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9 semaines intensives \u00e0 coder en Python et \u00e0 g\u00e9rer des donn\u00e9es. Anne-Marie et Christophe, alumni de la 2e promotion en Data Science du Wagon Paris, ont d\u00e9velopp\u00e9 les dix derniers jours du bootcamp deux projets : Catch a Fish et Djinews. Retour sur les diff\u00e9rentes \u00e9tapes qui leur ont permis de les r\u00e9aliser et de les pr\u00e9senter lors du Demo Day.","_yoast_wpseo_title":"Djinews et Catch a Fish, 2 projets du bootcamp Data Science | Le Wagon","contenu_de_larticle":"<h3>Quel est ton parcours et pourquoi as-tu souhait\u00e9 te former \u00e0 la data science ?&nbsp;<\/h3><div><strong>Anne-Marie :<\/strong> Je n\u2019ai pas un parcours tr\u00e8s classique pour un.e \u00e9tudiant.e du Wagon. J\u2019ai d\u2019abord \u00e9t\u00e9 professeure des Sciences de la Vie et de la Terre en coll\u00e8ge puis conseill\u00e8re en formation continue au rectorat de Versailles. Je faisais partie du service qui con\u00e7oit et met en oeuvre le plan de formation des personnels de l\u2019acad\u00e9mie. C\u2019est l\u00e0 o\u00f9 <strong>je me suis d\u00e9couvert une app\u00e9tence tr\u00e8s forte pour la data science et la programmation. <br><\/strong><br>En effet, le service devait concevoir, mettre en oeuvre puis \u00e9valuer plusieurs milliers de stages par an. Or j\u2019aime programmer depuis tr\u00e8s longtemps et j\u2019avais d\u00e9j\u00e0 suivi des cours de d\u00e9veloppement mobile et android au CNAM. C\u2019est donc tout naturellement que j\u2019ai commenc\u00e9 \u00e0 automatiser avec VBA et parfois python des t\u00e2ches simples comme l\u2019insertion des candidatures dans notre base de donn\u00e9es. Je m\u2019occupais aussi d\u2019une partie des requ\u00eates et statistiques r\u00e9alis\u00e9es en interne. <strong>J\u2019ai choisi de participer au bootcamp data science pour d\u00e9velopper des comp\u00e9tences de data analyst et aussi d\u00e9couvrir le monde des start-ups.<\/strong> Et puis, la double comp\u00e9tence administrative\/num\u00e9rique me semblait un atout indispensable pour devenir un cadre administratif dans la fonction publique.&nbsp;<br><br><\/div><blockquote>Nous avons donc choisi de combiner deux types d\u2019approches : l\u2019utilisation de la technique de Data Augmentation et le Transfert Learning.&nbsp;<\/blockquote><div><br><strong>Christophe :<\/strong> J'ai fait un Master en \u00abFinance &amp; Investments\u00bb \u00e0 Rotterdam School of Management et un Master en \u00abBig Data et Business Analytics\u00bb \u00e0 l'ESCP Europe. J'ai \u00e9galement eu quelques exp\u00e9riences en conseil dans le domaine de la transformation digitale. Mon deuxi\u00e8me Master et mon exp\u00e9rience professionnelle m'avaient d\u00e9j\u00e0 donn\u00e9 quelques bases en data science. <strong>Le bootcamp en data science du Wagon m'a servi de compl\u00e9ment par rapport \u00e0 mon Master pour solidifier mes comp\u00e9tences techniques et agrandir mon r\u00e9seau dans la data \u00e0 Paris.<\/strong><\/div><div><br><\/div><h3>Peux-tu r\u00e9sumer la d\u00e9marche que vous avez suivie pour votre projet ?&nbsp;<\/h3><div><strong>Christophe :<\/strong> L'id\u00e9e \u00e9tait d'utiliser des news pour pr\u00e9dire le stock market et cr\u00e9er une application de recommandation pour les investisseurs. Le dataset existait d\u00e9j\u00e0 sur un challenge <a href=\"https:\/\/www.kaggle.com\/\">Kaggle<\/a> et on a scrapp\u00e9 les donn\u00e9es manquantes pour pouvoir faire des pr\u00e9dictions en live. <strong>On a cr\u00e9\u00e9 un mod\u00e8le qui classifie les top des 25 news de chaque jours, dans 5 variables, selon<\/strong> : <br><br>- L'objectivit\u00e9<br>- La subjectivit\u00e9<br>- La neutralit\u00e9<br>- Si c'est positif ou n\u00e9gatif. <br><br>Ces variables ont \u00e9t\u00e9 utilis\u00e9es dans un mod\u00e8le de type lin\u00e9aire (Linear Discriminant Analysis).