Course Description
Viewing design problems as a collection of decision-making processes, data has been one of the important foundations for making such decisions. This course introduces the basics of data-related methods and cutting-edge applications using a programming language for computational practice. Through examples of data generated from human activities and nature, students will learn techniques in the representation, processing, analysis, learning, and visualization of data to gain insights, communicate information, and create for the intersection of data and design. The course will include field trips depending on availability and external collaborator, and the contents are subject to change to fulfill the course objectives.
Learning Outcome
At the end of this course, students will be able to:
- Conduct data analysis and gain insights within a given context.
- Employ advanced techniques to visualize and communicate information.
- Demonstrate ability to create for the intersection of data and design.
Course Instructors & Teaching Support
- Lead Instructor: Dr. Wan Fang
- wanf@sustech.edu.cn
- Tuesdays between 1000 and 1200
- Level 3, Block C1, Wisdom Park
- Teaching Assistant: Cheng Sirui
Grading

Academic Integrity
- This course follows the SUSTech Code of Academic Integrity. This course’s students must abide by the SUSTech Code of Academic Integrity. Any work submitted by a student in this course for academic credit will be the student’s work. Violations of the rules (e.g., cheating, copying, non-approved collaborations) will not be tolerated.
Course Materials
- (2008) Handbook of Data Visualization, Chun-houh Chen, Springer
- (2015) Storytelling with Data, Cole Nussbaumer Knaflic, Wiley
- (2001) The Visual Display of Quantitative Information, Edward R. Tufte, Graphics Press
Lecture Notes
Week | Monday, 8:00-9:50 | Wednesday, 10:20-12:10 | Assignments DDL |
---|---|---|---|
01 | Lecture 01: Data Storytelling | Course Introduction | ||
02 | Lecture 02: Introduction to Data | Dimensional Visualization of Data | Lecture 03: Design with Analytics | The Importance of Context | |
03 | Lecture 04: Concepts of Data X | Global Health Data | Team of 3 members | |
04 | Lecture 05: Data, Narrative, Visualization | Four Types of Data Analytics | Tutorial: 3-minute story & Big Idea | |
05 | Lecture 06: Data Quality Assessment | Practice with Python | ||
06 | Lecture 07: Descriptive Statistics | Exploratory Data Analysis | Tutorial | |
07 | Assignment 1 Presentation* | Assignment 1: Apr 2 @ 10:00 | |
08 | Lecture 08: Inferential Statistics | Lecture 9: Data-Driven Design | A/B Testing | |
09 | Interim Review | Submission: Apr 16 @ 10:00 | |
10 | Workshop I: D3 Basic | Lecture 10: Visual Encoding Design | |
11 | Lecture 11: DO and DON’T | Interaction | ||
12 | Lecture 12: Uncertainty | ||
13 | Workshop II: D3 Advance | ||
14 | Tutorial | Assignment 2 Presentation* | Assignment 2: May 21 @ 10:00 |
15 | Lecture 14: Course Review | ||
16 | Final Review Presentation | Final Project: Jun 8 @ 23:30 | |
17-18 | Final Exam | 参考课件: Data Thinking, EDA, A/B Testing, Visual Encoding Design |
Instructions on Individual Assignments


Instructions on Team Project

