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Ludwig Maximilians University Munich

Munich

Data Science

Subjects: Data Science;Science;Technology

M.Sc. | Master of Science

⏳ Duration

4 Semesters

🔠 Teaching Language

English

💶 Application Fee non-EU

No information

💶 Cost per Semester ?

85.0 EUR

💶 Tuition Fees per Semester

No Tuition Fee

⏱️ Application Deadline Summer

No information

⏱️ Application Deadline Winter

No information

Course description

Data Science is the art of extracting knowledge and insights from data. It demands skills in both statistical analysis and computer-based data processing. The elite Data Science program at LMU Munich is a collaborative effort between the Department of Statistics and the Institute for Informatics. This program is part of the Elite Network of Bavaria and enjoys its support. The curriculum is structured into modules, covering statistical and computational methods essential for handling large and complex data sets. Students learn how to collect, manage, and analyze these data sets to extract valuable information. The program also includes courses on data security, data confidentiality, and data ethics, ensuring a well-rounded education. In practical modules, students engage with real-world problems, working alongside industrial partners. The program also features summer schools and focused tutorials, providing additional learning opportunities. This comprehensive approach equips students with the skills needed to excel in the field of data science.

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Program Information

Study Location	Munich
Start Semester	Winter
Study Form	Full-time
Study Type	Postgraduate
Teaching Language	English
Dual	No
Remote	No
Application Fee non-EU	No information
Study Length	4 Semesters
Tuition Fees per Semester	No Tuition Fee
Cost per Semester	85.0 EUR

Step 2 - Check Application Requirements

⏱️ Application Start Summer

No information

⏱️ Application Deadline Summer

No information

⏱️ Application Start Winter

No information

⏱️ Application Deadline Winter

No information

Application Requirements

Language Requirements

CEFR	No information
IELTS	No information
Cambridge (CAE)	No information
TOEFL IBT	No information

Proficiency in English: at least B2 CEFR (or equivalent); or English university entrance qualification; or first degree in English.

Qualification Requirements

Bachelor of Science (or equivalent) in Statistics or Informatics or related disciplines (at least 180 ECTS or equivalent). Excellent knowledge in Informatics and Statistics. Applicants need to provide evidence of knowledge in the following fields: Statistical Science and Data-Based Modelling: This includes, in particular, statistics and topics such as data mining, probability theory, and machine learning (at least 30 ECTS or equivalent). (Average Grade 2) Computer Science and Computational Methods: This includes, in particular, data structures and algorithms, database systems, programming principles and practice, software engineering (at least 30 ECTS or equivalent). (Average Grade 3) Overall Average Grade must be better than 1.5. The overall average grade is composed of (1) Average Grade 1: the average grade from the best performance (equivalent to 150 ECTS), (2) Average Grade 2, and (3) Average Grade 3. → Overall average grade = (Av1+Av2+Av3)/3 Proficiency in English: at least B2 CEFR (or equivalent); or English university entrance qualification; or first degree in English. You need to fulfill all the requirements if you want to apply.

Document Requirements

The following documents must be submitted by all applicants via the portal: Transcript of records Bachelor certificate (if not yet available, it needs to be provided no later than 30 September 2024) Essay "Data Science" (in which you look at the developments and perspectives of Data Science as well as your planned area of specialisation, and your previous experience; max. 1,000 words) Proof of proficiency in English (see requirements) Applicants who do not hold German citizenship and have obtained their certificates and degrees from non-German institutions have to apply simultaneously to the LMU International Office.