How to use c2 method in wpt

Best JavaScript code snippet using wpt

educationalResources.ts

Source:educationalResources.ts

1import type { LocalizedString } from "../../i18n/Language";2/* spell-checker: disable */3import gameControllerImgUrl from "assets/img/gameController.png";4import grimoire01ImgUrl from "assets/img/grimoire01.png";5import grimoire02ImgUrl from "assets/img/grimoire02.png";6import grimoire03ImgUrl from "assets/img/grimoire03.png";7import grimoire04ImgUrl from "assets/img/grimoire04.png";8import grimoire05ImgUrl from "assets/img/grimoire05.png";9import grimoire06ImgUrl from "assets/img/grimoire06.png";10import grimoire07ImgUrl from "assets/img/grimoire07.png";11import grimoire08ImgUrl from "assets/img/grimoire08.png";12import grimoire09ImgUrl from "assets/img/grimoire09.png";13import grimoire10ImgUrl from "assets/img/grimoire10.png";14import grimoire11ImgUrl from "assets/img/grimoire11.png";15import grimoire12ImgUrl from "assets/img/grimoire12.png";16import grimoire13ImgUrl from "assets/img/grimoire13.png";17import neverendingImgUrl from "assets/img/neverending.png";18import grenouilleImgUrl from "assets/img/grenouille.jpg";19import coverImgUrl from "assets/img/cover.jpg";20import pollinisateurImgUrl from "assets/img/pollinisateur.jpg";21import crabeImgUrl from "assets/img/crabe.jpg";22import renardImgUrl from "assets/img/renard.jpg";23import odonateImgUrl from "assets/img/odonate.jpg";24import kubImgUrl from "assets/img/kub.png";25import elkImgUrl from "assets/img/elk.png";26import utilitrImgUrl from "assets/img/utilitr.png";27import rSvgUrl from "assets/svg/r.svg";28import jupyterImgUrl from "assets/img/jupyter.png";29import sparkImgUrl from "assets/img/spark.png";30import hiveSvgUrl from "assets/svg/hive.svg";31import redashSvgUrl from "assets/svg/redash.svg";32import pythonImgUrl from "assets/img/python.jpg";33import minioImgUrl from "assets/img/python.jpg";34import vaultSvgUrl from "assets/svg/vault.svg";35import gitImgUrl from "assets/img/git.png";36import bookImgUrl from "assets/img/book.png";37import btbImgUrl from "assets/img/btb.png";38export type EducationalResourceCategory =39 | "training courses with R"40 | "training courses with python"41 | "training courses in data science"42 | "best practices";43export type EducationalResourceTag = "discover" | "learn" | "consolidate" | "deepen";44export type EducationalResource = {45 name: LocalizedString;46 abstract: LocalizedString;47 /** List must contain at least one author */48 authors: [LocalizedString, ...LocalizedString[]];49 /** Epoch timestamp, get it for a specific date here: https://www.epochconverter.com */50 dateTime?: number;51 contributors?: LocalizedString[];52 /** Eg: video game, course, tutorial ... */53 types: LocalizedString[];54 tags: EducationalResourceTag[];55 category: EducationalResourceCategory;56 keywords?: string[];57 imageUrl?: string;58 /** Expressed in minutes */59 timeRequired?: number;60 /** At least one of the following must be specified */61 articleUrl?: LocalizedString;62 deploymentUrl?: LocalizedString;63};64export type EducationalResourceDirectory = {65 name: LocalizedString;66 abstract: LocalizedString;67 imageUrl?: string;68 parts: (EducationalResource | EducationalResourceDirectory)[];69};70export const educationalResources: (71 | EducationalResource72 | EducationalResourceDirectory73)[] = [74 {75 "name": {76 "fr": "FuncampR - Grimoire (FR)",77 "en": "FuncampR - Spellbook (EN - WIP)",78 },79 "abstract": {80 "fr": "Une aventure d'apprentissage vidéoludique pour le langage statistique R, à partager au sein du SSP (et du royaume de Statis). Pour en savoir plus, consulter le site https://funcamp.sspcloud.fr/",81 "en": "A serious game to learn statistical language R, dedicated to beginners - and gamers :-p. For more information, see https://funcamp.sspcloud.fr/",82 },83 "imageUrl": gameControllerImgUrl,84 "parts": [85 {86 "name": "icaRius",87 "abstract": {88 "fr": "La partie vidéoludique du FuncampR. Jeux de rôle inspiré d'un célèbre jeu vidéo des années 1990...",89 "en": "The video game part of FuncampR. A RPG inspired by a famous video game from the 1990s ...",90 },91 "authors": [92 "A. Degorre",93 {94 "fr": "communauté Solarus",95 "en": "Solarus Community",96 },97 ],98 "contributors": [99 {100 "fr": "Communauté FuncampR",101 "en": "FuncampR Community",102 },103 {104 "fr": "communauté Solarus",105 "en": "Solarus Community",106 },107 ],108 "types": [109 {110 "fr": "Jeu vidéo",111 "en": "Video Game",112 },113 ],114 "tags": ["discover", "learn"],115 "category": "training courses with R",116 "imageUrl": gameControllerImgUrl,117 "deploymentUrl":118 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/icarius?autoLaunch=true",119 },120 {121 "name": {122 "fr": "Grimoire IgoR - Chapitre 1",123 "en": "IgoR Spellbook - Chapter 1",124 },125 "abstract": {126 "fr": "Partie pédagogique du FuncampR. Chapitre 1 : la maison d’icaRius. Découverture du grimoire IgoR et de la langue des Runes",127 "en": "Educational part of FuncampR. Chapter 1: icaRius' home. Discovery of the IgoR Spellbook and the Runes' language",128 },129 "authors": [130 {131 "fr": "Communauté FuncampR",132 "en": "FuncampR Community",133 },134 ],135 "types": [136 {137 "fr": "Tutoriel R",138 "en": "R Tutorial",139 },140 ],141 "tags": ["discover"],142 "category": "training courses with R",143 "imageUrl": grimoire01ImgUrl,144 "deploymentUrl": {145 "fr": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapitre1»&onyxia.friendlyName=«Grimoire-Chap1»",146 "en": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapter1»&grimoire.quete=«Spellbook_IGoR»&onyxia.friendlyName=Spellbook-Chap1»",147 },148 },149 {150 "name": {151 "fr": "Grimoire IgoR - Chapitre 2",152 "en": "IgoR Spellbook - Chapter 2",153 },154 "abstract": {155 "fr": "Partie pédagogique du FuncampR. Chapitre 2 : la poule pondeuse. Dans le village de Kokoro, icaRius aide la fermière à recomposer le livre des pontes...",156 "en": "FuncampR educational part. Chapter 2: the laying hen. In the village of Kokoro, icaRius helps the farmer to recompose the egg-laying book...",157 },158 "authors": [159 {160 "fr": "Communauté FuncampR",161 "en": "FuncampR Community",162 },163 ],164 "types": [165 {166 "fr": "Tutoriel R",167 "en": "R Tutorial",168 },169 ],170 "tags": ["learn"],171 "category": "training courses with R",172 "imageUrl": grimoire02ImgUrl,173 "deploymentUrl": {174 "fr": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapitre2»&onyxia.friendlyName=«Grimoire-Chap2»",175 "en": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapter2»&grimoire.quete=«Spellbook_IGoR»&onyxia.friendlyName=«Spellbook-Chap2»",176 },177 },178 {179 "name": {180 "fr": "Grimoire IgoR - Chapitre 3",181 "en": "IgoR Spellbook - Chapter 3",182 },183 "abstract": {184 "fr": "Partie pédagogique du FuncampR. Chapitre 3 : le village de GrissGrass. Le chef du village demande à icaRius de trouver quelle est l’exploitation la plus productive en herbe de Mandragore.",185 "en": "FuncampR educational part. Chapter 3: the village of GrissGrass. The village chief asks icaRius to find the most productive Mandrake farm.",186 },187 "authors": [188 {189 "fr": "Communauté FuncampR",190 "en": "FuncampR Community",191 },192 ],193 "types": [194 {195 "fr": "Tutoriel R",196 "en": "R Tutorial",197 },198 ],199 "tags": ["learn"],200 "category": "training courses with R",201 "imageUrl": grimoire03ImgUrl,202 "deploymentUrl": {203 "fr": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapitre3»&onyxia.friendlyName=«Grimoire-Chap3»",204 "en": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapter3»&grimoire.quete=«Spellbook_IGoR»&onyxia.friendlyName=«Spellbook-Chap3»",205 },206 },207 {208 "name": {209 "fr": "Grimoire IgoR - Chapitre 4",210 "en": "IgoR Spellbook - Chapter 4",211 },212 "abstract": {213 "fr": "Partie pédagogique du FuncampR. Chapitre 4 : le secret de la culture de Mandragore. IcaRius doit retrouver la recette de la culture de la Mandragore.",214 "en": "FuncampR educational part. Chapter 4: The Secret of Mandrake Culture. IcaRius must find the recipe for the culture of the Mandrake.",215 },216 "authors": [217 {218 "fr": "Communauté FuncampR",219 "en": "FuncampR Community",220 },221 ],222 "types": [223 {224 "fr": "Tutoriel R",225 "en": "R Tutorial",226 },227 ],228 "tags": ["learn"],229 "category": "training courses with R",230 "imageUrl": grimoire04ImgUrl,231 "deploymentUrl": {232 "fr": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapitre4»&onyxia.friendlyName=«Grimoire-Chap4»",233 "en": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapter4»&grimoire.quete=«Spellbook_IGoR»&onyxia.friendlyName=«Spellbook-Chap4»",234 },235 },236 {237 "name": {238 "fr": "Grimoire IgoR - Chapitre 5",239 "en": "IgoR Spellbook - Chapter 5",240 },241 "abstract": {242 "fr": "Partie pédagogique du FuncampR. Chapitre 5 : le cuistot Batreb. Pour libérer Essespéus dans le château de Statis, icaRius doit d’abord obtenir la confiance du cuistot Batreb.",243 "en": "FuncampR educational part. Chapter 5: the cook Batreb. To free Essespeus in Statis Castle, icaRius must first gain the trust of cook Batreb.",244 },245 "authors": [246 {247 "fr": "Communauté FuncampR",248 "en": "FuncampR Community",249 },250 ],251 "types": [252 {253 "fr": "Tutoriel R",254 "en": "R Tutorial",255 },256 ],257 "tags": ["learn"],258 "category": "training courses with R",259 "imageUrl": grimoire05ImgUrl,260 "deploymentUrl": {261 "fr": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapitre5»&onyxia.friendlyName=«Grimoire-Chap5»",262 "en": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapter5»&grimoire.quete=«Spellbook_IGoR»&onyxia.friendlyName=«Spellbook-Chap5»",263 },264 },265 {266 "name": {267 "fr": "Grimoire IgoR - Chapitre 6",268 "en": "IgoR Spellbook - Chapter 6",269 },270 "abstract": {271 "fr": "Partie pédagogique du FuncampR. Chapitre 6 : la fake news. Essespéus et icaRius vont créer une Fake News pour tromper les armées de SaSSoS.",272 "en": "FuncampR educational part. Chapter 6: fake news. Essespéus and icaRius create a Fake News to deceive the armies of SaSSoS.",273 },274 "authors": [275 {276 "fr": "Communauté FuncampR",277 "en": "FuncampR Community",278 },279 ],280 "types": [281 {282 "fr": "Tutoriel R",283 "en": "R Tutorial",284 },285 ],286 "tags": ["learn"],287 "category": "training courses with R",288 "imageUrl": grimoire06ImgUrl,289 "deploymentUrl": {290 "fr": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapitre6»&onyxia.friendlyName=«Grimoire-Chap6»",291 "en": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapter6»&grimoire.quete=«Spellbook_IGoR»&onyxia.friendlyName=«Spellbook-Chap6»",292 },293 },294 {295 "name": {296 "fr": "Grimoire IgoR - Chapitre 7",297 "en": "IgoR Spellbook - Chapter 7",298 },299 "abstract": {300 "fr": "Partie pédagogique du FuncampR. Chapitre 7 (optionnel): le labyrinthe. Le Mage Delagarde propose à icaRius un défi pour obtenir les bonnes directions dans le labyrinthe.",301 "en": "FuncampR educational part. Chapter 7 (optional): the labyrinth. Mage Delagarde offers icaRius a challenge to get the right directions in the labyrinth.",302 },303 "authors": [304 {305 "fr": "Communauté FuncampR",306 "en": "FuncampR Community",307 },308 ],309 "types": [310 {311 "fr": "Tutoriel R",312 "en": "R Tutorial",313 },314 ],315 "tags": ["learn"],316 "category": "training courses with R",317 "imageUrl": grimoire07ImgUrl,318 "deploymentUrl": {319 "fr": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapitre7»&onyxia.friendlyName=«Grimoire-Chap7»",320 "en": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapter7»&grimoire.quete=«Spellbook_IGoR»&onyxia.friendlyName=«Spellbook-Chap7»",321 },322 },323 {324 "name": {325 "fr": "Grimoire IgoR - Chapitre 8",326 "en": "IgoR Spellbook - Chapter 8",327 },328 "abstract": {329 "fr": "Partie pédagogique du FuncampR. Chapitre 8: la plume d’IgoR. Pour soulever la pierre qui bloque le passage, icaRius doit apprendre de nouveaux sortilèges.",330 "en": "FuncampR educational part. Chapter 8: IgoR's Feather. To lift the stone blocking the passage, icaRius must learn new spells.",331 },332 "authors": [333 {334 "fr": "Communauté FuncampR",335 "en": "FuncampR Community",336 },337 ],338 "types": [339 {340 "fr": "Tutoriel R",341 "en": "R Tutorial",342 },343 ],344 "tags": ["learn"],345 "category": "training courses with R",346 "imageUrl": grimoire08ImgUrl,347 "deploymentUrl": {348 "fr": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapitre8»&onyxia.friendlyName=«Grimoire-Chap8»",349 "en": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapter8»&grimoire.quete=«Spellbook_IGoR»&onyxia.friendlyName=«Spellbook-Chap8»",350 },351 },352 {353 "name": {354 "fr": "Grimoire IgoR - Chapitre 9",355 "en": "IgoR Spellbook - Chapter 9",356 },357 "abstract": {358 "fr": "Partie pédagogique du FuncampR. Chapitre 9: le village de Sandia. Mam’Grouxi narre les innombrables naissances qu’elle a vu au fil des ans (des siècles?).",359 "en": "FuncampR educational part. Chapter 9: the village of Sandia. Mam’Grouxi recounts the countless births she has seen over the years.",360 },361 "authors": [362 {363 "fr": "Communauté FuncampR",364 "en": "FuncampR Community",365 },366 ],367 "types": [368 {369 "fr": "Tutoriel R",370 "en": "R Tutorial",371 },372 ],373 "tags": ["learn"],374 "category": "training courses with R",375 "imageUrl": grimoire09ImgUrl,376 "deploymentUrl": {377 "fr": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapitre9»&onyxia.friendlyName=«Grimoire-Chap9»",378 "en": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapter9»&grimoire.quete=«Spellbook_IGoR»&onyxia.friendlyName=«Spellbook-Chap9»",379 },380 },381 {382 "name": {383 "fr": "Grimoire IgoR - Chapitre 10",384 "en": "IgoR Spellbook - Chapter 10",385 },386 "abstract": {387 "fr": "Partie pédagogique du FuncampR. Chapitre 10: La porte de sortie - Save Me. Dans ses pérégrinations, icaRius se trouve pris au piège dans une salle de l'impossible",388 "en": "FuncampR educational part. Chapter 10: Exit Door - Save Me. In his wanderings, icaRius finds himself trapped in an Impossible Room.",389 },390 "authors": [391 {392 "fr": "Communauté FuncampR",393 "en": "FuncampR Community",394 },395 ],396 "types": [397 {398 "fr": "Tutoriel R",399 "en": "R Tutorial",400 },401 ],402 "tags": ["learn"],403 "category": "training courses with R",404 "imageUrl": grimoire10ImgUrl,405 "deploymentUrl": {406 "fr": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapitre10»&onyxia.friendlyName=«Grimoire-Chap10»",407 "en": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapter10»&grimoire.quete=«Spellbook_IGoR»&onyxia.friendlyName=«Spellbook-Chap10»",408 },409 },410 {411 "name": {412 "fr": "Grimoire IgoR - Chapitre 11",413 "en": "IgoR Spellbook - Chapter 11",414 },415 "abstract": {416 "fr": "Partie pédagogique du FuncampR. Chapitre 11: Codez-le une fois. L'automate TeoC enseigne à icaRius la Voie du Reproductible",417 "en": "FuncampR educational part. Chapter 11: Code It Once. The TeoC automaton teaches icaRius the Way of the Reproducible.",418 },419 "authors": [420 {421 "fr": "Communauté FuncampR",422 "en": "FuncampR Community",423 },424 ],425 "types": [426 {427 "fr": "Tutoriel R",428 "en": "R Tutorial",429 },430 ],431 "tags": ["learn"],432 "category": "training courses with R",433 "imageUrl": grimoire11ImgUrl,434 "deploymentUrl": {435 "fr": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapitre11»&onyxia.friendlyName=«Grimoire-Chap11»",436 "en": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapter11»&grimoire.quete=«Spellbook_IGoR»&onyxia.friendlyName=«Spellbook-Chap11»",437 },438 },439 {440 "name": {441 "fr": "Grimoire IgoR - Chapitre 12",442 "en": "IgoR Spellbook - Chapter 12",443 },444 "abstract": {445 "fr": "Partie pédagogique du FuncampR. Chapitre 12: De l’oxygène documentaire. Rencontre d'un drôle d’oiseau, FebeleR, féru de littérature statisienne et de grimoires...",446 "en": "FuncampR educational part. Chapter 12: Breathe and document. Meeting with a strange bird, FebeleR, fond of Statisian literature and grimoires ...",447 },448 "authors": [449 {450 "fr": "Communauté FuncampR",451 "en": "FuncampR Community",452 },453 ],454 "types": [455 {456 "fr": "Tutoriel R",457 "en": "R Tutorial",458 },459 ],460 "tags": ["learn"],461 "category": "training courses with R",462 "imageUrl": grimoire12ImgUrl,463 "deploymentUrl": {464 "fr": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapitre12»&onyxia.friendlyName=«Grimoire-Chap12»",465 "en": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapter12»&grimoire.quete=«Spellbook_IGoR»&onyxia.friendlyName=«Spellbook-Chap12»",466 },467 },468 {469 "name": {470 "fr": "Grimoire IgoR - Chapitre 13",471 "en": "IgoR Spellbook - Chapter 13",472 },473 "abstract": {474 "fr": "Partie pédagogique du FuncampR. Chapitre 13: l'histoire sans fin. La gueRnouille Asa apprend à icaRius à écrire lui-même la fin de l'histoire",475 "en": "FuncampR educational part. Chapter 13: The NeveRending Story. Asa fRog teaches icaRius to write himself the end of the story",476 },477 "authors": [478 {479 "fr": "Communauté FuncampR",480 "en": "FuncampR Community",481 },482 ],483 "types": [484 {485 "fr": "Tutoriel R",486 "en": "R Tutorial",487 },488 ],489 "tags": ["learn"],490 "category": "training courses with R",491 "imageUrl": grimoire13ImgUrl,492 "deploymentUrl": {493 "fr": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapitre13»&onyxia.friendlyName=«Grimoire-Chap13»",494 "en": "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/grimoire?autoLaunch=true&grimoire.chapitre=«chapter13»&grimoire.quete=«Spellbook_IGoR»&onyxia.friendlyName=«Spellbook-Chap13»",495 },496 },497 {498 "name": {499 "fr": "Grimoire - Neverending",500 "en": "Spellbook - Neverending",501 },502 "abstract": {503 "fr": "Partie pédagogique du FuncampR. Le parchemin pour écrire soi-même le chapitre 13 et la fin de l'histoire d'icaRius.",504 "en": "FuncampR educational part. The scroll on which icaRius writes chapter 13 and the end of the story.",505 },506 "authors": [507 {508 "fr": "Communauté FuncampR",509 "en": "FuncampR Community",510 },511 ],512 "types": [513 {514 "fr": "Tutoriel Rstudio",515 "en": "Rstudio Tutorial",516 },517 ],518 "tags": ["discover", "learn"],519 "category": "training courses with R",520 "imageUrl": neverendingImgUrl,521 "deploymentUrl":522 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-trainings/neverending?autoLaunch=true&onyxia.friendlyName=«neveRending»",523 },524 ],525 },526 {527 "name": "Parcours R",528 "abstract": "Parcours de formation à R du pôle ministériel MTES-MCTRCT",529 "imageUrl": rSvgUrl,530 "parts": [531 {532 "name": "1. Découvrir R et RStudio",533 "abstract":534 "Découvrir le fonctionnement de R, Aborder la dimension modulaire du logiciel, S’approprier l’interface graphique du logiciel, Être en capacité d’importer dans R un fichier CSV et de réaliser des calculs statistiques simples (somme, moyenne, table des fréquences)",535 "authors": [536 "Thierry Zorn",537 "Murielle Lethrosne",538 "Vivien Roussez",539 "Pascal Irz",540 ],541 "types": ["Tutoriel R"],542 "tags": ["discover"],543 "category": "training