Project Course: Applied Data Science

Summer Semester 2017

Goal and Content

The project course Applied Data Science is a new type of course that combines academic research and teaching with practical knowledge transfer. The contrasting feature of the course is to make students acquainted with the major challenging problems from theory to practical implementation along the vertical production line of knowledge. This includes the development of econometric models and their empirical implementation including the development of a user friendly software as end product. Students will experience through the vertical approach that mathematical modelling and knowledge transfer is not a one-way road: The quality of models improves from the feedback of practical experience, while successful applications require state-of-the-art models. By the nature of this vertical approach students will acquire competences at the intersection between Econometrics, Empirical Economics and Computer Science.

The course is project based, i.e. in the center of the course is a precisely defined issue, on which different workgroups work at different stages at the vertical knowledge transfer process. Interaction between workgroups and the instructors as well the opportunity to discuss the project development with practitioners will replace the conventional frontal classroom teaching.

In the center of this semester’s project course is the development of empirical portfolio models and the implementation in terms of a user friendly software package including a documentation.

The project course is designed for third year bachelor students in economics and mathematical finance who have taken Statistics I and II and Econometrics I. Students of the master program Social- and Economic Data Analysis (SEDA) may take the course as a data project course.

Programming

The software will be based on R. Programming experience is desirable, but not required. Students will be given an introduction to R, R Shiny¸ Eikon (Datastream replacement) and Wiki Media.

Grading

Grading will be based on the quality of the subproject (60%), presentations (30%) and classroom participation (10%).

Work Groups

Participants are requested to enroll for one of the work groups as soon as possible and no later than May 1st, 2017. Example for possible work groups are:

1. Portfolio Estimation

2. Forecasting

3. Evaluation and Graphical Analysis

4. Development of the Front End

Organizational Issues

• For more information contact: Winfried Pohlmeier, F319, Tel. 2660,

Winfried.Pohlmeier@uni-konstanz.de

• For enrollment contact: Verena Kretz, F329, verena.kretz@uni-konstanz.de, Tel. 2443

• There will be a first brief information session on Monday, Feb. 20th, in F208 at 13:30.

• ECTS: 6 credits