Complexity of data science has recently become harder and harder due to increasing complexity of data. Several new challenges emerged in data science ranging from software, algorithms, data and interpretation. For example, when developing software to analyze and visualize data, one faces to debug both software and data coincidently. Shortly, one needs to solve a sort of egg-and-chicken problem for software developments since errors might be originated from either software or data. How do we overcome it? When we visualize heterogeneous multidimensional data, we face the difficulty of understanding a complex system consisting of a lot of components. When we deal with high-dimensional heterogeneous data, one has to identify many interactions and understand their relationships. Additionally, these problems are today urged to be considered in a context of sustainability concepts, which introduce even more dimensions to the mere technical issues of big data. In this workshop, case studies of data analysis and efficient algorithms to analyze actual data related to real world in order to manage their overall complexity are collected.