* Nathan LHOTE, Maxime SAVARO, Mohammed SENHAJI
* Haolin REN, Baubacar FANE (Tracking the trading bot)
* AUPETIT Jordan, LAUMOND Antoine.
* ANTUNES Léa, ASSAB Rania
* Jérôme Bouzillard et Marjorie Mersch.
* Jolly Solène, Lamoureux Adrien, Sommard Vivien
* Vincent Bocquel, Jérémy Turon
* Etienne Grandier-Vazeille, Benoit Barthès
* Simon Archipoff, Marie Gasparoux
* Nicolas Yvon, Morgane Badré, Maxime Eychenié
* Nicolas Spagnulo, Anne-laure Mesure
* Benjamin LETOURNEAU, Vincent WILMET
It's worth studying this grid a bit, so you can get better grades – just imagine you have to grade your colleagues' work, or your own, after all.
Projects will be evaluated according to several criterion, each of them being assigned a grade [1, 5], interpreted in accordance with the criteria. The interpretation suggested here should not be taken too strictly, they are meant to give a flavor of what makes a difference between assigning a 2 or a 3, instead of a 3 or a 4, for instance.
|Grades & possible interpretations|
|Difficulty of problem addressed / questions asked||Easy||Requires some thinking but answer is straightforward||Requires some thinking and average work||Tricky, requires non obvious work||Difficult, probably impossible to answer satisfactorily in given time|
|Implicit difficulty of dataset used||Straightforward, not much work||Required work but obvious solution||Required work, inventive solution||Needed non obvious combination of ideas and tools||Difficult, probably needed too much time|
|Visualization (effort)||Relatively easy, relied on ready-made solutions||Good combinations of existing ideas and algorithms but could do better||Some thinking, a mixture of ready-made and custom visualization, pretty good end result||Astute and creative, pleasant and relevant||Seems new, required non obvious work|
|Visualization (quality)||Many things wrong, ended producing misleading graphical views||Many things need to be fixed (wrong colormap, wrong mapping for size, wrong algorithms used, etc.)||Overall OK, with only minor things to improve or add||Very good impression, clean colormap choices, shapes, relevant layouts, etc.||Additional aesthetics, almost close to professional graphics design|
|Visualization (creativity)||None really||Combining existing viz was a good idea||Unusual use of existing algorithm and/or customization||Astute solution, demonstrate mastery of ideas and techniques||Clearly exhibit a creative mind|
|Visualization (relevancy)||Didn't help much to solve the problem / answer questions||Only gave a hint about a possible solution||Was able to provide a non-surprising answer, with no real convincing arguments||Clearly allowed to provide a firm answer to problem||Brought additional insights on the problem|
|Written document (wiki)||Poor, hardly readable or usable||Contains everything it should, but needs to be reworked to be usable||Globally OK but could be improved||Good job, reads well||Extremely well crafted, could even be published as a blog as is|
|Evaluation (discussion)||Poor, confusing||Everything seems to be there, but unorderly making it hard to grasp the work||Globally OK, could improved (showing better images, emphasizing the right things, etc.)||Good presentation, well balanced, lively, got all the information in the right order, straight to the point||Entertaining, attractive, intellectually challenging discussion|
I have presented the term project using data from the 7th Framework Programme (FP7 - funding research in Europe) listing all participations of organizations (research centers, private companies) to projects.
If you have a favorite dataset, do not hesitate to use it. Your dataset must not be too small, not too complex and somehow “clean” (this is not a data curation exercise). The web is after all plenty of “Open Data” …
Tracking the trading bot
FP7 – European money funding research
There are tons of other datasets available
Just browse the web. The KONECT site is a good entry point.