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Syllabus

Infomation of Course

Program Common [공통(상호인정)] Course Type Major Elective [ 전공선택 ]
Course Code 31.481 Course No IE481
Section English English
L:L:C(AU) 3:0:3.0(0) Exam time
(classroom)
- Thu: 09:00~11:45
()
Course Title Special Topics in Industrial Engineering I<Data Visualization > [ 산업공학의 특수논제 I<데이터 가시화> ]
Class time
(classroom)
Tue: 10:30~12:00 / (E2)Industrial Engineering & Management Bldg. [ (E2)산업경영학동 ] (1125호)
Thu: 10:30~12:00 / (E2)Industrial Engineering & Management Bldg. [ (E2)산업경영학동 ] (1125호)
Notice 학부생만 수강 가능, 대학원생 수강 불가능

Information of Professor

Name 이의진(Lee, Uichin)
Department 산업및시스템공학과(Department of Industrial & Systems Engineering)
Phone 042-350-1616
E-Mail uclee@kaist.ac.kr

Plan of Lecture

Syllabus File
Syllabus URL docs.google.com/document/d/1lTjJtuPhZRZNYc2fQW67g2Ya379CtQuLJKPFxhAs1n8/edit?usp=sharing
Summary of Lecture Data visualization techniques help data scientists to interact with data to extract insightful information, examine hypotheses, and perform data storytelling for decision making. This course covers the fundamental concepts of data visualization, such as design principles, representation, perception, color, and data storytelling. Besides, it will provide in-depth tutorials and practices on the entire visualization process (i.e., ideation, prototyping, and usability testing) by building a practical web-based interactive service with Python. The course will be delivered in an active learning format such that concept learning is followed by in-class activities and programming practices. Furthermore, there will be programming sessions (e.g., Web programming, Python visualization libraries) and design studio sessions (e.g., design process and peer feedback). A final project on building real-world visual analytics solutions will help students to use the techniques learned in the class (e.g., exploring a mobile and wearable sensor dataset on the web).
Material for Teaching Visual Thinking for Design, Colin Ware, Morgan Kaufman (2008)
Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures, Claus O. Wilke, O’Reilly 2019 https://serialmentor.com/dataviz
Interactive Data Visualization with Python, Abha Belokar, Sharath Chandra Guntuku, Shubhangi Hora, Ashu Kumar, Oct. 2019
Evaluation Criteria Term project: 50% (DP1 5%, DP2 10%, DP3 10%, DP4 10%, DP5 15%)
Participation: 10% (in-class + studio) + (3% extra points)
1 homework + 3 programming assignments: 20% (HW 5%, PR1 4%, PR2 5%, PR3 6%)
Nano-quiz: 20% (three lowest scored quizzes out of 15 (20%) are dropped)
Lecture Schedule Week 1 Introduction to Data Visualization / HTML/CSS basics
Week 2 Design Principles 1 / Flask Programming Basics
Week 3 Design Principles 2 / Firebase & JavaScript Basics
Week 4 Data Storytelling / Python: Pandas & Static Viz
Week 5 Process & Visual Variables / Python: Interactive Viz 1 (with Bokeh, Plot.ly, Altair)
Week 6 Charting Basics / Python: Interactive Viz 2
Week 7 Interaction
Week 8 Mid-term exam period
Week 9 Perception & Temporal Data Visualization / Python: Temporal Data Visualization
Week 10 Color + Geo Data Viz / Usability Testing Lab
Week 11 Perception (Advanced) / Python: Geographical Data Viz
Week 12 Color (Advanced) / Perception & Color Lab
Week 13 Visual Analytics & Machine Learning / Python: ML Practice (Scikit-Learn) & Advanced Viz Practice
Week 14 Visualization Research / Visualization Research Lab
Week 15 Final Presentations
Week 16 Final exam period
Memo TA: Hansoo Lee, Joonyoung Park, Yugyeong Jung
Real-time distance class using Zoom: https://zoom.us/j/7522370639