Presentation Data Science Challenge
•
0 j'aime•791 vues
Presenting a D3js dashboard created for the Data Innovation Summit based on survey results.
Recommandé
Recommandé
Contenu connexe
En vedette
En vedette (16)
Similaire à Presentation Data Science Challenge
Similaire à Presentation Data Science Challenge (20)
Dernier
Dernier (20)
Presentation Data Science Challenge
- 1. Data Innovation Dashboard Data Visualizations for the Curious 1
- 2. Outline ● Interactive Data Visualizations: Motivation ● Data Innovation Summit Survey ● Optional: Getting started with D3.js 2
- 3. Interactive data visualizations: Motivation 3
- 4. Any questions? “All our knowledge results from questions, which is another way of saying that questioning is our most important intellectual tool” Neil Postman 4
- 5. (for the sole purpose of drama and making a point) 5
- 6. Mission 6
- 9. Do we really want to ... give answers to questions never asked 9
- 11. Data Innovation Summit Survey: Dashboard 11
- 12. DIS Survey results Goal: analyze the survey data! Two types of data: ● User metadata: gender, age,... ● User skill scores: math, business,... 12
- 14. 14
- 15. Data Innovation Summit Survey: Analysis 15
- 16. Popular & Rare skills
- 18. Pairwise score comparisons -Business => -Marketing Math ~ Statistics Math Business +Business => -Marketing => Marketing is a rare skill (prev.) 3->3 3->4 3->5 3->2 2->3 2->2 2->1 2->4 4->4 4->3 4->5 1->5 1->4 5->2 5->1 4->2 4->1
- 19. Who talks business? Gender: irrelevant Age: 45-54 Degree: irrelevant Experience: 10+ 19 45+ 45+ 10+ 10+ PhD PhD
- 20. Machine learning for PhDs 20 Gender: irrelevant Age: 25-34 Degree: PhD! Experience: 5-10 PhD PhD 25+ 25+ 5-10 5-10
- 21. Data driven: new data => new visual Age: 18-24: Female 7 (23%) <> Tot 19(8%) Experience: No: Female 9 (30%) <> Tot 34(14%) 21 -25-25 00
- 22. Distributions 22 Statistics: 5 -> 1 Females don’t like Backends / NoSQL
- 23. Females don’t do fives 23 very few 5-ratings!
- 24. Revealing the secret of women If a woman says 4 she basically means 5 If a woman says I am not experienced she’s probably as skilled as you are! 24
- 25. Fraud Detection 25 Anomaly Detection Good at everything!?
- 26. GOTCHA! 26 Metadata of super scientist
- 27. Anonimization is a joke GOTCHA! 27
- 28. Any questions ... Maybe ask our app? Blogpost: https://hippomongous.wordpress.com/2015/03/25/data-innovation-summit-dashboard/ Webapp: http://facet-it.be/dis/index.html 28
- 29. Thanks for listening Dieter De Witte Nicholas Ocket Big Data Scientist @ iMinds - Multimedialab Senior java developer & Freelance webdesigner 29
- 31. Do it yourself... Read online: http://chimera.labs.oreilly. com/books/1230000000345/ Tech Stack: SVG, HTML, CSS, Javascript 31
- 32. About D3.js Created by Mike Bostock more examples: http://bl.ocks.org/mbostock 32
- 33. Features D3.js Most prominent api methods: .append() .data() .transition() .on() .selectAll() 33
Notes de l'éditeur
- would be funny if this would already be the end of the presentation blablabla . but questionsa are important! en.wikiquote.org/wiki/Neil_Postman
- blank slide
- but that is the wrong question! It is not us who wants to know what is in the dataset ...so… what we didn’t want to do
- vaak krijg je als data scientist een dataset en de domein expert je de vraag om daar patronen in te zoeken, de domein expert weet ook niet wat je precies moet zoeken vinden omdat hij geen data scientist is… het probleem dat de data analyst mooilijk kan inschatten wat de zinvolle patronen zijn...
- Histogram matrices have filter functions connected the bars, the filter function also influence the population overview on the previous slide
- Slope chart can be used to focus on individuals Some people are good at orthogonal skills!? Someone’s honest about their visual skills
- Dieter De Witte: definetly the prettiest :-) Nicholas Ocket: definetely the piechart man
- .data() most important one: joining data with xml elements for our app de .on is also very relevant since actions in one visualisations trigger queries on a second visualisation