Summer SchoolSystem Dynamics and Data Science with Python- fully booked, waitinglist only
| Studienort | Deutschland, Berlin |
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| Art | On Campus, Vollzeit |
| Nominale Dauer | 2 Wochen |
| Studiensprache | Englisch |
| Auszeichnungen | Summer School |
| Akkreditierung | 3 ECTS |
| Studiengebühren | 1.050 € 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: €990 This course/tuition fee covers the course, course materials and a cultural program. |
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| 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. |
| Einstiegsqualifikation | 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:
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| Sprachanforderungen | Englisch 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. CEFR: B2 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. |
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| Sonstige Voraussetzungen | Fundamentals of mathematics and statistics in Bachelor programs Besondere Anforderungen für Nicht-EU Bewerber: Please upload your insurance waiver in English (all pages). |
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| Weitere Informationen |
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Übersicht
This course covers the theory, tools, and techniques associated with systems thinking approach which allows students to understand the relationship and connections between components of a system, instead of looking at the individual components one by one. Moreover, the course 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 program helps students to develop understanding and proficiency in system dynamics simulation to evaluate the future of one business in the real world by system thinking approach to consider the linear and nonlinear impacts between different components of one business.
Learning Goals
- Systems Thinking and Business Dynamics
- Learn the relevance of taking a wider system perspective in examining challenges and understand why decisions and responses change naturally over time
- Learn to examine the possible impacts of policy changes and technological innovations on business environment
Tools for System Dynamics Modeling
- Develop skills in the use of simple mapping and spreadsheets to elicit mental models of system structures, and be able to anticipate from their structures, the dynamic behavior of simple closed‐loop systems
- Understanding statistical association and the difference between causation and correlation
Machine Learning (ML) process, supervised vs unsupervised, validation approaches, over/ under fitting
- Introduction to basic Clustering approaches
- Introduction to basic Classification approaches