Summer School
© Hitesh Choudhary
Business Data Science with Python
On-Campus:2.1 - 27.1.2023.5 ECTS

max. 24 participants
Registration: closed

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TU Berlin Summer & Winter School
+49 30 4472 0230


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 course covers practical contents of statistics, machine learning, information visualization, and data analysis techniques through python programming language and other tools.

Learning Goals & Syllabus

Learning Goals:

  • 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

  • Review the principles of descriptive techniques
  • Causality vs. Correlations
  • Causal loop diagrams and Confounding effects
  • Linear Regression
  • Statistical inference and their applications in the business context, social science and marketing 


A detailed syllabus with information on the schedule will be made available to registered participants.
You may find the syllabus useful when discussing with your home university whether the ECTS credits attainable for this course are accepted by them.

Please note this is a full-time, intensive course and participants will be expected to attend lectures (18 hours of class per week) and complete independent study Monday through Friday. Additional study may also be required on weekends. The activities of the cultural program are also shown in the syllabus.

Target Audience

The Bachelor graduates and Master/PhD students of:

  • Business and Economics
  • Industrial, IT and Computer Science,
  • All Engineering programs 

Participants of the TU Berlin Summer University must meet the following requirements: (i) B2 level English, or equivalent and (ii) at least one year of university experience.

Fundamentals of mathematics and statistics in Bachelor programs should be known.


Dr. Hamid Mostofi is senior researcher at faculty 1 of TU Berlin. His main research interests and projects are application of data science techniques in the business and social sciences. He has experiences in the field of business analysis for urban infrastructure development and urban mobility as well as renewable energy development.
He is the team member of GECI project at TU Berlin (Green Energy Center) which is defined by the Federal Ministry of the Environment (BMU). He is also the team member of the project eHaul (Electrification of long haul heavy-duty commercial vehicles ) which is funded by the Federal German Ministry of Economic Affairs and Energy.
He has the work experience in the field of teaching and preparing learning materials In the field of MBA courses in German universities such as Sustainable Mobility Management MBA  (TU Berlin) – MBA Renewables (Beuth Hochschule für Technik Berlin) and MBA&E (Hochschule für Technik und Wirtschaft Berlin).

Course fees

Course fees for Business Data Science with Python are as follows:

  • Student: 1950 Euro
  • Working professional/Non-student: 2340 Euro

Please note that students will be required to upload proof of their student status (student card/ enrollment information) during the registration process.

Winter University On-Campus:02 January - 27 January, 20235 ECTS credit points