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Course Objective
Recently, various energy and environmental data have been collected according to the deployment of sensors. This course aims to explore the implications of recent studies using big data in energy and environment-related fields. Also, hands-on exercises will give students some ideas on how to deal with the actual energy and environmental big data.
This seminar class is based on student’s active participation. This class includes 2-3 in-class hands-on exercises, which don’t require prerequisite knowledge of Python. Also, students’ weekly presentations of the assigned articles. Students are required to develop and present a research proposal based on the data science approach.
Evaluation Criteria
Attendance: 10%
In-class participation [discussions and comments]: 10%
Paper presentation: 20%
Hands-on exercises results: 15%
Two research presentations: 25%
Final research paper: 20%
Lecture Schedule
Week 1 (8/9) Course Orientation; climate change and challenges for carbon neutrality
Week 2 (9/5) The role of data and the contributions of computational social sciences to tackle climate change
Week 3 (9/12) The big data sources and issues
Week 4 (9/19) Hands-on exercise ? Data balancing: Electricity data balancing
Week 5 (9/26) Patterns and trends in the data
Week 6 (10/3) National Foundation Day
Week 7 (10/10) Student Research Proposal
Week 8 (10/17) No Class
Week 9 (10/24) Forecasting demands and supply
Week 10 (10/31) Hands-on exercise ? Forecasting: VRE generation forecasting
Week 11 (11/7) Understanding the public’s attitudes towards energy and environmental issues using text data
Week 12 (11/14) Hands-on exercise ? Analysis of unstructured data: Naver news data analysis
Week 13 (11/21) The effect of interventions
Week 14 (11/28) Emerging themes and approaches
Week 15 (12/5) Student final project presentation
Week 16 (12/12) No class