GEH2047 Introduction to Data Visualization and Data Analytics in Research and Daily Lives
This course is designed to enable students to learn the significance of data visualization in research and daily lives and develop knowledge and skills to present quantitative data using data visualization tools for their own studies and honors projects. In considering most students lack data literacy, the course will not require extensive quantitative analyses, but adopt a problem-solving approach by linking research problems, hypotheses, data analytics and interpretations of findings.
Students have to actively participate in formative assessments, group presentation project and individual portfolio. There are numerous computer lab workshops in which students will try out data visualization and data analytics tools. Attendance in these workshops is considered a must for developing skills through hands-on practices of data visualization applications and for completing problem-solving tasks, group project, and individual portfolio. The research topics for the group project will cover five disciplines (i.e. sociology, psychology, linguistics, computer science, and education) such that students can choose their preferences with respect to their majors and then elaborate further by drawing relevance to an issue in daily lives (including teaching). The research topics are chosen as they are expected to enhance students’ social awareness of issues that are important for theory and practice.
Course Intended Learning Outcomes (CILOs)
Upon completion of this course, students will be able to:
CILO1: Understand the development and principles of data analytics and data visualization;
CILO2: Understand the applications of data analytics and data visualization in advancing our understanding of problems in research and daily lives;
CILO3: Develop and apply competencies in using software for data analytics and data visualization; and
CILO4: Apply theories of data analytics and data visualization and competencies in using software for data visualization and data analytics in instructor-chosen and student-selected problems and research topics related to their majors and/or honors projects.