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:

  1. Conduct data analysis and gain insights within a given context.
  2. Employ advanced techniques to visualize and communicate information.
  3. Demonstrate ability to create for the intersection of data and design.

Course Instructors & Teaching Support

  • Lead Instructor: Dr. Wan Fang

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

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   Workshop II: D3 Advanced  
13   Lecture 12: Uncertainty  
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
 
* Due to the number of enrolled students in this course, only half students are selected to present assignment 1 and the other half present assignment 2.

Instructions on Individual Assignments

Instructions on Team Project