|Art||On Campus, Vollzeit|
|Nominale Dauer||4 weeks|
|Studiengebühren||1.890 € pro Programm
The program price consists of the course/tuition fee (student or working professional, see details below) plus the registration fee (€60).
Student course/tuition fee: €1890
This course/tuition fee covers the course, course materials and a cultural program.
|Anmeldegebühr||60 € einmalig
The registration fee is in addition to the course/tuition fee and covers the processing of your application. It is payable upon registration. Please note that the registration fee is non-refundable.
At least one year of university experience or equivalent work experience
Die Zulassungsunterlagen werden in folgenden Sprachen akzeptiert: Englisch / Deutsch.
Please upload one of the following documents:
Upload copies in a word or pdf format
All applicants are required to upload a document or certificate to demonstrate their proficiency in English language. If you are a non-native English speaker, you must prove you have a score equivalent to the level B2 or above in the European system (the Common European Framework of Reference for Languages, or CEFR), or provide evidence that you’ve undertaken an equivalent degree/studies in English.
More details: www.tu-berlin.de/menue/summer_university/requirements/
If you are a native English speaker, please select this during registration. You will then be exempt from having to upload proof of English level.
Basic programming knowledge is also required for this course. Students should be able to write and run small programs in the language of their choice. Students should also have basic knowledge in linear algebra and statistics/probability theory and know what loops, conditionals, methods/functions, libraries, vectors, matrices, gradient and probability distributions are.
Besondere Anforderungen für Nicht-EU Bewerber:
Please upload your insurance waiver in English (all pages).
Data Science with Python course introduce learners to data science through the
python programming language.
This skills-based specialization is intended for learners who have a basic programming
background and want to apply statistical, machine learning, information visualization,
data analysis techniques through popular python toolkits such as numpy, pandas, matplotlib,
- Efficient and robust scientific computation and plotting.
- Random variables, distributions and sampling.
- Supervised and unsupervised regression methods.
- One- and multi-class classification algorithms.
- Model selection via cross validation and objective function minimization.