NDAK15017U Interactive Data Exploration (IDE)
Visualizing scientific data is important, not only for presenting results and conclusions in a convincing and appealing manner, but also as tools for exploration and generating hypotheses. Interaction networks between molecules, social relationships between friends, or road networks can all be represented as graphs and visualized using force-directed graph layouts (http://getspringy.com). Large treestructures can be visualized and explored using sunbursts (http://mbostock.github.io/protovis/ex/sunburst.html) or collapsible trees (http://bl.ocks.org/tchaymore/1249394). Finally, displaying, navigating and animating three-dimensional scenes is essential to most disciplines that involve some form of physical simulation. While static visualizations can be created with tools like matplotlib, animated or interactive visualizations that are easily shared across any modern platform (webpage, tablet app, phone app, conference video-presentation) have much wider uses.
This course will introduce interactive visualization techniques that are particularly relevant in science, and teach the student to implement these. A set of techniques, representing a wide range of scientific visualizations, will be demonstrated during lectures and projects, and students will be trained in reading, transforming and reducing a dataset to a representation that fits a certain visualization need. There is some flexibility to adapt projects to the students background but likely topics that will be covered include, interactive scatter+bar plots, force-directed graph drawing, decision trees, geographical maps, scene visualization, 3D volume visualization, and 3D streamlines/streaklines/pathlines.
There will be weekly programming exercises, mainly creating and
using JavaScript libraries, where students get hands-on experience
with the curriculum.
Knowledge
Familiarity with a wide set of techniques used for interactive visualization
Experience with 4-6 key interactive visualization techniques
- Familiarity with at least two large JavaScript libraries that will enable students to easily build or adapt visualizations in their future research careers.
Skills
Visualizing graphs, trees, matrices, maps, and 3D-scenes using JavaScript libraries
Parsing, representing and transforming data from/to e.g. CSV, JSON, and XML formats using JavaScript libraries
Competences
Producing cross-platform, sharable, interactive visualizations of scientific data
Producing high-quality, publication ready, customized figures for scientific publications
The following books are expected to be used:
Interactive Data Visualization for the Web, Scott Murray
- Learning Three.js
- Category
- Hours
- Exam
- 1
- Exercises
- 60
- Lectures
- 28
- Preparation
- 42
- Project work
- 75
- Total
- 206
As
an exchange, guest and credit student - click here!
Continuing Education - click here!
- Credit
- 7,5 ECTS
- Type of assessment
- Continuous assessmentOral defence, 15minFinal grade is an overall evaluation of
- the 5 individual weekly projects
- final group project
- oral defense of final project (no preparation) - Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
- Re-exam
Oral exam, 30 minutes, with no preparation. To qualify 4 of the 5 individual weekly projects must be passed, no later than two weeks before the re-exam.
Criteria for exam assesment
To get the grade 12, a student should
- Have accomplished the learning objectives of the course
- Be able to present and discuss both final project and topics taught during the course
Course information
- Language
- English
- Course code
- NDAK15017U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- A
- Course capacity
- No limit
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Course responsibles
- Wouter Krogh Boomsma (2-7a6543676c316e7831676e)