Data-driven innovations are shaping the way we live today. Visualization is at the interface between computation and people who work with data and algorithms. Targeting practitioners with computing science background, this course is focused on tools and advanced methods with applications including Biomedicine, Science, and Business.

More details can be found in the Course Outline - Fall 2018.

Instructor:	Steven Bergner <>
Teaching assistant:	Kangxue Yin
Lecture:	W 10:30 am - 12:20 pm, WMC 3253
Lab:	M 11:30 am - 12:20 pm, ASB 10928
Assignment submission:	CourSys

Schedule (tentative)

Shortcuts: HW 1, HW 2, Projects, HW 3, HW 4

			Topics

1	Wed Sep 5	lec	Administrative Goals of Visualization Examples
2	Mon Sep 10	lab	Desktop tools (Tableau etc.)
	Wed Sep 12	lec	Visual design principles and pitfalls
3	Mon Sep 17	lab	Plotting and Dashboards in Python
	Wed Sep 19	lec	Visualization process / pipeline Data Types and Tasks HW1 due: Data dashboard using Tableau et al.
4	Mon Sep 24	lab	homework
	Wed Sep 26	lec	Vis Tasks (cont'd) Marks and Channels / Graphical Perception / Colour
5	Mon Oct 1	lab	Colours & D3
	Wed Oct 3	lec	Exploratory Data Analysis (EDA) Multi/high-dimensional data
6	Mon Oct 8		(no lab - Thanksgiving)
	Wed Oct 10	lec	Data representation Interpolation of spatial data Examples of visual mappings and algorithms for 2D+3D scalar fields HW2 due: Vis in the Browser
7	Mon Oct 15	lab	ParaView tutorial
	Wed Oct 17	lec	Visual mappings and algorithms for 2D+3D scalar fields Colour, Contours, Height plots
8	Mon Oct 22	lab	More on colours
	Wed Oct 24	lec	3D vector fields Milestone 1: Project proposal due
9	Mon Oct 29	lab	Q & A
	Wed Oct 31	lec	Raycasting Machine learning and Visualization
10	Mon Nov 5	lab	GIS tutorial (qgis), Maps
	Wed Nov 7	lec	Guest lecture: Alex Razoumov on Visualization and Supercomputing
11	Mon Nov 12		(no lab - in lieu of Remembrance Day)
	Wed Nov 14	lec	Text Graphs and Trees HW3 due: Climate data and flow vis
12	Mon Nov 19	lab	Graph Drawing Tutorial
	Wed Nov 21	lec	Reducing Attribute and Item Complexicy using Multiple Coordinated Views or Navigation
13	Mon Nov 26	lab	Project Q & A
	Wed Nov 28	lec	Validation and Evaluation of Infovis HW4 due: Network Vis
14	Mon Dec 3	lab	Project Presentations Milestone 2: Project prototypes due

Reading

Two books, (Munzner 2014) and (Telea 2014), are providing basic background to the lectures on design and algorithms, respectively. For specific topics, selected research papers are referred to for further detail, if needed.

Prerequisites

Note, Cmpt361 is not a prerequisite for this course, anymore.

The admission criteria for the graduate program in computing science at SFU are sufficient background for this course. In particular, programming skills in Python or a C-like language are expected, as well as, basic mathematical skills including linear algebra, and basic statistics. Lab sessions provide tutorials on some of the foundations, including computer graphics.

Grading and workload

(Tentative, to be discussed in first lecture.)

Assignments in CMPT 767 are due in two to three week intervals, which works out to 4 assignments (HW 1 - 4). The extra workload for this course is planned around 8-10 h/week including programming and reading tasks. There is no mid-term, but the final project will divide into proposal + milestones from the middle of the semester towards a final presentation. The assignments are weighted equally and count 50% towards the final grade, where the other 50% are composed of different aspects of the project (implementation + presentation + report).