Students
مصاريف
CHF 4,000
تاريخ البدء
2026-09-01
وسيلة الدراسة
في الحرم الجامعي
مدة
2 years

لقد شاهدت 5/5 برامج/جامعات. يمكنك مشاهدة حتى 5 برامج/جامعات

أنشئ حساباً مجانياً لفتح المحتوى الكامل!

بالتسجيل، فإنك توافق على بيان الخصوصية و الشروط والأحكام.

حقائق البرنامج
تفاصيل البرنامج
درجة
ماجستير
تخصص رئيسي
الذكاء الاصطناعي | علوم البيانات | هندسة البرمجيات
التخصص
تقنيات المعلومات والاتصالات | الرياضيات والإحصاء
نوع التعليم
في الحرم الجامعي
توقيت
لغة الدورة
إنجليزي
مصاريف
متوسط ​​الرسوم الدراسية الدولية
CHF 4,000
دفعات
تاريخ بدء البرنامجآخر موعد للتسجيل
2026-09-01-
عن البرنامج

نظرة عامة على البرنامج


Master in Data Science

The Master in Data Science (MDS) at the Universitŕ della Svizzera italiana (USI) offers students the opportunity to acquire an in-depth understanding and a solid set of skills in data science, including statistical modelling, machine learning, and high-performance computing. It provides an innovative combination of methodological and applied competencies that prepare students to operate at the forefront of modern data-driven research and industry.


Program Structure

The MDS programme spans four semesters over two years, totalling 120 ECTS of full-time study. The curriculum blends foundational courses in numerical mathematics and computer science with a diverse array of application-focused topics. The programme culminates with a master's thesis in the form of a half-year project worth 30 ECTS, which can be carried out in an industrial or research setting.


Specialised Tracks

Designed to align with students' unique interests and career goals, the programme offers two specialised tracks for a targeted educational experience:


  • High-Performance Computing (HPC): Concentrates on large-scale computing and optimisation techniques, equipping students for careers in sectors that demand high computational capabilities.
  • Statistical modelling and machine learning: Focuses on the analysis and interpretation of data to understand complex systems and relationships, suitable for those interested in studying interconnected phenomena.

Admission Requirements

Applicants must hold a bachelor's degree in the field of Computer Science, Mathematics, Physics, Electrical Engineering, Statistics, or related disciplines granted by an accredited university. Applicants with a bachelor's degree in the social sciences with adequate computational and analytical training will also be considered.


Language Requirements

Admission to English-language Master's programmes at USI requires a good command of English. Applicants whose previous degree was obtained in a language other than English are required to provide an internationally recognized language certificate at the B2+ level as defined by the Common European Framework of Reference for Languages (CEFR), or an equivalent test result.


Tuition Fees

Tuition fees amount to CHF 4,000 per semester. For students whose official residence was in Switzerland or Liechtenstein at the time of the final high school exam, the fees are CHF 2,000.


Career Prospects

Career opportunities for data scientists are continuously expanding and changing. The future is very promising for graduates with in-depth knowledge and understanding of mathematical modelling and computer systems. The advanced technical and problem-solving skills developed by our graduates are highly valued by employers.


Double Degree Options

The Master in Data Science offers students the opportunity to undertake a double degree programme, in partnership with:


  • FAU (Friedrich-Alexander University in Erlangen-Nürnberg)
  • LUT (Lappeenranta-Lahti University of Technology in Finland)
  • UNIBO (Universitŕ di Bologna)

Research Opportunities

The Faculty of Informatics encourages and promotes the talent of its Bachelor and Master students by offering them summer internships in academic research within the programme Undergraduate Research Opportunities Program - UROP. Internships are extracurricular, and access is on a competitive basis. Students work one-on-one with an advisor to develop a deeper understanding of both the concepts taught during the semester and the research topic.


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