courses with R",544 "imageUrl": grenouilleImgUrl,545 "deploymentUrl":546 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%ABParcoursR_M1%C2%BB&service.image.custom.enabled=true&service.image.custom.version=%C2%ABghcr.io%2Fmtes-mct%2Fparcours_r_socle_introduction-4.0.4%C2%BB&security.allowlist.enabled=false&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2FMTES-MCT%2Fparcours-r%2Fmaster%2Finit_script_formation.sh%C2%BB",547 "articleUrl": "https://mtes-mct.github.io/parcours_r_socle_introduction/",548 },549 {550 "name": "2. Préparer ses données avec R et le Tidyverse",551 "abstract":552 "Être en capacité d’explorer les données, de les comprendre, de les structurer, de les croiser et les enrichir avec des données externes pour les préparer à des traitements statistiques. La préparation des données est une étape fondamentale pour faciliter la réalisation des analyses statistiques",553 "authors": ["Maël Theulière", "Bruno Terseur"],554 "types": ["Tutoriel R"],555 "tags": ["learn"],556 "category": "training courses with R",557 "imageUrl": coverImgUrl,558 "deploymentUrl":559 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%ABParcoursR_M2%C2%BB&service.image.custom.enabled=true&service.image.custom.version=%C2%ABghcr.io%2Fmtes-mct%2Fparcours_r_socle_preparation_des_donnees-4.0.4%C2%BB&security.allowlist.enabled=false&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2FMTES-MCT%2Fparcours-r%2Fmaster%2Finit_script_formation.sh%C2%BB",560 "articleUrl":561 "https://mtes-mct.github.io/parcours_r_socle_preparation_des_donnees/",562 },563 {564 "name": "3. Statistiques descriptives avec R",565 "abstract":566 "Rappels théoriques sur les méthodes usuelles de statistiques uni- et bi-variées, mise en œuvre avec R, interprétation",567 "authors": ["Solène Colin", "Vivien Roussez", "Pascal Irz"],568 "types": ["Tutoriel R"],569 "tags": ["learn"],570 "category": "training courses with R",571 "imageUrl": pollinisateurImgUrl,572 "deploymentUrl":573 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%ABParcoursR_M3%C2%BB&service.image.custom.enabled=true&service.image.custom.version=%C2%ABghcr.io%2Fmtes-mct%2Fparcours_r_module_statistiques_descriptives-4.0.4%C2%BB&security.allowlist.enabled=false&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2FMTES-MCT%2Fparcours-r%2Fmaster%2Finit_script_formation.sh%C2%BB",574 "articleUrl":575 "https://mtes-mct.github.io/parcours_r_module_statistiques_descriptives/",576 },577 {578 "name": "4. Analyse des données multi-dimensionnelles avec R",579 "abstract":580 "Méthodologie pour évaluer, en fonction des caractéristiques des données, la pertinence des méthodes usuelles d'analyse multidimensionnelle (ACP, AFC, ACM, CAH). Mise en œuvre avec le package factoMiner. Sorties graphiques avec le package factoextra. Interprétation",581 "authors": ["Vivien Roussez", "Pascal Irz"],582 "types": ["Tutoriel R"],583 "tags": ["consolidate"],584 "category": "training courses with R",585 "imageUrl": crabeImgUrl,586 "deploymentUrl":587 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%ABParcoursR_M4%C2%BB&service.image.custom.enabled=true&service.image.custom.version=%C2%ABghcr.io%2Fmtes-mct%2Fparcours_r_module_analyse_multi_dimensionnelles-4.0.4%C2%BB&security.allowlist.enabled=false&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2FMTES-MCT%2Fparcours-r%2Fmaster%2Finit_script_formation.sh%C2%BB",588 "articleUrl":589 "https://mtes-mct.github.io/parcours_r_module_analyse_multi_dimensionnelles/",590 },591 {592 "name": "5. Valoriser ses données avec R",593 "abstract":594 "Utiliser les outils R pour produire des graphiques avec le package ggplot2. Produire des cartes en utilisant ggplot2 et sf. Produire des tableaux interactifs. Rendre interactifs des graphiques et des cartes",595 "authors": ["Murielle Lethrosne", "Maël Theulière"],596 "types": ["Tutoriel R"],597 "tags": ["consolidate"],598 "category": "training courses with R",599 "imageUrl": renardImgUrl,600 "deploymentUrl":601 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%ABParcoursR_M5%C2%BB&service.image.custom.enabled=true&service.image.custom.version=%C2%ABghcr.io%2Fmtes-mct%2Fparcours_r_module_datavisualisation-4.0.4%C2%BB&security.allowlist.enabled=false&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2FMTES-MCT%2Fparcours-r%2Fmaster%2Finit_script_formation.sh%C2%BB",602 "articleUrl":603 "https://mtes-mct.github.io/parcours_r_module_datavisualisation/",604 },605 {606 "name": "7. Analyse spatiale",607 "abstract":608 "Introduction aux données spatiales, lire et écrire des données spatiales, manipuler des donnés spatiales, créer des cartes.",609 "authors": ["Murielle Lethrosne", "Maël Theulière"],610 "types": ["Tutoriel R"],611 "tags": ["consolidate"],612 "category": "training courses with R",613 "imageUrl": odonateImgUrl,614 "deploymentUrl":615 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%ABParcoursR_M7%C2%BB&service.image.custom.enabled=true&service.image.custom.version=%C2%ABghcr.io%2Fmtes-mct%2Fparcours_r_module_analyse_spatiale-4.0.4%C2%BB&security.allowlist.enabled=false&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2FMTES-MCT%2Fparcours-r%2Fmaster%2Finit_script_formation.sh%C2%BB",616 "articleUrl":617 "https://mtes-mct.github.io/parcours_r_module_analyse_spatiale/",618 },619 ],620 },621 {622 "name": "Initiation à Python",623 "abstract":624 "Cours introductif à Python : fondamentaux du langage et premières manipulations de données",625 "authors": ["inseefrlab"],626 "contributors": ["Romain Avouac"],627 "types": ["Notebook Python"],628 "tags": ["discover", "learn"],629 "category": "training courses with python",630 "imageUrl":631 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",632 "parts": [633 {634 "name": "Introduction",635 "abstract":636 'Introduction de l\'auto-formation "Initiation à Python" du SSP Cloud',637 "authors": ["inseefrlab"],638 "contributors": ["Romain Avouac"],639 "types": ["Notebook Python"],640 "tags": ["discover", "learn"],641 "category": "training courses with python",642 "imageUrl":643 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",644 "parts": [],645 },646 {647 "name": "Fondamentaux du langage",648 "abstract": "Présentation de la syntaxe et des objets de base en Python",649 "authors": ["inseefrlab"],650 "contributors": ["Romain Avouac"],651 "types": ["Notebook Python"],652 "tags": ["discover", "learn"],653 "category": "training courses with python",654 "imageUrl":655 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",656 "parts": [657 {658 "name": "Types de base et variables",659 "abstract":660 "Découverte des types de base (nombres et chaînes de caractères) et des variables.",661 "authors": ["inseefrlab"],662 "contributors": ["Romain Avouac"],663 "types": ["Notebook Python"],664 "tags": ["discover", "learn"],665 "category": "training courses with python",666 "imageUrl":667 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",668 "deploymentUrl":669 "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-initiation%C2%BB&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABfundamentals%20types-variables%C2%BB&security.allowlist.enabled=false",670 },671 {672 "name": "Structures de données 1 : listes et tuples",673 "abstract":674 "Découverte des structures de données séquentielles : listes et tuples.",675 "authors": ["inseefrlab"],676 "contributors": ["Romain Avouac"],677 "types": ["Notebook Python"],678 "tags": ["discover", "learn"],679 "category": "training courses with python",680 "imageUrl":681 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",682 "deploymentUrl":683 "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-initiation%C2%BB&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABfundamentals%20data-structures1%C2%BB&security.allowlist.enabled=false",684 },685 {686 "name": "Structures de données 2 : dictionnaires et sets",687 "abstract":688 "Découverte des structures de données non-ordonnées : dictionnaires et sets.",689 "authors": ["inseefrlab"],690 "contributors": ["Romain Avouac"],691 "types": ["Notebook Python"],692 "tags": ["discover", "learn"],693 "category": "training courses with python",694 "imageUrl":695 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",696 "deploymentUrl":697 "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-initiation%C2%BB&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABfundamentals%20data-structures2%C2%BB&security.allowlist.enabled=false",698 },699 {700 "name": "Tests",701 "abstract":702 "Découverte des tests et des structures conditionnelles, qui permettent à un programme de prendre des décisions de manière automatisée.",703 "authors": ["inseefrlab"],704 "contributors": ["Romain Avouac"],705 "types": ["Notebook Python"],706 "tags": ["discover", "learn"],707 "category": "training courses with python",708 "imageUrl":709 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",710 "deploymentUrl":711 "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-initiation%C2%BB&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABfundamentals%20tests%C2%BB&security.allowlist.enabled=false",712 },713 {714 "name": "Boucles",715 "abstract":716 "Automatisation d'opérations répétitives à l'aide des boucles for et des boucles while.",717 "authors": ["inseefrlab"],718 "contributors": ["Romain Avouac"],719 "types": ["Notebook Python"],720 "tags": ["discover", "learn"],721 "category": "training courses with python",722 "imageUrl":723 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",724 "deploymentUrl":725 "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-initiation%C2%BB&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABfundamentals%20loops%C2%BB&security.allowlist.enabled=false",726 },727 {728 "name": "Fonctions",729 "abstract":730 "Rendre son code mieux structuré et plus lisible avec les fonctions.",731 "authors": ["inseefrlab"],732 "contributors": ["Romain Avouac"],733 "types": ["Notebook Python"],734 "tags": ["discover", "learn"],735 "category": "training courses with python",736 "imageUrl":737 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",738 "deploymentUrl":739 "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-initiation%C2%BB&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABfundamentals%20functions%C2%BB&security.allowlist.enabled=false",740 },741 {742 "name": "Notions de programmation orientée objet",743 "abstract":744 "Un rapide tour dans le monde des objets, leurs attributs et leurs méthodes",745 "authors": ["inseefrlab"],746 "contributors": ["Romain Avouac"],747 "types": ["Notebook Python"],748 "tags": ["discover", "learn"],749 "category": "training courses with python",750 "imageUrl":751 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",752 "deploymentUrl":753 "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-initiation%C2%BB&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABfundamentals%20oop%C2%BB&security.allowlist.enabled=false",754 },755 ],756 },757 {758 "name": "Manipulation de données",759 "abstract": "Exploration, manipulation et visualisation de données",760 "authors": ["inseefrlab"],761 "contributors": ["Romain Avouac"],762 "types": ["Notebook Python"],763 "tags": ["discover", "learn"],764 "category": "training courses with python",765 "imageUrl":766 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",767 "parts": [768 {769 "name": "Manipulation de fichiers",770 "abstract":771 "Manipulation de fichiers externes : import de modules et lecture/écriture de fichiers texte.",772 "authors": ["inseefrlab"],773 "contributors": ["Romain Avouac"],774 "types": ["Notebook Python"],775 "tags": ["discover", "learn"],776 "category": "training courses with python",777 "imageUrl":778 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",779 "deploymentUrl":780 "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-initiation%C2%BB&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%20modules-files%C2%BB&security.allowlist.enabled=false",781 },782 {783 "name": "Travailler avec des fichiers CSV et JSON",784 "abstract":785 "Manipulation des fichiers CSV et JSON, deux types de fichiers très utilisés pour la diffusion de données.",786 "authors": ["inseefrlab"],787 "contributors": ["Romain Avouac"],788 "types": ["Notebook Python"],789 "tags": ["discover", "learn"],790 "category": "training courses with python",791 "imageUrl":792 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",793 "deploymentUrl":794 "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-initiation%C2%BB&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%20csv-json-files%C2%BB&security.allowlist.enabled=false",795 },796 {797 "name": "Calcul numérique avec NumPy",798 "abstract":799 "Manipulation des arrays et des fonctions de NumPy, la librairie de référence pour le calcul numérique.",800 "authors": ["inseefrlab"],801 "contributors": ["Romain Avouac"],802 "types": ["Notebook Python"],803 "tags": ["discover", "learn"],804 "category": "training courses with python",805 "imageUrl":806 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",807 "deploymentUrl":808 "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-initiation%C2%BB&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%20numpy%C2%BB&security.allowlist.enabled=false",809 },810 ],811 },812 {813 "name": "Mener un projet statistique avec Python",814 "abstract":815 "Bonnes pratiques pour mener des projets statistiques avec Python",816 "authors": ["inseefrlab"],817 "contributors": ["Romain Avouac"],818 "types": ["Notebook Python"],819 "tags": ["discover", "learn"],820 "category": "training courses with python",821 "imageUrl":822 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",823 "parts": [],824 },825 {826 "name": "Projet final",827 "abstract":828 'Projet final validant l\'auto-formation "Initiation à Python" du SSP Cloud',829 "authors": ["inseefrlab"],830 "contributors": ["Romain Avouac"],831 "types": ["Notebook Python"],832 "tags": ["discover", "learn"],833 "category": "training courses with python",834 "imageUrl":835 "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",836 "parts": [],837 },838 ],839 },840 {841 "name": "Python pour la data science",842 "abstract": "Approfondissement de Python pour la data science : manipulation de données, visualisation, modélisation, traitement du langage naturel",843 "authors": [844 "Lino Galiana"845 ],846 "types": [847 "Notebook Python"848 ],849 "tags": [850 "consolidate",851 "learn"852 ],853 "category": "training courses with python",854 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",855 "parts": [856 {857 "name": "Manipulation de données",858 "abstract": "Manipulation et récupération automatisée de données",859 "authors": [860 "Lino Galiana"861 ],862 "types": [863 "Notebook Python"864 ],865 "tags": [866 "consolidate",867 "learn"868 ],869 "category": "training courses with python",870 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",871 "parts": [872 {873 "name": "Retour sur numpy",874 "abstract": "`numpy` constitue la brique de base de l'écosystème de la _data-science_ en\n`Python`. Toutes les librairies de manipulation de données, de modélisation\net de visualisation reposent, de manière plus ou moins directe, sur `numpy`.\nIl est donc indispensable de revoir quelques notions sur ce package avant\nd'aller plus loin.\n",875 "authors": [876 "Lino Galiana"877 ],878 "types": [879 "Notebook Python"880 ],881 "tags": [882 "consolidate",883 "learn"884 ],885 "category": "training courses with python",886 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",887 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2001_numpy%C2%BB&security.allowlist.enabled=false"888 },889 {890 "name": "Introduction à pandas",891 "abstract": "`pandas` est l'élément central de l'écosystème `Python` pour la _data-science_. \nLe succès récent de `Python` dans l'analyse de données tient beaucoup à `pandas` qui a permis d'importer la\nlogique `SQL` dans le langage `Python`. `pandas` embarque énormément de\nfonctionalités qui permettent d'avoir des _pipelines_ efficaces pour\ntraiter des données de volumétrie moyenne (jusqu'à quelques Gigas). Au-delà\nde cette volumétrie, il faudra se tourner vers d'autres solutions\n(`PostgresQL`, `Dask`, `Spark`...).\n",892 "authors": [893 "Lino Galiana"894 ],895 "types": [896 "Notebook Python"897 ],898 "tags": [899 "consolidate",900 "learn"901 ],902 "category": "training courses with python",903 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",904 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2002a_pandas_tutorial%C2%BB&security.allowlist.enabled=false"905 },906 {907 "name": "Pratique de pandas: un exemple complet",908 "abstract": "Après avoir présenté la logique de `pandas` dans le chapitre précédent, \nce chapitre vise à illustrer les fonctionalités du package \nà partir de données d'émissions de gaz à effet de serre\nde l'[`Ademe`](https://data.ademe.fr/). \n",909 "authors": [910 "Lino Galiana"911 ],912 "types": [913 "Notebook Python"914 ],915 "tags": [916 "consolidate",917 "learn"918 ],919 "category": "training courses with python",920 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",921 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2002b_pandas_TP%C2%BB&security.allowlist.enabled=false"922 },923 {924 "name": "Données spatiales: découverte de geopandas",925 "abstract": "Les données géolocalisées se sont multipliées depuis quelques années, qu'il\ns'agisse de données open-data ou de traces numériques géolocalisées de\ntype _big-data_. Pour les données spatiales, le package `geopandas`\nétend les fonctionalités de l'écosystème `pandas` afin de permettre\nde manipuler des données géographiques complexe de manière simple.\n",926 "authors": [927 "Lino Galiana"928 ],929 "types": [930 "Notebook Python"931 ],932 "tags": [933 "consolidate",934 "learn"935 ],936 "category": "training courses with python",937 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",938 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2003_geopandas_tutorial%C2%BB&security.allowlist.enabled=false"939 },940 {941 "name": "Pratique de geopandas: données vélib",942 "abstract": "Ce chapitre illustre les fonctionalités de `geopandas` à partir des\ndécomptes de vélo fournis par la ville de Paris\nen [opendata](https://opendata.paris.fr/explore/dataset/comptage-velo-donnees-compteurs/map/?disjunctive.id_compteur&disjunctive.nom_compteur&disjunctive.id&disjunctive.name&basemap=jawg.dark&location=12,48.85855,2.33754).\nIl prolonge\nle chapitre précédent avec des données un petit peu plus complexes\nà manipuler.