Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Atelier Datalab - volet technique
Search
Sponsored
·
SiteGround - Reliable hosting with speed, security, and support you can count on.
→
Providenz - Laurent Paoletti
September 29, 2014
Technology
83
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
Atelier Datalab - volet technique
Stockage, analyse, visualisation de données et machine learning
Providenz - Laurent Paoletti
September 29, 2014
More Decks by Providenz - Laurent Paoletti
See All by Providenz - Laurent Paoletti
Introduction au machine learning
providenz
0
220
Des builds front plus rapides
providenz
0
55
Back to front
providenz
0
180
Machine Learning for the rest of us
providenz
1
210
Brunch, le builder pour les developpeurs pressés
providenz
0
170
Postgresql la plateforme de vos données
providenz
0
270
Performance web (Brown bag lunch)
providenz
0
52
Montée en charge
providenz
0
50
Présentation de django
providenz
0
50
Other Decks in Technology
See All in Technology
英語が話せなくてもKubeConスタッフに参加した話
yusuke427
2
1.1k
FDEの心得
noriakioji
5
6.4k
巨大気象データと戦う ― サロゲートモデル学習を高速化する圧縮技術
gpuunite_official
0
250
8bit CPU 2026
koba789
6
2.7k
Invisible to AI? Making TYPO3 Sites Quotable by AI Search Systems
wolfgangwagner
0
240
Adding Right-to-Left support to your web application with CSS logical properties — Lessons from Redmine
vividtone
0
170
SmartHR Engineering Team Deck
smarthr
1
3.5k
DDDのエッセンスを取り入れたAIでの開発
ak2ie
1
210
【GCC2026】TrueHDRIを用いたルックデブ環境とライティングテクニック
bandainamcostudios
PRO
0
280
なぜ Temporal の大小比較には compare しかないのか / Why Does Temporal Only Have compare() for Comparisons
kazukihayase
1
200
型落ちシンクライアント端末のPoEモジュールを自作したかった話
logica0419
0
320
dbt in Microsoft Fabric
ryomaru0825
0
160
Featured
See All Featured
Automating Front-end Workflow
addyosmani
1369
210k
ラッコキーワード サービス紹介資料
rakko
1
4.5M
Building Flexible Design Systems
yeseniaperezcruz
330
40k
Typedesign – Prime Four
hannesfritz
42
3.1k
The AI Revolution Will Not Be Monopolized: How open-source beats economies of scale, even for LLMs
inesmontani
PRO
3
3.7k
Thoughts on Productivity
jonyablonski
76
5.3k
Leveraging LLMs for student feedback in introductory data science courses - posit::conf(2025)
minecr
1
350
WENDY [Excerpt]
tessaabrams
11
39k
Digital Projects Gone Horribly Wrong (And the UX Pros Who Still Save the Day) - Dean Schuster
uxyall
1
2.5k
Effective software design: The role of men in debugging patriarchy in IT @ Voxxed Days AMS
baasie
0
480
A Guide to Academic Writing Using Generative AI - A Workshop
ks91
PRO
1
380
First, design no harm
axbom
PRO
2
1.3k
Transcript
DATALAB l ’atelier Laurent Paoletti @providenz TVT - 29 septembre
2014
DATA BIG DATA DATASCIENCE définitions
VOLUME VÉLOCITÉ VARIÉTÉ COMPLEXITÉ critères
DONNÉES STRUCTURÉES SEMI-STRUCTURÉES NON STRUCTURÉES typologie
TEXTE HORODATEES GÉOGRAPHIQUES SCIENCE - FINANCE LOGS GRAPHE IMAGE/SON/VIDEO typologie
OPENDATA SERVICES - API ORGANIQUE CROWDSOURCING OBJETS CONNECTÉS ACHAT SCRAPING
- EXTRACTION sources
sources - api
HOME SERVEUR(S) CLOUD CUSTOM ! GPU FPGA plateformes -infrastructure
FICHIERS excel csv hdf5 plateformes -persistance
DB RELATIONELLES ! MYSQL POSTGRESQL SQLSERVER, ORACLE plateformes -persistance
SIG:POSTGIS plateformes -persistance
GRAPHES: NEO4J plateformes -persistance
RECHERCHE : ELASTICSEARCH plateformes -persistance
HADOOP SPARK HBASE plateformes -persistance
MAP-REDUCE plateformes -persistance
EXTRACTION NETTOYAGE ETL analyse - préparation
FILTRAGE TRANSFORMATION STATISTIQUES analyse
R SQL PYTHON OPENREFINE analyse - outils
« capacité qu’on donne à une machine d’ingérer des données
à apprendre et de s’enrichir grâce à son expérience » machine learning
machine learning ANTI-SPAM RECOMMANDATIONS SCORING OPTIMISATION DE PRIX IDENTIFICATION
TRAINING DATA machine learning 101
machine learning 101
machine learning 101 setosa
machine learning 101
machine learning 101 DATASET MODELE DATA PREDICTION apprentissage humain
« For a long time, we thought that Tamoxifen was
roughly 80% effective for breast cancer patients. But now we know much more: we know that it’s 100% effective in 70% to 80% of the patients, and ineffective in the rest. » ! machine learning 101
machine learning regression classification !
machine learning - outils R JAVA PYTHON SAAS ! !
visualisation http://flowingdata.com/page/2/
http://www.brightpointinc.com/interactive/political_influence/index.html?source=d3js WEB visualisation
http://www.brightpointinc.com/interactive/political_influence/index.html?source=d3js visualisation
EXCEL - GNUPLOT PYTHON - MATPLOTLIB WEB - D3.JS !
! visualisation - outils
Général: http://www.oreilly.com/data/ Pandas: http://pandas.pydata.org/ R: http://www.r-project.org/ Python: https://www.python.org/ Machine learning:
http://scikit-learn.org/ Openrefine: http://openrefine.org/ Postgis: http://postgis.net/ Elasticsearch: http://www.elasticsearch.org/ Hadoop: http://hadoop.apache.org/ Spark: https://spark.apache.org/ Hbase: http://hbase.apache.org/ D3: http://d3js.org/ Bigml: https://bigml.com/ Prediction API: https://cloud.google.com/prediction/?hl=fr ressources
merci Laurent Paoletti @providenz TVT - 29 septembre 2014