1504 Data Science for International Business
Univ.Prof. Thomas Lindner, Ph.D.
Contact details
Weekly hours
Language of instruction
09/15/22 to 09/26/22
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Day Date Time Room
Thursday 10/06/22 02:30 PM - 05:30 PM D1.1.074
Thursday 10/13/22 02:30 PM - 05:30 PM D1.1.074
Thursday 10/20/22 02:30 PM - 05:30 PM D1.1.074
Thursday 11/10/22 02:30 PM - 05:30 PM D1.1.074
Thursday 11/17/22 02:30 PM - 05:30 PM D1.1.074
Thursday 12/01/22 02:30 PM - 07:00 PM D1.1.074
Thursday 12/15/22 02:30 PM - 05:30 PM D1.1.074

The course follows the below structure:

  1. Introduction to Analytics, Course Admin, and Introduction to DataCamp
  2. Basic Mathematics for Analytics (online module "Data Science for Business" due)
  3. Inferential Statistics with R (I) (online module "Introduction to R" due)
  4. Inferential Statistics with R (II) and Application (online module "Generalized Linear Models in R" due)
  5. Guest Lecture on Analytics, Artificial Intelligence, Quantum Computing, and the Blockchain
  6. Analytics in MNCs and Project Coaching (project proposal due)
  7. 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 courses “Managing and Analyzing Data for Business Decisions”, “Machine Learning and the MNC”, 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 can be downloaded for this class here: 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
  • 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.

1 Author: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

An Introduction to Statistical Learning with Applications in R

Publisher: Springer
Year: 2015
Content relevant for class examination: No
Content relevant for diploma examination: No
Recommendation: Reference literature
Type: Book
Recommended previous knowledge and skills

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

Availability of lecturer(s)

Last edited: 2022-11-10