<strong> Au final, notre mod\u00e8le a \u00e9t\u00e9 capable de pr\u00e9dire 8 fois sur 10, si le cours de la Bourse de la journ\u00e9e allait augmenter ou diminuer.<\/strong><\/div><div><br><\/div><div><strong>Anne-Marie :<\/strong> Notre projet \u201ccatch a fish !\u201d consistait \u00e0<strong> cr\u00e9er une application qui permettait d\u2019identifier des poissons \u00e0 partir de leur photographie.<\/strong> Je suis photographe animalier amateur et le domaine de la reconnaissance d\u2019images m\u2019a toujours int\u00e9ress\u00e9e. L\u2019id\u00e9e m\u2019est venue en observant des visiteurs dans les aquariums. <strong>J\u2019avais envie de proposer une nouvelle forme de visite avec un aspect ludique tr\u00e8s marqu\u00e9, sur le mod\u00e8le d\u2019une chasse au tr\u00e9sor.&nbsp;<br><\/strong><br><\/div><blockquote>Le d\u00e9ploiement sur GCP (Google Cloud Platform) n\u2019a pas \u00e9t\u00e9 simple mais nous disposons maintenant des comp\u00e9tences n\u00e9cessaires pour l\u2019utiliser dans nos futurs projets.&nbsp;<\/blockquote><div><br><strong>La principale difficult\u00e9 \u00e9tait de trouver suffisamment d\u2019images pour pouvoir entra\u00eener le mod\u00e8le de deep learning utilis\u00e9 pour la reconnaissance d\u2019images <\/strong>car nous n\u2019avons pas trouv\u00e9 de dataset tout pr\u00eat. C\u2019est pourquoi nous avons utilis\u00e9 la recherche d\u2019images de google avec une extension de google chrome qui permet d\u2019automatiser en partie le t\u00e9l\u00e9chargement. <strong>Il nous a fallu quasiment trois jours pour nettoyer les donn\u00e9es : \u00e9liminer les doublons et v\u00e9rifier les identifications. <\/strong>Cela nous a permis de r\u00e9cup\u00e9rer environ 80 images pour 11 esp\u00e8ces de poissons diff\u00e9rentes. Ce nombre d\u2019images n\u2019\u00e9tait pas suffisant pour obtenir un mod\u00e8le avec un bon taux de reconnaissance. Nous avons donc choisi de combiner deux types d\u2019approches : l\u2019utilisation de la technique de data augmentation et le transfert learning.&nbsp;<br><br>La premi\u00e8re consiste \u00e0 cr\u00e9er des images modifi\u00e9es \u00e0 partir des images de notre dataset (en jouant sur le zoom, la rotation, ou bien le d\u00e9calage du sujet). La seconde est bas\u00e9e sur un r\u00e9seau de neurones VGG16 qui a d\u00e9j\u00e0 \u00e9t\u00e9 entra\u00een\u00e9 sur des milliers d\u2019images. Nous avons ajout\u00e9 \u00e0 ce r\u00e9seau des couches suppl\u00e9mentaires pour le sp\u00e9cialiser dans la reconnaissance de poissons. Cela a bien fonctionn\u00e9 et nous avons obtenu un score de 70%.&nbsp;<\/div><div><a href=\"https:\/\/res.cloudinary.com\/wagon\/image\/upload\/v1\/cache\/206fe38da0bebc40e365b517c82959fe.jpg\" data-trix-attachment=\"{&quot;contentType&quot;:&quot;image\/jpeg&quot;,&quot;filename&quot;:&quot;ablwsk-lewagon-3600_catch a fish.jpg&quot;,&quot;filesize&quot;:128374,&quot;height&quot;:1000,&quot;href&quot;:&quot;https:\/\/res.cloudinary.com\/wagon\/image\/upload\/v1\/cache\/206fe38da0bebc40e365b517c82959fe.jpg&quot;,&quot;url&quot;:&quot;https:\/\/res.cloudinary.com\/wagon\/image\/upload\/v1\/cache\/206fe38da0bebc40e365b517c82959fe.jpg&quot;,&quot;width&quot;:1500}\" data-trix-content-type=\"image\/jpeg\" data-trix-attributes=\"{&quot;caption&quot;:&quot;Dany, Anne-Marie et Pauline du projet : Catch a Fish&quot;}\"><figure class=\"attachment attachment--preview attachment--jpg\"><img src=\"https:\/\/res.cloudinary.com\/wagon\/image\/upload\/v1\/cache\/206fe38da0bebc40e365b517c82959fe.jpg\" width=\"1500\" height=\"1000\"><figcaption class=\"attachment__caption attachment__caption--edited\">Dany, Anne-Marie et Pauline du projet : Catch a Fish<\/figcaption><\/figure><\/a><br><br><\/div><h3>Qui \u00e9taient les membres de ton \u00e9quipe et comment vous \u00eates vous organis\u00e9.e.s ?