Applied Data Science

Data science comprises a significant variety of methods and technologies for aggregating and analyzing data. The aim of most courses in AI is to understand the finer details of the methodological aspects. This course, however, is aimed at developing knowledge of, skills in, and competence of the most used methods. The course exploits the fact that many business-relevant, practical problems applications of data science do not require the most sophisticated methods. Moreover, most of the value-generating solutions should focus on the overall development of the solution, and not only on the data analysis part. Which problem to solve, which business goals to measure, and how to continuously monitor the proposed solution are major parts of the overall process that are hardly talked about. This course specifically focuses on the practical applications of these elements of data-driven analysis projects.

Knowledge: The candidate will establish deep knowledge about the development cycle of data science projects.

Skills: The candidate will gain solid skills in setting up and configuring data science tools. The candidate will develop good skills in identifying what methods are appropriate for what type of problems.

Competence: The candidate will establish competence in the application of selected data science methods to address business and strategic challenges.

The course consists of lectures, assignments and project work. The students need to complete a group-based project that is to be presented as well as two individual assignment. In the group project, the students go through realistic, problem-oriented analytics of the data. The group project is to develop practical skills in configuring the relevant tools/technologies, pre-processing data, and conducting the analytics. The individual assignments discuss the group project in light of relevant literature from the course curriculum.