DATA VISUALIZATION AND REPRESENTATION

GRA517

Course Code: GRA517
Semester: -
Theory+Practice: 2 + 2
ECTS: 10

Course Type

Elective Course

Course Level

Master's Degree With Thesis

Year of Study

Semester

ECTS

Name of Lecturer(s)

Learning Outcomes

At the end of this course, the students;

Data Literacy and AI-Assisted Analysis
No-Code Generative Design Competence
Visual Storytelling (Data Storytelling)
Multi-Media Competence and Medium Adaptation

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

Course Description

This course covers the processes of analyzing, making sense of, and transforming complex data sets into an aesthetic visual language from a graphic design perspective. The course is supported by methods that do not require coding knowledge (no-code) and current Artificial Intelligence (AI) technologies. While students experience concepts such as data mining, synthetic data generation, and generative design in a studio environment , they learn not only to "show" data but to "tell stories" through data (data storytelling). The aim is to produce strong visual representations that interact with the audience by passing information through a critical filter.

Course Contents

WEEK	TOPICS
1^st Week	The Intersection of Data, Information, and Graphic Design: The history of data visualization, explanation of the hierarchy of knowledge, and the balance between aesthetics and functionality.
2^nd Week	Analysis with Data Mining and Artificial Intelligence: Explanation of data types (Qualitative, Quantitative, Temporal, Spatial) and transforming them into meaningful insights for the designer.
3^rd Week	Visual Perception, Cognition, and Gestalt: Using Gestalt principles in data grouping; the semantic impact of color theory on data.
4^th Week	Networks, Flows (Sankey), and Spatial Maps: Studies on conveying data through metaphors.
5^th Week	Static Infographics and Editorial Design: High-density data design for magazines, posters, and print media.
6^th Week	Data and No-Code Generative Design: Speculative Design and Design Fiction.
7^th Week	Typography of Numerals: Labeling hierarchy, readability, and font selection in data-intensive designs.
8^th Week	Mapping and Geographic Information Systems (GIS): Cartographic design principles and the visualization of spatial data.
9^th Week	Midterm / Critique: Presentation and critique of the midterm projects
10^th Week	Data Storytelling and Scrollytelling.
11^th Week	Artificial Intelligence Ethics: Bias and "Deepfake" data.
12^th Week	3-Dimensional Data and Data Physicalization.
13^th Week	Final Project Development: The data collection (or AI generation) and design concept phase for topics selected by students.
14^th Week	Final Exhibition and Jury.

Recommended or Required Reading

The Visual Display of Quantitative Information ? Edward Tufte
Storytelling with Data: A Data Visualization Guide for Business Professionals ? Cole Nussbaumer Knaflic
The Functional Art: An Introduction to Information Graphics and Visualization ? Alberto Cairo
Visual Complexity: Mapping Patterns of Information ? Manuel Lima
Dear Data ? Giorgia Lupi & Stefanie Posavec

Planned Learning Activities and Teaching Methods

Lecture
Case Study
Project
Presentation

Assessment Methods and Criteria

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Midterm exam
Preparation for Quiz
Individual or group work
Preparation for Final exam
Course hours
Preparation for Midterm exam
Laboratory (including preparation)
Final exam
Homework
Total Work Load			0
Total Work Load / 30 (h)			0
ECTS Credit of the Course			10

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3	LO4
K1
K2
K3
K4
K5
K6
K7
K8
K9
K10
K11
K12
K13
K14
K15
K16
K17