<\/h3><div><strong>Anne-Marie :<\/strong> Notre \u00e9quipe \u00e9tait compos\u00e9e de 4 personnes : Dany, Paul-Emmanuel, Pauline et moi. <strong>Nous avons commenc\u00e9 par un brainstorming pour d\u00e9finir notre produit final : les fonctionnalit\u00e9s de base obligatoires et les fonctionnalit\u00e9s optionnelles. <\/strong>Puis nous avons essay\u00e9 de parall\u00e9liser le maximum de t\u00e2ches. Par exemple, Dany et moi avons collect\u00e9 les images pendant que Paul-Emmanuel et Pauline codaient notre premier mod\u00e8le. <br><br><strong>Nous avons fait au minimum deux r\u00e9unions tous les jours : une le matin afin de nous r\u00e9partir les t\u00e2ches et une autre le soir pour faire le point sur l\u2019avancement du projet. <\/strong>Tr\u00e8s souvent, nous avons fait un point interm\u00e9diaire de mi-journ\u00e9e quand nous rencontrions un probl\u00e8me ponctuel. Nous avons utilis\u00e9 Trello comme outil de gestion de projet. Je garde de tr\u00e8s bons souvenirs de cette p\u00e9riode de projet. Bien s\u00fbr, il y a eu des moments de stress, surtout au d\u00e9but quand nous avons eu du mal \u00e0 collecter suffisamment d\u2019images. Nous avons beaucoup appris sur le deep learning et sur la mise en production. Le d\u00e9ploiement sur GCP (Google Cloud Platform) n\u2019a pas \u00e9t\u00e9 simple mais nous disposons maintenant des comp\u00e9tences n\u00e9cessaires pour l\u2019utiliser dans nos futurs projets. Nous avons aussi r\u00e9alis\u00e9 beaucoup de progr\u00e8s dans l\u2019utilisation de Github.&nbsp;<\/div><div><br><strong>Christophe : <\/strong>Les membres de mon \u00e9quipe \u00e9taient Claire, Matthieu et Jean-Philippe. Notre projet est un bon exemple de comment un projet peut avoir du succ\u00e8s, m\u00eame si tous les membres sont \u00e0 distance.<strong> Avec des zooms quotidiens et de la confiance, la distance ne nous a pas emp\u00each\u00e9e de bien travailler en \u00e9quipe.<br><\/strong><a href=\"https:\/\/res.cloudinary.com\/wagon\/image\/upload\/v1\/cache\/49448e459566c3d8b3b4561b841795f4.jpg\" data-trix-attachment=\"{&quot;contentType&quot;:&quot;image\/jpeg&quot;,&quot;filename&quot;:&quot;ablwsk-lewagon-3722_Christophe.jpg&quot;,&quot;filesize&quot;:83798,&quot;height&quot;:1000,&quot;href&quot;:&quot;https:\/\/res.cloudinary.com\/wagon\/image\/upload\/v1\/cache\/49448e459566c3d8b3b4561b841795f4.jpg&quot;,&quot;url&quot;:&quot;https:\/\/res.cloudinary.com\/wagon\/image\/upload\/v1\/cache\/49448e459566c3d8b3b4561b841795f4.jpg&quot;,&quot;width&quot;:1500}\" data-trix-content-type=\"image\/jpeg\" data-trix-attributes=\"{&quot;caption&quot;:&quot;Christophe et son projet : Djinews&quot;}\"><figure class=\"attachment attachment--preview attachment--jpg\"><img src=\"https:\/\/res.cloudinary.com\/wagon\/image\/upload\/v1\/cache\/49448e459566c3d8b3b4561b841795f4.jpg\" width=\"1500\" height=\"1000\"><figcaption class=\"attachment__caption attachment__caption--edited\">Christophe et son projet : Djinews<\/figcaption><\/figure><\/a><\/div><h3>Quels sont tes projets pour la suite ?<\/h3><div><strong>Christophe : <\/strong>Pour le moment, je suis Teacher Assistant au Wagon \u00e0 Paris et \u00e0 Londres et j'ai quelques petites missions en freelance avec une amie. En parall\u00e8le, je cherche un poste en tant que <a href=\"https:\/\/www.lewagon.com\/fr\/metiers-tech\/data-science\/data-analyst\">data analyst<\/a> et <a href=\"https:\/\/www.lewagon.com\/fr\/metiers-tech\/data-science\/data-scientist\">data scientist<\/a>. <strong>Sur le long terme, j\u2019aimerais bien utiliser la data pour rendre le monde plus \u00e9cologique.