Introduction into Data Science (IntroDS17), Master & Promotion

Semester: SS2017
Study Courses: Master Business Administration, Master Information Systems, Master Business Administration and Law, Master Business Pedagogics
Credit Points: 6 CP
Contact person: Iaroslav Shcherbatyi


Organizations in industry and research use data for decision support and to build data-intensive products and services. The skill set required by organizations to support these processes has been covered by the term Data Science. Ranging from scalable data management, parallel algorithms, statistical modeling, and the handling of a complex ecosystem of tools and platforms to solve business problems, skills required for analytics of massive data touch a variety of disciplines. The course attempts to articulate the expected output of future Data Scientists and tries to equip participants with the ability to meet these expectations by giving them methodical how-to knowledge combined with answers to why- and when-questions. As one of the pitfalls of data analysis is attempting to solve the wrong problem, the lecture will also focus on the business side to illuminating how to solve business problems and how to address the correct data questions. Lectures of the course cover concepts and tools participants will need throughout the entire Data Science pipeline, starting with asking the right kind of questions for making inferences to communicating and visualizing results as well as transforming them into business models. In accompanying exercises introduced methods and algorithms will be applied practically, involving web programming, statistics, and the ability to manipulate data sets with code. In a final project work participants will apply all the skills learned by building up a data-intensive product or service for solving a concrete business problem with real-world data.

Dates and Rooms


General information

  • Composition of lecture: lectures and exercises
  • Teaching language: English

Recommended Background

We expect you to have taken the Math courses in department 1 or 6 as well as the Statistics courses held by Dr. Martin Becker (descriptive and inferential statistics). Furthermore, you should hold basic programming skills (Python, Java, C++, C# or similar). In case you have passed Programming I & II at department 6, you should be prepared accordingly.


The number of participants is restricted to 25. Please, enrol yourself via All information and documents regarding the course will be published there. For the exam, please register via VIPA.


The course covers a module test consisting of an exam (120 minutes) (60% of module grade) as well as the grade of the exercise (40% of module grade). PhD students can get a seminar certificate for participating in the lecture if they pass the exercises, i.e. their exercise was graded with 4.0 or better.