flexible structure allows students to tailor their learning to academic or career ambitions.

Applicants for this master’s program must hold a bachelor’s degree (minimum 180 ECTS) and submit a full application including academic records, a CV, two letters of recommendation, proof of English proficiency, and a 500-word motivational essay explaining their academic background and career goals. English proficiency can be demonstrated via TOEFL (88+), IELTS (6.5+), Duolingo (120+), or equivalent. Previous studies in Economics are not mandatory. Those pursuing the research-oriented track are encouraged to take a math test (calculus, probability, linear algebra) or submit a GRE score to strengthen their application. CEU offers its own online math test as an alternative to GRE, and the best of the two scores will be considered for scholarship decisions.

This master's program provides in-depth training in the mathematical principles behind data science algorithms. It includes advanced topics such as non-convex optimization, stochastic processes, numerical simulation, graph theory, high-dimensional statistics, and numerical linear algebra, complemented by essential computer science courses in neural networks, distributed computing, cryptography, and information retrieval. Students also apply their knowledge in various fields such as artificial intelligence, computational neuroscience, control engineering, cybersecurity, and bioinformatics. The program is tailored for students who want to deeply understand how machine learning models work, how to analyze data mining techniques critically, and how to manage complex systems like power grids and traffic networks under uncertainty.

The study environment encourages interdisciplinary collaboration, as students often engage with peers from engineering and economics backgrounds, gaining exposure to real-world applications in energy systems, neuroscience, and sustainable technologies. International students can take German language courses to enhance their integration and future employability. Graduates are well-prepared for careers in academia, industry, or the public sector in fields like healthcare, finance, consulting, technology, and resource management.

To apply for this master's program, applicants must hold a relevant academic degree equivalent to a bachelor's in mathematics or business mathematics, with a similar level of mathematical coursework. Proficiency in English is required, proven through accepted certificates such as a TOEFL iBT score of at least 79, an IELTS Academic score of 6.0, a Cambridge Certificate level B, or UniCert Level II. Certificates stating that English was the medium of instruction are not accepted. However, for applicants with a German university entrance qualification, the Abitur certificate may serve as sufficient proof of English proficiency.

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.

This program in astrophysics and cosmology offers advanced training in topics such as galaxy formation, cosmology, stellar evolution, and computational astrophysics. The first year covers core courses in astrophysical processes, black holes, and statistics, while the second year emphasizes laboratory work, professional skills, and a final research project. Students can choose from a wide range of electives, including gravitational lensing, exoplanets, radio astronomy, and high-energy astrophysics. The program is designed to prepare students for careers in research or industry, with opportunities for internships and international mobility.

Admission to this program requires a bachelor's degree in physics, astronomy, or a related field, or an equivalent international qualification with strong coursework in physics, mathematics, and astronomy. Students who have not yet completed their degree may apply if they graduate by the end of the calendar year. Proof of English language proficiency at B2 level is mandatory and can be demonstrated through an accepted certificate, a degree taught entirely in English, evidence of passing a clearly labeled B2-level English exam, or native speaker status. Documentation must meet specific format and content requirements. EU students typically pay around €1,200/year based on family income. Non-EU students usually pay a fixed fee of about €1,400/year. Check with the university for your exact fee.

This program offers two specializations: Data Analytics for Economics and Business Analytics. Both provide strong foundations in programming, econometrics, statistics, and machine learning. The Data Analytics track emphasizes economic policy, sustainability, and quantitative investment, while Business Analytics focuses on marketing, network modeling, and digital transformation. Students deepen skills in big data, optimization, and cybersecurity. The program includes internships, a final thesis, and offers international exchange opportunities.

Applicants must demonstrate English proficiency at B2 level (CEFR). A recognized three-year university degree is required, with at least 12 ECTS in Economics, 6 ECTS in Business Administration, and 12 ECTS in Statistics, Mathematics, or Informatics. Degrees earned abroad must be recognized as equivalent. Conditional admission is possible if 140 ECTS have been completed at the time of application, but the degree must be finalized by the enrolment deadline.

This master's program focuses on scientific computing and consists of 120 ECTS credits, structured into mandatory modules, electives in both mathematics and an applied discipline, free-choice courses, and a master's thesis. Core subjects include Scientific Computing, Numerical Linear Algebra, and Numerical Mathematics II for Engineers. Students must select 26 ECTS in applied mathematics and 18 ECTS in a chosen applied discipline, which can also include modules from other Berlin universities. An additional 16 ECTS can be freely selected from any course offered at TU Berlin or its partner institutions. The 30-ECTS master's thesis can be written in either applied mathematics or the applied discipline and must reflect current research standards.

This master's program in Scientific Computing requires a bachelor's degree in mathematics or a related field with a strong mathematical foundation. To qualify, applicants must demonstrate coursework equivalent to at least 18 ECTS in Analysis/Linear Algebra, 6 ECTS in Differential Equations, and 9 ECTS in Numerical Analysis. These areas must cover key topics such as vector spaces, partial differential equations, and numerical methods for solving equations. English proficiency at CEFR level B2 is also mandatory. German is not required. There is no selection process beyond verifying that all admission requirements are met at the time of application.

This Master’s program focuses on business intelligence, data mining, data governance, and digital transformation, providing students with the tools to manage and analyze complex data for strategic decision-making. Core courses include Business Intelligence I & II, Data Mining I & II, and Data-Driven Decision Making, while electives offer a broad range of options from cybersecurity and CRM systems to innovation management and generative AI. Students must complete a dissertation or work project alongside research methods training. The program is designed for those interested in combining data science with business strategy and technology management.

To apply for the Master’s in Information Management, candidates must hold a Bachelor’s or Master’s degree in a compatible field such as Information Systems, Computer Science, Data Science, Engineering, Mathematics, Statistics, Business Analytics, or other quantitative or technology-oriented areas. Applicants from fields like Economics or Management may also be considered if they demonstrate a strong analytical or technical background. In addition, candidates must show strong proficiency in English (spoken and written), a high level of motivation, and a record of academic or professional excellence.

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 program in statistics and economics offers three specialization tracks: Marketing and Market Research, Quantitative Methods for Economic Decisions, and Business Analytics. The Marketing track emphasizes consumer behavior, market analysis, and data-driven marketing strategies. The Quantitative Methods track focuses on econometrics, public policy evaluation, and socio-economic statistics. The Business Analytics track combines statistical computing, data management, and predictive modeling for business decisions. All tracks include internship or research options, language courses, and final examination preparation modules.

Applicants must hold a degree in Statistics or equivalent. Those with different qualifications may still be eligible if they have earned at least 16 credits in relevant areas, including probability, statistics, and mathematics—of which at least 8 must be in statistics and 6 in economics or business management subjects. Candidates with a foreign degree will be assessed for equivalence or relevant academic background. English language proficiency at B2 level is mandatory only for the Business Analytics track. Knowledge of Italian is also required and can be demonstrated via a language test or an Italian-language diploma. Final-year students may apply but must graduate by the end of the year. EU students typically pay around €1,200/year based on family income. Non-EU students usually pay a fixed fee of about €1,400/year. Check with the university for your exact fee.