System Dynamics and Data Science with Python

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.

Course leader

Dr. Hamid Mostofi is senior researcher since 2017 in faculty 1 of TU Berlin. He worked in automotive industry, urban mobility and smart city research institutes from 2008.

Target group

The Bachelor graduates and Master/PhD students of:

Business and Economics
Industrial, Civil, IT and Computer Science, Mechanical, Electronical Engineering
Urban planning
Transportation Engineering

Course aim

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
-Understanding and developing the skills to apply descriptive techniques and -Statistical inference in the real business cases, social and marketing studies
-Machine Learning (ML) process, supervised vs unsupervised, validation approaches, over/ under fitting
-Introduction to basic Clustering approaches
-Introduction to basic Classification approaches

Credits info

3 EC
ECTS

Fee info

EUR 950: 950 euros for students and 1140 euros for working professionals