\n",943 "authors": [944 "Lino Galiana"945 ],946 "types": [947 "Notebook Python"948 ],949 "tags": [950 "consolidate",951 "learn"952 ],953 "category": "training courses with python",954 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",955 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2003_geopandas_TP%C2%BB&security.allowlist.enabled=false"956 },957 {958 "name": "Webscraping avec python",959 "abstract": "`Python` permet de facilement récupérer une page web pour en extraire des\ndonnées à restructurer. Le webscraping, que les Canadiens nomment\n_\"moissonnage du web\"_, est une manière de plus en plus utilisée de\nrécupérer une grande masse d'information en temps réel. \n",960 "authors": [961 "Lino Galiana"962 ],963 "types": [964 "Notebook Python"965 ],966 "tags": [967 "consolidate",968 "learn"969 ],970 "category": "training courses with python",971 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",972 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004a_webscraping_TP%C2%BB&security.allowlist.enabled=false"973 },974 {975 "name": "Maîtriser les expressions régulières",976 "abstract": "Les expressions régulières fournissent un cadre très pratique pour manipuler\nde manière flexible des données textuelles. Elles sont très utiles\nnotamment pour les tâches de traitement naturel du langage (__NLP__)\nou le nettoyage de données textuelles.\n",977 "authors": [978 "Lino Galiana"979 ],980 "types": [981 "Notebook Python"982 ],983 "tags": [984 "consolidate",985 "learn"986 ],987 "category": "training courses with python",988 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",989 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004b_regex_TP%C2%BB&security.allowlist.enabled=false"990 },991 {992 "name": "Requêter via des API avec Python",993 "abstract": "Les __API__ (_Application Programming Interface_) sont un mode d'accès aux\ndonnées en expansion. Grâce aux API, l'automatisation de scripts\nest facilitée puisqu'il n'est plus nécessaire de stocker un fichier,\net gérer ses différentes versions, mais uniquement de requêter une base\net laisser au producteur de données le soin de gérer les mises à jour de\nla base. \n",994 "authors": [995 "Lino Galiana"996 ],997 "types": [998 "Notebook Python"999 ],1000 "tags": [1001 "consolidate",1002 "learn"1003 ],1004 "category": "training courses with python",1005 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1006 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004c_API_TP%C2%BB&security.allowlist.enabled=false"1007 },1008 {1009 "name": "Exercices supplémentaires de webscraping",1010 "abstract": "Un exercice supplémentaire de _webscraping_,\noù l'on construit de manière automatique sa liste de courses à partir des données\nde [`Marmiton`](https://www.marmiton.org/).\n",1011 "authors": [1012 "Lino Galiana"1013 ],1014 "types": [1015 "Notebook Python"1016 ],1017 "tags": [1018 "consolidate",1019 "learn"1020 ],1021 "category": "training courses with python",1022 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1023 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2006a_exo_supp_webscraping%C2%BB&security.allowlist.enabled=false"1024 }1025 ]1026 },1027 {1028 "name": "Visualisation de données",1029 "abstract": "Graphiques, cartes, et visualisations interactives",1030 "authors": [1031 "Lino Galiana"1032 ],1033 "types": [1034 "Notebook Python"1035 ],1036 "tags": [1037 "consolidate",1038 "learn"1039 ],1040 "category": "training courses with python",1041 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1042 "parts": [1043 {1044 "name": "De beaux graphiques avec python: mise en pratique",1045 "abstract": "Une partie essentielle du travail du \n_data-scientist_ est d'être en mesure\nde synthétiser une information dans des\nreprésentations graphiques percutantes. Ce\nchapitre permet de découvrir\nles fonctionalités graphiques de `matplotlib`,\n`seaborn` et `plotly` pour représenter des statistiques\nsur les décomptes de vélo à Paris.\n",1046 "authors": [1047 "Lino Galiana"1048 ],1049 "types": [1050 "Notebook Python"1051 ],1052 "tags": [1053 "consolidate",1054 "learn"1055 ],1056 "category": "training courses with python",1057 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1058 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABvisualisation%20matplotlib%C2%BB&security.allowlist.enabled=false"1059 },1060 {1061 "name": "De belles cartes avec python: mise en pratique",1062 "abstract": "La cartographie est un excellent moyen de diffuser\nune connaissance, y compris à des publics peu\nfamiliers de la statistique. Ce chapitre permet\nde découvrir la manière dont on peut\nutiliser `Python` pour construire des \ncartes standards (avec `geopandas`) ou \nréactives (`folium`). Cela se fera\nà travers un exercice permettant\nde visualiser la fréquentation par les\nvélos des routes parisiennes.\n",1063 "authors": [1064 "Lino Galiana"1065 ],1066 "types": [1067 "Notebook Python"1068 ],1069 "tags": [1070 "consolidate",1071 "learn"1072 ],1073 "category": "training courses with python",1074 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1075 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABvisualisation%20maps%C2%BB&security.allowlist.enabled=false"1076 }1077 ]1078 },1079 {1080 "name": "Modélisation",1081 "abstract": "Preprocessing, apprentissage supervisé et non supervisé, évaluation de modèles",1082 "authors": [1083 "Lino Galiana"1084 ],1085 "types": [1086 "Notebook Python"1087 ],1088 "tags": [1089 "consolidate",1090 "learn"1091 ],1092 "category": "training courses with python",1093 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1094 "parts": [1095 {1096 "name": "Préparation des données pour construire un modèle",1097 "abstract": "Afin d'avoir des données cohérentes avec les hypothèses de modélisation,\nil est absolument fondamental de prendre le temps de\npréparer les données à fournir à un modèle. La qualité de la prédiction\ndépend fortement de ce travail préalable qu'on appelle _preprocessing_.\nBeaucoup de méthodes sont disponibles dans `scikit`, ce qui rend ce travail\nmoins fastidieux et plus fiable. \n",1098 "authors": [1099 "Lino Galiana"1100 ],1101 "types": [1102 "Notebook Python"1103 ],1104 "tags": [1105 "consolidate",1106 "learn"1107 ],1108 "category": "training courses with python",1109 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1110 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmodelisation%200_preprocessing%C2%BB&security.allowlist.enabled=false"1111 },1112 {1113 "name": "Evaluer la qualité d'un modèle",1114 "abstract": "Faire preuve de méthode pour évaluer la qualité d'un modèle \npermet de proposer des prédictions plus robustes, ayant\nde meilleures performances sur un nouveau jeu de données\n(prédictions _out-of-sample_). Décomposer\nl'échantillon initial en sous-échantillons d'entraînement\net de tests, faire de la validation croisée, utiliser\nles bonnes mesures de performances \npeut se faire, grâce à scikit, de manière relativement standardisée.\nCette démarche scientifique est essentielle pour assurer la confiance\ndans la qualité d'un modèle, ce qu'a illustré récemment\nun [cycle de séminaire de Princeton](https://reproducible.cs.princeton.edu/)\n",1115 "authors": [1116 "Lino Galiana"1117 ],1118 "types": [1119 "Notebook Python"1120 ],1121 "tags": [1122 "consolidate",1123 "learn"1124 ],1125 "category": "training courses with python",1126 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1127 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmodelisation%201_modelevaluation%C2%BB&security.allowlist.enabled=false"1128 },1129 {1130 "name": "Classification: premier modèle avec les SVM",1131 "abstract": "La classification permet d'attribuer une classe d'appartenance (_label_\ndans la terminologie du _machine learning_)\ndiscrète à des données à partir de certaines variables explicatives\n(_features_ dans la même terminologie).\nLes algorithmes de classification sont nombreux. L'un des plus intuitifs et\nles plus fréquemment rencontrés est le `SVM` (*support vector machine*).\nCe chapitre illustre les enjeux de la classification à partir de\nce modèle sur les données de vote aux élections présidentielles US de 2020.\n",1132 "authors": [1133 "Lino Galiana"1134 ],1135 "types": [1136 "Notebook Python"1137 ],1138 "tags": [1139 "consolidate",1140 "learn"1141 ],1142 "category": "training courses with python",1143 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1144 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmodelisation%202_SVM%C2%BB&security.allowlist.enabled=false"1145 },1146 {1147 "name": "Régression: une introduction",1148 "abstract": "La régression linéaire est la première modélisation statistique\nqu'on découvre dans un cursus quantitatif. Il s'agit en effet d'une\nméthode très intuitive et très riche. Le _Machine Learning_ permet de\nl'appréhender d'une autre manière que l'économétrie. Avec `scikit` et\n`statsmodels`, on dispose de tous les outils pour satisfaire à la fois\ndata scientists et économistes. \n",1149 "authors": [1150 "Lino Galiana"1151 ],1152 "types": [1153 "Notebook Python"1154 ],1155 "tags": [1156 "consolidate",1157 "learn"1158 ],1159 "category": "training courses with python",1160 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1161 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmodelisation%203_regression%C2%BB&security.allowlist.enabled=false"1162 },1163 {1164 "name": "Sélection de variables : une introduction",1165 "abstract": "L'accès à des bases de données de plus en plus riches permet\ndes modélisations de plus en plus raffinées. Cependant,\nles modèles parcimonieux sont généralement préférables\naux modèles extrêmement riches pour obtenir de bonnes\nperformances sur un nouveau jeu de données (prédictions\n_out-of-sample_). Les méthodes de sélection de variables,\nnotamment le [`LASSO`](https://fr.wikipedia.org/wiki/Lasso_(statistiques)),\npermettent de sélectionner le signal le plus\npertinent dilué au milieu du bruit lorsqu'on a beaucoup d'information à\ntraiter. \n",1166 "authors": [1167 "Lino Galiana"1168 ],1169 "types": [1170 "Notebook Python"1171 ],1172 "tags": [1173 "consolidate",1174 "learn"1175 ],1176 "category": "training courses with python",1177 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1178 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmodelisation%204_featureselection%C2%BB&security.allowlist.enabled=false"1179 },1180 {1181 "name": "Clustering",1182 "abstract": "Le _clustering_ consiste à répartir des observations dans des groupes,\ngénéralement non observés,\nen fonction de caractéristiques observables. Il s'agit d'une\napplication classique, en _machine learning_\nde méthodes non supervisées puisqu'on ne dispose généralement pas de l'information \nsur le groupe auquel apprartient réellement une observation. Les applications\nau monde réel sont nombreuses, notamment dans le domaine de la\nsegmentation tarifaire.