Data Science (DS 19), Master & Promotion

Semester: SS 2019
Study Courses: Master Business Administration, Master Information Systems, Master Business Administration and Law, Master Business Pedagogics
Credit Points: 6 CP
Contact person: Prashant Shivaram Bhat

Description

Companies in research and industry use data to safeguard decisions and to offer data-intensive products and services. The competencies required for these processes are summarized under the term Data Science. The analysis of large amounts of data is composed of scalable data management, parallel algorithms, statistical modelling and a secure handling of the complex interaction of various instruments and platforms and is anchored in various disciplines. On the one hand, this lecture is intended to explain to the participants what is expected of future data scientists and on the other hand to give them the skills they need to fulfil these expectations. The methodical knowledge imparted in the course is not only intended to be a short "how-to", but also to enable the participants to independently decide when and why certain methods are to be used. Since one of the biggest problems in data analysis is often the wrong question, the lecture will also look at the company perspective to solve typical company problems and ask the right questions for a suitable data analysis. The lecture presents concepts and instruments that are needed throughout the entire data science pipeline. In addition to the correct approach, the lecture will discuss the interpretation of the analysis results as well as their visualization and transformation into business models. In accompanying exercises, presented methods and algorithms will be applied in practice, focusing on web programming, statistics and the manipulation of data sets.

Dates and Rooms

Organization

General information about the lecture

  • structure: lecture and tutorial
  • language of the course: english

Recommended previous knowledge

We expect that you have already attended the mathematics lectures of Faculty 1 or 6 and the statistics lectures of Dr. Martin Becker ("Deskriptive Statistik und Wahrscheinlichkeitsrechnung", "Schließende Statistik"). Furthermore, for a successful participation you should have at least a basic knowledge of the programming language Python. If you have completed Programming I and II of Faculty 6, you should be prepared accordingly.

Enrollment formalities

The number of participants is limited to 25. Please register at https://lehre.iss.uni-saarland.de/. All further information and documents can be found there. Please also register for the examination via the examination secretariat VIPA.

Examination formalities

The exam consists of a written test (120 minutes) (60% of the module mark) and the practice mark (40% of the module mark). Doctoral students may receive a certificate of attendance for the lecture if they pass the exercises, i.e. complete this work with a grade of at least 4.0.