Problem-driven teaching activities for the capstone project course of data science

Abstract

The rapid development of data applications poses severe challenges as well as significant opportunities for data science specialty. In this poster, the authors report on problem-driven teaching activities for the capstone project course of data science. The teaching activities consist of problem formation from real-world applications based on data analysis competitions, refining techniques and theories to build domain knowledge, and implementing data science practice to improve students' ability of data thinking and data analysis. Preliminary results indicate that the problem-driven teaching activities can be efficiently carried out to facilitate students to achieve the ability of data analysis, and students attending the course win world-class data analysis competitions, such as KDD (Knowledge Discovery and Data Mining) Cup and Kaggle.