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 & Lab Notes

WeekTuesday, 14:00-15:50Friday, 14:00-15:50Assignments DDL
01Lecture 01: Data Storytelling | Course Introduction
02Lecture 02: Introduction to Data | Basic Visualization of DataLecture 03: The Importance of Context | Design with Analytics
03Lecture 04: Concepts of Data X | Data X by ExamplesTeam of 2 members (or 1 if you could find a teammate)
04Lecture 05: Data, Narrative, Visualization | Four Types of Data AnalyticsTeam Project Presentation: 3-minute story & Big IdeaSlides Submission:
Mar 14 @ 23:30
05Lecture 06: Data Quality Assessment | Practice with Python
06Lecture 07: Descriptive Statistics | Exploratory Data AnalysisLecture 08: Inferential Statistics
07Assignment 1 Presentation*Assignment 1:
Apr 1 @ 23:30
08Lecture 13: Data-Driven Design | A/B TestingGuest Lecture
09Interim ReviewSlides Submission:
Apr 15 @ 23:30
10Lecture 09: Visual Encoding DesignLecture 10: Tableau Basics by TA
11Lecture 11: DO and DON’T | Interaction
12Lecture 12: Advanced Charts in Tableau by TATutorial
13Assignment 2 Presentation*Assignment 2:
May 13 @ 23:30
14Lecture 13: UncertaintyTutorial
15Tutorial
16Lecture 14: Course ReviewFinal ReviewFinal Project:
Jun 9 @ 23:30
18Final Exam: Jun 18, 14:00-16:00, 三教303参考课件:
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

  • Form a team of 2 members for the final project
  • The details of the final showcase presentation are subject to change according to the number of total projects