1348 Data Science for International Business
Univ.Prof. Thomas Lindner, Ph.D.
Contact details
Weekly hours
Language of instruction
09/04/23 to 09/22/23
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Day Date Time Room
Monday 11/20/23 01:00 PM - 05:30 PM D2.0.038
Tuesday 11/21/23 01:00 PM - 05:30 PM D5.1.004
Wednesday 11/22/23 01:00 PM - 05:30 PM D5.1.003
Thursday 11/23/23 01:00 PM - 05:30 PM D2.0.038
Friday 11/24/23 01:00 PM - 05:30 PM D1.1.078

The course follows the below structure:

  1. Introduction to Analytics, Course Admin, and Introduction to DataCamp (online module "Data Science for Business" due)
  2. Inferential Statistics with R (online modules "Introduction to R" and "Generalized Linear Models in R" due)
  3. Analytics in MNCs and Project Coaching (project proposal due)
  4. Guest Lecture on Analytics, Artificial Intelligence, and Quantum Computing
  5. Project Presentations
Learning outcomes

In this class, students will learn to understand and apply advanced tools for business analytics. We will develop conceptual and mathematical foundations. Then, we will apply these foundations to analytical questions using R (A language for statistical computing). The course will be accompanied by a learning module in DataCamp, which students can use to acquire basic programming skills, and extend existing knowledge. We will solve simple isolated exercises, as well as more involved issues in business case studies. After completing this course, students will be able to manage and execute data science and analytics tasks, and understand how these tasks contribute to MNC strategy and competitive advantage. Students taking this class are expected to have a basic understanding of statistics, for example evidenced by successful completion of the course “Managing and Analyzing Data for Business Decisions” or similar courses in other study programs. Alternatively, students can familiarize themselves with basic statistical concepts using self-teaching materials provided before the course starts. Although they are helpful, no prior experience with programming languages is required.

Attendance requirements

Attendance at all sessions is required.

Teaching/learning method(s)

The class is a workshop-style course, with many interactive elements. Students are expected to give presentations, provide feedback on each other’s work, and discuss their progress with instructors. The class is also accompanyied by an online teaching module, which forms an inherent part of teaching and student evaluation in this class. This online module is provided by DataCamp, a leading provider of online resources for data science training.

The final case for this class is "Paillasse International SA: Global Market Selection". The case package including this case study will be made available. The key question for the final case is "Where should Paillasse invest?". This question is to be answered using data analysis techniques.

  • 40% final presentation
  • 20% essay on digital technology including discussion of generative AI component
  • 15% homework assignments
  • 10% class participation
  • 10% pre-class test in the first session
  • 5% project proposal

Total scores between 60% and 70% receive a grade 4, scores between 70% and 80% receive a grade 3, scores between 80% and 90% receive a grade 2, and scores above 90% receive a grade 1.


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Recommended previous knowledge and skills

Some experience with statistics and programming languages is useful, but not necessary.

Availability of lecturer(s)


I expect that some of you will use generative AI (ChatGPT and similar tools), potentially also for assignments. There is, per se, nothing bad about that. You will need to learn how to use those tools. We will not focus on how to use generative AI in this class (except for one assignment) but I would like to highlight a couple of key points:

Be aware of the limits of generative AI!

  • Your prompts and their quality will drive the quality of the output. You will need to refine your prompts in order to get good outcomes.
  • Don’t trust anything that the tool is writing. If it gives you some numbers or facts, you should assume that it is wrong unless you can either fact check with other sources, or unless you can be sure that you know that the information is correct. Ultimately, you will be responsible for errors or any other types of limitations that the tool produces.
  • AI is a tool and you need to acknowledge the use of it. Please include a paragraph at the end of any assignment that explains if and how you have used AI and what prompts you have used, as well as what you have used them for. Failure to do so is violates your pledge of academic integrity and can have serious consequences.
  • Note that the use of AI tools can be detected by WU plagiarism software. In case that there is indication for undue AI usage, first, an audit interview with the student will be scheduled. Follow-up consequences will be determined afterwards.
  • In this context, please note also the official WU guidelines on plagiarism:"
Last edited: 2023-06-30