This year-long course aims to achieve two purposes. First, it will equip students with some basic but essential data analytic skills. Second, it will guide students to approach a real world problem from the transdisciplinary perspective. For the first aim, there will be a series of tutorials and workshops on the basic principles and techniques of data science, including some elementary concepts and methods of machine learning. For the second aim, each student will be assigned at least one supervisor in each semester, under whose guidance the student will go through a process of problem identification, analysis, ideation, and solution prototyping. In the first semester (Guided Study I), the main objective is to train students to recognize important sub-problems in a complex issue, identify the types of information and skill that are relevant to addressing a problem, and plan independent studies and research to acquire the relevant knowledge. In the second semester (Guided Study II), the main objective is to guide students to carry out some of the planned studies and research that span more than one discipline, analyse the target problem from multiple, transdisciplinary perspectives, and write proposals of preliminary ideas and steps towards solving the problem. At the end of the course, students are expected to present mock-ups illustrating their problem-solving ideas and reflect in writing on the advantages and challenges of a transdisciplinary approach.