Summer SchoolBusiness Data Science with Python
|Art||On Campus, Vollzeit|
|Nominale Dauer||4 weeks (5 ECTS)|
|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.
Die Zulassungsunterlagen werden in folgenden Sprachen akzeptiert: Englisch / Deutsch.
Please upload one of the following documents:
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/international/summer-school/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.
Besondere Anforderungen für Nicht-EU Bewerber:
Please upload your health insurance policy in English (all pages).
The course of Business Data Science with Python contains the learning materials, practices and case studies to develop the knowledge and skills of the students in the field of data science and its application in the real business/work world.
The students will learn how to apply analytical techniques and scientific principles to extract valuable information from business data for decision-making, strategic planning.
This skills-based specialization is intended for learners who have a basic programming background and want to apply statistical, machine learning, information visualization, and data analysis techniques through python programming language and other tools.
- Understanding statistical association and the difference between causation and correlation
- Understanding and developing the skills to apply descriptive techniques and Statistical inference in the real business cases, social and marketing studies
- Structural Equation Modeling SEM, Confirmatory Factor analysis CFA , Path analysis
- Time-series Analysis
- Advanced visualization techniques as an initial step to solve data analysis problems, including Geo-based visualization and Network visualization
- Machine Learning (ML) process, supervised vs unsupervised, validation approaches, over/ under fitting
- Introduction to basic Clustering approaches
- Introduction to basic Classification approaches
- Introduction to Social Network concept and its principles and applications.
Main course components:
Principles of Python
- Descriptive techniques
- Statistical inference
- Linear Regression
- Nonlinear Regression
- Structural Equation Modeling SEM
- Time series