<\/strong><\/div><div><br><\/div><div><strong>Anne-Marie :<\/strong> Je compte continuer \u00e0 d\u00e9velopper mes comp\u00e9tences en data science. <strong>J\u2019ai pour projet de d\u00e9velopper un syst\u00e8me de comptage automatique d\u2019oiseaux \u00e0 la mangeoire. <\/strong><br><br>J\u2019esp\u00e8re aussi pouvoir utiliser mes nouvelles comp\u00e9tences de data analyst dans le domaine professionnel. Il me reste encore \u00e0 passer et r\u00e9ussir des concours pour devenir cadre administratif dans la fonction publique. <strong>J\u2019ai aussi un r\u00eave qui serait de cr\u00e9er une start-up d\u2019Etat dans les prochaines ann\u00e9es.&nbsp;<\/strong><\/div><div><br><br><\/div><h3>Un conseil \u00e0 donner aux personnes qui souhaitent suivre la formation ?<\/h3><div><strong>Anne-Marie :<\/strong> <strong>Je conseille aux nouveaux et nouvelles dans l\u2019aventure du <\/strong><a href=\"https:\/\/www.lewagon.com\/fr\/data-science-course\/full-time\"><strong>bootcamp Data Science<\/strong><\/a><strong> d\u2019apprendre tr\u00e8s vite \u00e0 lire les messages d\u2019erreur : <\/strong>rep\u00e9rer quelle ligne du programme d\u00e9clenche l\u2019erreur ainsi que le type d\u2019erreur.&nbsp; Cela permet de r\u00e9soudre une grande majorit\u00e9 des probl\u00e8mes. <br><br>Et pour celles et ceux qui sont d\u00e9j\u00e0 \u00e0 l\u2019aise avec les messages d\u2019erreur, se construire sa propre d\u00e9marche de r\u00e9solution de bugs. Par exemple, la mienne consistait \u00e0 commencer par v\u00e9rifier syst\u00e9matiquement les param\u00e8tres d\u2019une fonction et le type de valeur qu\u2019elle retourne. Ensuite, je passe \u00e0 la v\u00e9rification de structures de contr\u00f4le (boucle for, if\/else). <br><br><strong>Christophe : <\/strong>Just do it ! La data aujourd'hui et dans le futur va de plus en plus impacter chaque industrie et partie de notre vie.<strong> C'est le moment de se former aux <\/strong><a href=\"https:\/\/www.lewagon.com\/fr\/metiers-tech\/data-science\"><strong>m\u00e9tiers de la Data Science<\/strong><\/a><strong>, et \u00e0 mon avis, Le Wagon t'apprend toutes les choses essentielles dans un cadre enrichissant et stimulant.<br><br><\/strong>Pour revoir tous les projets de ce Demo Day, \u00e7a se passe ici  <a href=\"https:\/\/www.youtube.com\/watch?v=WIKLhyrjpis\"><figure class=\"attachment attachment--content\" data-trix-attachment=\"{&quot;content&quot;:&quot;<div class=&quot;video-wrapper&quot;><iframe width=&quot;100%&quot; height=&quot;388&quot; src=&quot;https:\/\/www.youtube.com\/embed\/WIKLhyrjpis&quot; frameborder=&quot;0&quot; allowfullscreen><\/iframe><\/div>&quot;}\" data-trix-content-type=\"undefined\"><div class=\"video-wrapper\"><iframe width=\"100%\" height=\"388\" src=\"https:\/\/www.youtube.com\/embed\/WIKLhyrjpis\" frameborder=\"0\" allowfullscreen=\"\"><\/iframe><\/div><figcaption class=\"attachment__caption\"><\/figcaption><\/figure><\/a><\/div>","_contenu_de_larticle":null,"titre_de_la_banniere":null,"_titre_de_la_banniere":null,"paragraphe_de_la_banniere":null,"_paragraphe_de_la_banniere":null,"intitule_du_bouton_de_la_banniere":null,"_intitule_du_bouton_de_la_banniere":null,"lien_du_bouton_de_la_banniere":null,"_lien_du_bouton_de_la_banniere":null,"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.7) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Djinews et Catch a Fish, 2 projets du bootcamp Data Science | Le Wagon<\/title>\n<meta name=\"description\" content=\"Apr\u00e8s 9 semaines intensives \u00e0 coder en Python et \u00e0 g\u00e9rer des donn\u00e9es. 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Anne-Marie et Christophe, alumni de la 2e promotion en Data Science du Wagon Paris, ont d\u00e9velopp\u00e9 les dix derniers jours du bootcamp deux projets : Catch a Fish et Djinews. 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