Course Overview

Subject Description

Computational Thinking for Design is an introductory programming course that combines programming both in the design and computing contexts targeted at novice programmers. It introduces students to programming and design computing skills essential for their studies at SUTD regardless of pillar preference.

PillarASD & ISTD
GradingPass / Fail

Learning Objectives

  1. Acquire conceptual knowledge and skills for visual and python programming. 
  2. Acquire basic knowledge of computational geometry concepts.
  3. Develop hands‐on experience with applying computational thinking approaches to explore solutions to design and engineering problems.
  4. Gain skills in programming the Raspberry pi micro-controller.
  5. Learn and practice effective technical communication skills for formal written reports.

Measurable Outcomes

  1. Implement a working visual/textual program to generate variations of a 3‐dimensional model in accordance to a given geometric problem
  2. Develop python programs that meet a set of specifications to solve computational problems.
  3. Produce a physical artefact as the final outcome of a computational process for a design project.
  4. Develop and deliver a written report on time that describes the results of the design project.

Course Structure

The first half of this course is led by faculty of Architecture and Sustainable Design [ASD], while the second half is led by faculty of Information Systems Technology and Design [ISTD]. This site contains contents regarding the first part of the term.

The first six weeks focus on computational design using the visual programming paradigm [>]. The following six weeks, after recess week, introduce programming using Python. Contents taught in both parts of the course complement one another.

Course Overview

Computational Design Thinking takes place twice a week. Each session is 2.5 hours long, split into two circa one hour segments. The first segment is typically theory oriented while the second aims towards practice and applications. The content is organised thematically into 3 parts: Generative Design, Parametric Design and Simulateion. The table below captures the overall organization content in further details.






Generative Design

Parametric Design

Simulated Design

Session A

1A – Course Overview
Introduction and administrative matters

Orientation Activities
No cohort session

2A – Visual programming
Introduction to Grasshopper

2A – Associative modelling
Symbolic expressions to epicycles

3A – Spatial Geometry
Points and vector algebra

3A – Curve geometry
Tangent, normal and curvature

4A – Information nesting
Lists, grids, tables

4A – Data landscapes
Scalar fields

5A – Space and time
Clock and persistence

5A – Modelling Entities
Projectile physics

6A – Form-finding I
Vaults and minimal surfaces

6A – Form-finding II
Mesh relaxation

Session B

1B – Computer Aided Design
Introduction to Rhinoceros

1B – Ranges and expressions
Spreadsheet programming

2B – Logical patterns
Conditionals and control flow

2B – Generative design
Assignment 1 workshop

3B – Surface geometry
Tangent, normal and curvature

3B – Parametric modelling
Spiral Bridge

4B – Mesh geometry
Topology , visualisation and subdivision

4B – Parametric design
Assignment 2 workshop

5B – Multibody dynamics

5B – Material Elasticity
Spring systems

6B – Swarm Intelligence

Concluding Session
Discussion and review

Course Instructors

Cohort 01

Zheng Kai

Lyvia Simano
Kat Yongjie
Teaching Assistants

Cohort 05

Jason Lim
Lecturer (Course Lead)

Janice Yong
Jiang Zhuoqun
Teaching Assistants

Cohort 09

Naomi Bachtiar
Adjunct Lecturer

Praveen Govindarajan
Janice Yong
Rachel Lim

Teaching Assistants

Cohort 02

Geraldine Quek
Faculty Fellow

Valent Tan
Samuel Lam
Teaching Assistants

Cohort 06

Geraldine Quek
Faculty Fellow

Sharmayne Lim
Praveen Govindarajan
Teaching Assistants

Cohort 10

Geraldine Quek
Faculty Fellow

Jiang Zhuoqun
Sharmayne Lim
Teaching Assistants

Cohort 03

Bige Tuncer
Associate Professor

Praveen Govindarajan
Megan Lee

Teaching Assistants

Cohort 07

Aloysius Lian
Adjunct Lecturer

Jiang Zhuoqun
Teaching Assistants

Cohort 11

Thaddeus Lee
Adjunct Lecturer

Aaron Soares
Sheryl Mah
Teaching Assistants

Cohort 04

Rachel Tan
Adjunct Lecturer

Sharmayne Lim
Praveen Govindarajan
Teaching Assistants

Cohort 08

Aloysius Lian
Adjunct Lecturer

Jiang Zhuoqun
Praveen Govindarajan
Teaching Assistants

Grading Components

The table below summarizes grading components. Assignments 1 and 2 will be handed out in the first ASD half of the course (weeks 1 to 6). Coursework comprises quizes and short exercises that are given out throughout the course. The 1D project and final exam components are part of the second ISTD half of the course (weeks 8 to 14). Finally, the 2D Project is a common project that involves all freshmore courses in week 10.

1 – 6Coursework (Visual programming)4%
2 – 4Assignment 115%
4 – 6Assignment 220%
8 – 13Coursework (Python programming)9%
8 – 131D Project10%
102D Project15%
14Final Examination25%
Evaluation Survey &
Course Participation

Course Policies

Coursework submitted within seven days after the deadline will have a 50% penalty on the score, thereafter will have a 100% penalty. Students may only miss formal assessments (quizzes, exams) due to the following reasons: (1) medical leave, (2) family emergencies, (3) other matters beyond their control, with documentary proof.

During class sessions all personal communication devices should be switched to silent mode. The use of social media unrelated to classroom activities (such as for private mail, instant messaging, surfing the internet, reading the news, or playing games) is considered inappropriate and distracting to other people.

Copying from someone else’s assignments or other class content is considered cheating and is not tolerated in this class. Signing an attendance sheet in place of another student is also considered cheating. You are expected to attend all classroom sessions.

Please remember that attempting to dishonestly influence or manipulate an academic evaluation, grade, or record is considered a breach of course rules and will be taken very seriously by the instructors, leading to undesirable results for the students conducting these actions.