IS 703: Seminar Applied Data Science

Contents
Welcome to the Masters Seminar in Applied Data Science, a unique, project-based course designed for Masters students who are looking forward to immersing themselves in the world of real-world data analysis and solution development. This seminar is characterized by our exciting collaboration with Coface, a leading global credit insurance group (www.coface.com). In addition to the credit insurance business, Coface operates factoring with Coface Finanz GmbH and is also a leader in this field in Germany. Factoring is a financial transaction in which a company sells its trade receivables (i.e. invoices) to a third party, the so-called factor. This process provides the company with immediate cash flow, which is crucial for maintaining liquidity and financing day-to-day operations. Coface will provide students with access to current, industry-relevant factoring datasets and offer an unprecedented opportunity to apply data science techniques in a practical context.
The seminar is structured to not only introduce students to the challenges of the factoring industry but also to equip them with the skills to develop applicable data science solutions. One of the key learning goals is to enable students to effectively solve real-world problems using advanced data science methods. This involves analyzing and interpreting complex data sets provided by Coface and applying a range of sophisticated algorithms and techniques to address the practical challenges in the factoring industry.
The seminar is an excellent opportunity for students with a strong interest in data science to develop and apply their knowledge in a real-world setting, bridging the gap between academic learning and practical application. It promises to be an enriching and challenging experience, offering a glimpse into the dynamic intersection of data science and the factoring industry.

Learning outcomes
After completing this course, students will

• Have developed the skills to analyze and interpret real-world data sets.
• Be able to apply advanced data science techniques and algorithms to solve practical problems.
• Have enhanced collaboration skills by working in teams and improve communication skills through presentations.

Necessary prerequisites
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Recommended prerequisites
Basic data science knowledge and rudimentary experience with data analysis software such as Python or R.

Examiner Performing lecturer

Prof. Dr. Kevin Bauer Prof. Dr. Kevin Bauer