\n",1183 "authors": [1184 "Lino Galiana"1185 ],1186 "types": [1187 "Notebook Python"1188 ],1189 "tags": [1190 "consolidate",1191 "learn"1192 ],1193 "category": "training courses with python",1194 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1195 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmodelisation%205_clustering%C2%BB&security.allowlist.enabled=false"1196 },1197 {1198 "name": "Premier pas vers l'industrialisation avec les pipelines scikit",1199 "abstract": "Les _pipelines_ `scikit` permettent d'intégrer de manière très flexible\nun ensemble d'opérations de pre-processing et d'entraînement de modèles\ndans une chaîne d'opérations. Il s'agit d'une approche particulièrement\nappropriée pour réduire la difficulté à changer d'algorithme ou pour\nfaciliter la ré-application d'un code à de nouvelles données\n",1200 "authors": [1201 "Lino Galiana"1202 ],1203 "types": [1204 "Notebook Python"1205 ],1206 "tags": [1207 "consolidate",1208 "learn"1209 ],1210 "category": "training courses with python",1211 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1212 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmodelisation%206_pipeline%C2%BB&security.allowlist.enabled=false"1213 }1214 ]1215 },1216 {1217 "name": "Traitement du langage naturel",1218 "abstract": "Analyse et modélisation des données textuelles",1219 "authors": [1220 "Lino Galiana"1221 ],1222 "types": [1223 "Notebook Python"1224 ],1225 "tags": [1226 "consolidate",1227 "learn"1228 ],1229 "category": "training courses with python",1230 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1231 "parts": [1232 {1233 "name": "Quelques éléments pour comprendre les enjeux",1234 "abstract": "Les corpus textuels étant des objets de très grande dimension\noù le ratio signal/bruit est faible, il est nécessaire de mettre\nen oeuvre une série d'étapes de nettoyage de texte. Ce chapitre va\nexplorer quelques méthodes classiques de nettoyage en s'appuyant\nsur le _Comte de Monte Cristo_ d'Alexandre Dumas. \n",1235 "authors": [1236 "Lino Galiana"1237 ],1238 "types": [1239 "Notebook Python"1240 ],1241 "tags": [1242 "consolidate",1243 "learn"1244 ],1245 "category": "training courses with python",1246 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1247 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABNLP%2001_intro%C2%BB&security.allowlist.enabled=false"1248 },1249 {1250 "name": "Nettoyer un texte: approche bag-of-words (exercices)",1251 "abstract": "Ce chapitre continue de présenter l'approche de __nettoyage de données__ \ndu `NLP` en s'appuyant sur le corpus de trois auteurs\nanglo-saxons : Mary Shelley, Edgar Allan Poe, H.P. Lovecraft.\nDans cette série d'exercice nous mettons en oeuvre de manière\nplus approfondie les différentes méthodes présentées\nprécedemment.\n",1252 "authors": [1253 "Lino Galiana"1254 ],1255 "types": [1256 "Notebook Python"1257 ],1258 "tags": [1259 "consolidate",1260 "learn"1261 ],1262 "category": "training courses with python",1263 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1264 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABNLP%2002_exoclean%C2%BB&security.allowlist.enabled=false"1265 },1266 {1267 "name": "Latent Dirichlet Allocation (LDA)",1268 "abstract": "Le modèle [Latent Dirichlet Allocation (LDA)](https://fr.wikipedia.org/wiki/Allocation_de_Dirichlet_latente)\nest un modèle probabiliste génératif qui permet\nde décrire des collections de documents de texte ou d’autres types de données discrètes.\nLa `LDA` fait\npartie d’une catégorie de modèles appelés _\"topic models\"_, qui cherchent à découvrir des structures\nthématiques cachées dans des vastes archives de documents.\n",1269 "authors": [1270 "Lino Galiana"1271 ],1272 "types": [1273 "Notebook Python"1274 ],1275 "tags": [1276 "consolidate",1277 "learn"1278 ],1279 "category": "training courses with python",1280 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1281 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABNLP%2003_lda%C2%BB&security.allowlist.enabled=false"1282 },1283 {1284 "name": "Méthodes de vectorisation : comptages et word embeddings",1285 "abstract": "Pour pouvoir utiliser des données textuelles dans des algorithmes\nde _machine learning_, il faut les vectoriser, c'est à dire transformer\nle texte en données numériques. Dans ce TP, nous allons comparer\ndifférentes méthodes de vectorisation, à travers une tâche de prédiction :\n_peut-on prédire un auteur littéraire à partir d'extraits de ses textes ?_\nParmi ces méthodes, on va notamment explorer le modèle `Word2Vec`, qui\npermet d'exploiter les structures latentes d'un texte en construisant\ndes _word embeddings_ (plongements de mots).\n",1286 "authors": [1287 "Lino Galiana"1288 ],1289 "types": [1290 "Notebook Python"1291 ],1292 "tags": [1293 "consolidate",1294 "learn"1295 ],1296 "category": "training courses with python",1297 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1298 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABNLP%2004_word2vec%C2%BB&security.allowlist.enabled=false"1299 },1300 {1301 "name": "Exercices supplémentaires",1302 "abstract": "Des exercices supplémentaires pour pratiquer les concepts du NLP\n",1303 "authors": [1304 "Lino Galiana"1305 ],1306 "types": [1307 "Notebook Python"1308 ],1309 "tags": [1310 "consolidate",1311 "learn"1312 ],1313 "category": "training courses with python",1314 "imageUrl": "https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",1315 "deploymentUrl": "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%ABpython-datascience%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABNLP%2005_exo_supp%C2%BB&security.allowlist.enabled=false"1316 }1317 ]1318 }1319 ]1320 },1321 {1322 "name": "Ateliers AMI IA",1323 "abstract":1324 "L'objectif de cet atelier est de vous faire découvrir le déroulement d'un projet de data science à travers trois cas d'études.",1325 "authors": ["LabIA-Etalab"],1326 "contributors": ["LabIA-Etalab"],1327 "types": ["Notebook Python"],1328 "tags": ["consolidate", "learn"],1329 "category": "training courses with python",1330 "imageUrl": pythonImgUrl,1331 "parts": [1332 {1333 "name": "Introduction",1334 "abstract": "Introduction aux outils de datascience",1335 "authors": ["LabIA-Etalab"],1336 "contributors": ["LabIA-Etalab"],1337 "types": ["Notebook Python"],1338 "tags": ["discover", "learn"],1339 "category": "training courses with python",1340 "imageUrl": pythonImgUrl,1341 "deploymentUrl":1342 "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&init.personalInit=«https%3A%2F%2Fgit.lab.sspcloud.fr%2Ftm8enk%2Finit%2F-%2Fraw%2Fmain%2Finit.sh»&init.personalInitArgs=«https%3A%2F%2Fgithub.com%2Fetalab-ia%2Fami-ia%20session2%2Foutils_du_datascientist.ipynb»&onyxia.friendlyName=«outils»&git.enabled=false&s3.enabled=false&discovery.hive=false&discovery.mlflow=false&vault.enabled=false",1343 },1344 {1345 "name": "Atelier 1",1346 "abstract": "Introduction à la data visualisation",1347 "authors": ["LabIA-Etalab"],1348 "contributors": ["LabIA-Etalab"],1349 "types": ["Notebook Python"],1350 "tags": ["discover", "learn"],1351 "category": "training courses with python",1352 "imageUrl": pythonImgUrl,1353 "deploymentUrl":1354 "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&init.personalInit=«https%3A%2F%2Fgit.lab.sspcloud.fr%2Ftm8enk%2Finit%2F-%2Fraw%2Fmain%2Finit.sh»&init.personalInitArgs=«https%3A%2F%2Fgithub.com%2Fetalab-ia%2Fami-ia%20session2%2Fintroduction_a_la_data_visualisation.ipynb»&onyxia.friendlyName=«data%20visualisation»&git.enabled=false&s3.enabled=false&discovery.hive=false&discovery.mlflow=false&vault.enabled=false",1355 },1356 {1357 "name": "Atelier 2",1358 "abstract": "Introduction au traîtement du langage naturel",1359 "authors": ["LabIA-Etalab"],1360 "contributors": ["LabIA-Etalab"],1361 "types": ["Notebook Python"],1362 "tags": ["discover", "learn"],1363 "category": "training courses with python",1364 "imageUrl": pythonImgUrl,1365 "deploymentUrl":1366 "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&init.personalInit=«https%3A%2F%2Fgit.lab.sspcloud.fr%2Ftm8enk%2Finit%2F-%2Fraw%2Fmain%2Finit.sh»&init.personalInitArgs=«https%3A%2F%2Fgithub.com%2Fetalab-ia%2Fami-ia%20session2%2Fintroduction_au_NLP.ipynb»&onyxia.friendlyName=«NLP»&git.enabled=false&s3.enabled=false&discovery.hive=false&discovery.mlflow=false&vault.enabled=false",1367 },1368 {1369 "name": "Atelier 3",1370 "abstract": "Introduction au machine learning",1371 "authors": ["LabIA-Etalab"],1372 "contributors": ["LabIA-Etalab"],1373 "types": ["Notebook Python"],1374 "tags": ["discover", "learn"],1375 "category": "training courses with python",1376 "imageUrl": pythonImgUrl,1377 "deploymentUrl":1378 "https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&init.personalInit=«https%3A%2F%2Fgit.lab.sspcloud.fr%2Ftm8enk%2Finit%2F-%2Fraw%2Fmain%2Finit.sh»&init.personalInitArgs=«https%3A%2F%2Fgithub.com%2Fetalab-ia%2Fami-ia%20session2%2Fintroduction_au_machine_learning.ipynb»&onyxia.friendlyName=«ML»&git.enabled=false&s3.enabled=false&discovery.hive=false&discovery.mlflow=false&vault.enabled=false",1379 },1380 ],1381 },1382 {1383 "name": "Initiation à Spark",1384 "abstract":1385 "Parcours de formation au calcul distribué avec Spark pour du traitement de données à grande échelle.",1386 "imageUrl": sparkImgUrl,1387 "parts": [1388 {1389 "name": "1. Introduction à Spark",1390 "abstract": "Bases d'architecture et premiers exemples",1391 "authors": ["Inseefrlab"],1392 "types": ["Notebook Python"],1393 "tags": ["discover", "learn"],1394 "category": "training courses in data science",1395 "imageUrl": sparkImgUrl,1396 "deploymentUrl":1397 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/jupyter?autoLaunch=true&spark.sparkui=true&kubernetes.role=«edit»&init.personalInit=«https://raw.githubusercontent.com/InseeFrLab/spark-formation/main/init-notebook.sh&vault.secret=«diffusion/spark-lab/1-introduction»&vault.directory=«tm8enk»&onyxia.friendlyName=«1_Intro_spark»",1398 },1399 {1400 "name": "2. Datalake S3",1401 "abstract":1402 "Faire du spark avec comme source et destination un système de fichier hadoop compatible : S3",1403 "authors": ["Inseefrlab"],1404 "types": ["Notebook Python"],1405 "tags": ["learn", "consolidate"],1406 "category": "training courses in data science",1407 "imageUrl": minioImgUrl,1408 "deploymentUrl":1409 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/jupyter?autoLaunch=true&spark.sparkui=true&kubernetes.role=«edit»&init.personalInit=«https://raw.githubusercontent.com/InseeFrLab/spark-formation/main/init-notebook.sh»&vault.secret=«diffusion/spark-lab/2-datalake»&vault.directory=«tm8enk&onyxia.friendlyName=«2_Datalake»",1410 },1411 {1412 "name": "2.2 Données chiffrées sur S3",1413 "abstract":1414 "Utiliser une donnée chiffrée sur S3, définir vos propres clés de chiffrement avec les clés de chiffrement fournies par Vault (SSE-C).",1415 "authors": ["Inseefrlab"],1416 "types": ["Notebook Python"],1417 "tags": ["learn", "consolidate"],1418 "category": "training courses in data science",1419 "imageUrl": vaultSvgUrl,1420 "deploymentUrl":1421 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/jupyter?autoLaunch=true&spark.sparkui=true&kubernetes.role=«edit»&init.personalInit=«https://raw.githubusercontent.com/InseeFrLab/spark-formation/main/init-notebook.sh»&vault.secret=«diffusion/spark-lab/2b-vault-s3-sseC»&vault.directory=«tm8enk»&onyxia.friendlyName=«2b_vault-s3-sseC»",1422 },1423 {1424 "name": "3. Spark et Kubernetes",1425 "abstract":1426 "Faire du Spark avec un cluster Spark sur Kubernetes et notion de lazy evaluation, transformation et action",1427 "authors": ["Inseefrlab"],1428 "types": ["Notebook Python"],1429 "tags": ["learn", "consolidate"],1430 "category": "training courses in data science",1431 "imageUrl": kubImgUrl,1432 "deploymentUrl":1433 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/jupyter?autoLaunch=true&spark.sparkui=true&kubernetes.role=«edit»&init.personalInit=«https://raw.githubusercontent.com/InseeFrLab/spark-formation/main/init-notebook.sh»&vault.secret=«diffusion/spark-lab/3-spark-kubernetes»&vault.directory=«tm8enk»&onyxia.friendlyName=«3_Spark_Kubernetes»",1434 },1435 {1436 "name": "3.2 Allocation Dynamique Kubernetes",1437 "abstract":1438 "Faire du spark avec un nombre d'executeur variable en fonction de votre besoin",1439 "authors": ["Inseefrlab"],1440 "types": ["Notebook Python"],1441 "tags": ["learn", "consolidate"],1442 "category": "training courses in data science",1443 "imageUrl": kubImgUrl,1444 "deploymentUrl":1445 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/jupyter?autoLaunch=true&spark.sparkui=true&kubernetes.role=«edit»&init.personalInit=«https://raw.githubusercontent.com/InseeFrLab/spark-formation/main/init-notebook.sh»&vault.secret=«diffusion/spark-lab/3b-dynamic-allocation»&vault.directory=«tm8enk»&onyxia.friendlyName=«3_Dynamic_allocation»",1446 },1447 {1448 "name": "4. Le format de données parquet",1449 "abstract": "Notion de partitions et format parquet",1450 "authors": ["Inseefrlab"],1451 "types": ["Notebook Python"],1452 "tags": ["learn", "consolidate"],1453 "category": "training courses in data science",1454 "imageUrl": sparkImgUrl,1455 "deploymentUrl":1456 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/jupyter?autoLaunch=true&spark.sparkui=true&kubernetes.role=«edit»&init.personalInit=«https://raw.githubusercontent.com/InseeFrLab/spark-formation/main/init-notebook.sh»&vault.secret=«diffusion/spark-lab/4-format-parquet»&vault.directory=«tm8enk»&onyxia.friendlyName=«4_format_parquet»",1457 },1458 {1459 "name": "5. Hive-metastore et metadonnées",1460 "abstract": "Metadonnées des tables d'un datalake",1461 "authors": ["Inseefrlab"],1462 "types": ["Notebook Python"],1463 "tags": ["learn", "consolidate"],1464 "category": "training courses in data science",1465 "imageUrl": hiveSvgUrl,1466 "deploymentUrl":1467 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/jupyter?autoLaunch=true&spark.sparkui=true&kubernetes.role=«edit»&init.personalInit=«https://raw.githubusercontent.com/InseeFrLab/spark-formation/main/init-notebook.sh»&vault.secret=«diffusion/spark-lab/5-hive-metastore»&vault.directory=«tm8enk»&onyxia.friendlyName=«5_hive_metastore»",1468 },1469 {1470 "name": "6. Spark-thrift et redash",1471 "abstract":1472 "Et si on faisait simplement du SQL en externalisant le driver spark et un outil de visualisation : redash",1473 "authors": ["Inseefrlab"],1474 "types": ["Notebook Python"],1475 "tags": ["learn", "consolidate"],1476 "category": "training courses in data science",1477 "imageUrl": redashSvgUrl,1478 "deploymentUrl":1479 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/jupyter?autoLaunch=true&init.personalInit=«https://raw.githubusercontent.com/InseeFrLab/spark-formation/main/init-notebook.sh»&vault.secret=«diffusion/spark-lab/6-spark-thrift-server»&vault.directory=«tm8enk»&onyxia.friendlyName=«6_spark_thrift_server»",1480 },1481 {1482 "name": "7. Spark streaming",1483 "abstract":1484 "Analyse de tweets. Notions de batch, micro-batch, streaming tout dépend de la vélocité recherchée.",1485 "authors": ["Inseefrlab"],1486 "types": ["Notebook Python"],1487 "tags": ["learn", "consolidate"],1488 "category": "training courses in data science",1489 "imageUrl": sparkImgUrl,1490 "deploymentUrl":1491 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/jupyter?autoLaunch=true&spark.sparkui=true&kubernetes.role=«edit»&init.personalInit=«https://raw.githubusercontent.com/InseeFrLab/spark-formation/main/init-notebook.sh»&vault.secret=«diffusion/spark-lab/7-spark-streaming»&vault.directory=«tm8enk»&onyxia.friendlyName=«7_spark_streaming»",1492 },1493 {1494 "name": "8. Spark Graphx",1495 "abstract": "Analyse de tweets avec l'utilisation de graph Spark",1496 "authors": ["Inseefrlab"],1497 "types": ["Notebook Python"],1498 "tags": ["learn", "consolidate"],1499 "category": "training courses in data science",1500 "imageUrl": sparkImgUrl,1501 "deploymentUrl":1502 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/jupyter?autoLaunch=true&spark.sparkui=true&kubernetes.role=«edit»&init.personalInit=«https://raw.githubusercontent.com/InseeFrLab/spark-formation/main/init-notebook.sh»&vault.secret=«diffusion/spark-lab/8-spark-graphx»&vault.directory=«tm8enk»&onyxia.friendlyName=«8_spark_graphx»",1503 },1504 {1505 "name": "9. Spark GPU",1506 "abstract": "A la découverte des GPUs avec spark",1507 "authors": ["Inseefrlab"],1508 "types": ["Notebook Python"],1509 "tags": ["learn", "consolidate"],1510 "category": "training courses in data science",1511 "imageUrl": sparkImgUrl,1512 "deploymentUrl":1513 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rapidsai?autoLaunch=true&spark.sparkui=true&kubernetes.role=«edit»&init.personalInit=«https://raw.githubusercontent.com/InseeFrLab/spark-formation/main/init-notebook.sh»&vault.secret=«diffusion/spark-lab/9-spark-gpu»&vault.directory=«tm8enk»&onyxia.friendlyName=«9_spark_gpu»",1514 },1515 {1516 "name": "10. SparkR",1517 "abstract": "R pour de gros volumes",1518 "authors": ["Inseefrlab"],1519 "types": ["Tutoriel R"],1520 "tags": ["consolidate"],1521 "category": "training courses in data science",1522 "imageUrl": sparkImgUrl,1523 "articleUrl":1524 "https://minio.lab.sspcloud.fr/projet-spark-lab/SparkR.html",1525 },1526 ],1527 },1528 {1529 "name": "Analyse Textuelle",1530 "abstract": "Initiation à l'analyse textuelle",1531 "authors": ["SSPLAB"],1532 "contributors": [1533 "Stéphanie Himpens, Milena Suarez Castillo, Stéphanie Combes, Benjamin Sakarovitch",1534 ],1535 "imageUrl": bookImgUrl,1536 "parts": [1537 {1538 "name": "Analyse d'article avec R",1539 "abstract":1540 "Analyser un corpus d'articles du journal Le Monde. Prétraiter (nettoyer, normaliser) les données afin de pouvoir en extraire de l'information, description du vocabulaire, identifier des thèmes ou la polarité du texte (négatif, positif)",1541 "authors": ["SSPLAB"],1542 "contributors": [1543 "Stéphanie Himpens, Milena Suarez Castillo, Stéphanie Combes, Benjamin Sakarovitch",1544 ],1545 "types": ["Tutoriel R"],1546 "tags": ["discover", "learn"],1547 "category": "training courses in data science",1548 "imageUrl": bookImgUrl,1549 "deploymentUrl":1550 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&init.personalInit=«https://git.lab.sspcloud.fr/ssplab/formation_text_mining_public/-/raw/master/installR.sh»&onyxia.friendlyName=«Text_Mining_R»",1551 },1552 {1553 "name": "Analyse d'article avec Python",1554 "abstract":1555 "Analyser un corpus d'articles du journal Le Monde. Prétraiter (nettoyer, normaliser) les données afin de pouvoir en extraire de l'information, description du vocabulaire, identifier des thèmes ou la polarité du texte (négatif, positif)",1556 "authors": ["SSPLAB"],1557 "contributors": [1558 "Stéphanie Himpens, Milena Suarez Castillo, Stéphanie Combes, Benjamin Sakarovitch",1559 ],1560 "types": ["Notebook Python"],1561 "tags": ["discover", "learn"],1562 "category": "training courses in data science",1563 "imageUrl": pythonImgUrl,1564 "deploymentUrl":1565 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/jupyter?autoLaunch=true&init.personalInit=«https://git.lab.sspcloud.fr/ssplab/formation_text_mining_public/-/raw/master/installPy.sh»&onyxia.friendlyName=«Text_Mining_Python»",1566 },1567 {1568 "name": "Appariemment flou avec Elastic Search",1569 "abstract":1570 "Calculer les calories d'une recette de cuisine en cherchant les produits dans Elastic",1571 "authors": ["SSPLAB"],1572 "contributors": [1573 "Stéphanie Himpens, Milena Suarez Castillo, Stéphanie Combes, Benjamin Sakarovitch",1574 ],1575 "types": ["Notebook Python"],1576 "tags": ["learn", "consolidate"],1577 "category": "training courses in data science",1578 "imageUrl": elkImgUrl,1579 "deploymentUrl":1580 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/jupyter?autoLaunch=true&onyxia.friendlyName=%C2%ABFuzzyMatchElasticInitiation%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fgit.lab.sspcloud.fr%2Fhby7ih%2Fhandsonelastic%2F-%2Fraw%2Fmaster%2Finit.sh%C2%BB&resources.requests.memory=%C2%AB10512Mi%C2%BB&security.allowlist.enabled=false&persistence.enabled=false&discovery.hive=false",1581 },1582 ],1583 },1584 {1585 "name": "Carroyage et lissage spatial sur R",1586 "abstract":1587 "Apprendre à carroyer les informations, réaliser des lissages spatiaux et calculer des indicateurs à partir des données carroyées sur R",1588 "imageUrl": btbImgUrl,1589 "parts": [1590 {1591 "name": "Introduction",1592 "abstract":1593 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"https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%AButilitr%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fminio.lab.sspcloud.fr%2Fpierrelamarche%2Futilitr%2Finit_utilitr.sh%C2%BB&service.image.version=%C2%ABinseefrlab%2Futilitr%3A0.7.0%C2%BB&vault.secret=%C2%AButilitr%2Futilitr%C2%BB",1898 "articleUrl": "https://www.book.utilitr.org/api.html",1899 },1900 {1901 "name": "Se connecter à une base de données",1902 "abstract":1903 "Accéder à des données stockées dans une base de données (sous forme Oracle, PostgreSQL, etc.).",1904 "authors": ["UtilitR"],1905 "types": ["Tutoriel R"],1906 "tags": ["learn", "consolidate"],1907 "category": "training courses with R",1908 "imageUrl": utilitrImgUrl,1909 "deploymentUrl":1910 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%AButilitr%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fminio.lab.sspcloud.fr%2Fpierrelamarche%2Futilitr%2Finit_utilitr.sh%C2%BB&service.image.version=%C2%ABinseefrlab%2Futilitr%3A0.7.0%C2%BB&vault.secret=%C2%AButilitr%2Futilitr%C2%BB",1911 "articleUrl": "https://www.book.utilitr.org/bdd.html",1912 },1913 ],1914 },1915 {1916 "name": "Manipuler des données avec R",1917 "abstract":1918 "Manipuler des données stucturées sous forme de data.frame.L’utilisateur souhaite manipuler des données stucturées sous forme de data.frame.",1919 "imageUrl": utilitrImgUrl,1920 "parts": [1921 {1922 "name": "Manipuler des données avec le tidyverse",1923 "abstract":1924 "Manipuler des données stucturées sous forme de data.frame avec tidyverse.",1925 "authors": ["UtilitR"],1926 "types": ["Tutoriel R"],1927 "tags": ["learn", "consolidate"],1928 "category": "training courses with R",1929 "imageUrl": utilitrImgUrl,1930 "deploymentUrl":1931 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%AButilitr%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fminio.lab.sspcloud.fr%2Fpierrelamarche%2Futilitr%2Finit_utilitr.sh%C2%BB&service.image.version=%C2%ABinseefrlab%2Futilitr%3A0.7.0%C2%BB&vault.secret=%C2%AButilitr%2Futilitr%C2%BB",1932 "articleUrl": "https://www.book.utilitr.org/tidyverse.html",1933 },1934 {1935 "name": "Manipuler des données avec data.table",1936 "abstract":1937 "Manipuler des données stucturées sous forme de data.frame avec data.table.",1938 "authors": ["UtilitR"],1939 "types": ["Tutoriel R"],1940 "tags": ["learn", "consolidate"],1941 "category": "training courses with R",1942 "imageUrl": utilitrImgUrl,1943 "deploymentUrl":1944 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%AButilitr%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fminio.lab.sspcloud.fr%2Fpierrelamarche%2Futilitr%2Finit_utilitr.sh%C2%BB&service.image.version=%C2%ABinseefrlab%2Futilitr%3A0.7.0%C2%BB&vault.secret=%C2%AButilitr%2Futilitr%C2%BB",1945 "articleUrl": "https://www.book.utilitr.org/datatable.html",1946 },1947 {1948 "name": "Joindre des tables de données",1949 "abstract":1950 "Apparier deux tables de données selon une ou plusieurs variables de jointure.",1951 "authors": ["UtilitR"],1952 "types": ["Tutoriel R"],1953 "tags": ["learn", "consolidate"],1954 "category": "training courses with R",1955 "imageUrl": utilitrImgUrl,1956 "deploymentUrl":1957 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%AButilitr%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fminio.lab.sspcloud.fr%2Fpierrelamarche%2Futilitr%2Finit_utilitr.sh%C2%BB&service.image.version=%C2%ABinseefrlab%2Futilitr%3A0.7.0%C2%BB&vault.secret=%C2%AButilitr%2Futilitr%C2%BB",1958 "articleUrl": "https://www.book.utilitr.org/jointures.html",1959 },1960 {1961 "name": "Manipuler des données textuelles",1962 "abstract":1963 "Manipuler du texte (repérer et extraire une chaîne de caractères, concaténer, remplacer une chaîne par une autre, modifier la casse, etc.).",1964 "authors": ["UtilitR"],1965 "types": ["Tutoriel R"],1966 "tags": ["learn", "consolidate"],1967 "category": "training courses with R",1968 "imageUrl": utilitrImgUrl,1969 "deploymentUrl":1970 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%AButilitr%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fminio.lab.sspcloud.fr%2Fpierrelamarche%2Futilitr%2Finit_utilitr.sh%C2%BB&service.image.version=%C2%ABinseefrlab%2Futilitr%3A0.7.0%C2%BB&vault.secret=%C2%AButilitr%2Futilitr%C2%BB",1971 "articleUrl": "https://www.book.utilitr.org/textdata.html",1972 },1973 {1974 "name": "Utiliser des données d’enquêtes",1975 "abstract":1976 "Exploiter des données d’enquête pour calculer des indicateurs.",1977 "authors": ["UtilitR"],1978 "types": ["Tutoriel R"],1979 "tags": ["learn", "consolidate"],1980 "category": "training courses with R",1981 "imageUrl": utilitrImgUrl,1982 "deploymentUrl":1983 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%AButilitr%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fminio.lab.sspcloud.fr%2Fpierrelamarche%2Futilitr%2Finit_utilitr.sh%C2%BB&service.image.version=%C2%ABinseefrlab%2Futilitr%3A0.7.0%C2%BB&vault.secret=%C2%AButilitr%2Futilitr%C2%BB",1984 "articleUrl": "https://www.book.utilitr.org/surveydata.html",1985 },1986 {1987 "name": "Manipuler des données spatiales",1988 "abstract":1989 "Traiter avec R des données spatiales (données géolocalisées, polygones, etc.).",1990 "authors": ["UtilitR"],1991 "types": ["Tutoriel R"],1992 "tags": ["learn", "consolidate"],1993 "category": "training courses with R",1994 "imageUrl": utilitrImgUrl,1995 "deploymentUrl":1996 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%AButilitr%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fminio.lab.sspcloud.fr%2Fpierrelamarche%2Futilitr%2Finit_utilitr.sh%C2%BB&service.image.version=%C2%ABinseefrlab%2Futilitr%3A0.7.0%C2%BB&vault.secret=%C2%AButilitr%2Futilitr%C2%BB",1997 "articleUrl": "https://www.book.utilitr.org/spatdata.html",1998 },1999 {2000 "name": "L’analyse de données (ACP, ACM, ACF…)",2001 "abstract":2002 "Méthodes classiques d’analyse de données (composantes principales, correspondances multiples, l’analyse factorielle des correspondance, etc.)",2003 "authors": ["UtilitR"],2004 "types": ["Tutoriel R"],2005 "tags": ["learn", "consolidate"],2006 "category": "training courses with R",2007 "imageUrl": utilitrImgUrl,2008 "deploymentUrl":2009 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%AButilitr%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fminio.lab.sspcloud.fr%2Fpierrelamarche%2Futilitr%2Finit_utilitr.sh%C2%BB&service.image.version=%C2%ABinseefrlab%2Futilitr%3A0.7.0%C2%BB&vault.secret=%C2%AButilitr%2Futilitr%C2%BB",2010 "articleUrl": "https://www.book.utilitr.org/acp.html",2011 },2012 ],2013 },2014 {2015 "name": "Superviser sa session R",2016 "abstract":2017 "Les bonnes pratiques pour faire un bon usage de ces ressources er ne pas gêner le travail des autres applications ou utilisateurs.",2018 "imageUrl": utilitrImgUrl,2019 "parts": [2020 {2021 "name": "Faire des graphiques avec ggplot2",2022 "abstract":2023 "Réaliser des graphiques (nuages de points, histogrammes, densité, etc.) et les personnaliser (légendes, titres, échelles, etc.).",2024 "authors": ["UtilitR"],2025 "types": ["Tutoriel R"],2026 "tags": ["learn", "consolidate"],2027 "category": "training courses with R",2028 "imageUrl": utilitrImgUrl,2029 "deploymentUrl":2030 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%AButilitr%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fminio.lab.sspcloud.fr%2Fpierrelamarche%2Futilitr%2Finit_utilitr.sh%C2%BB&service.image.version=%C2%ABinseefrlab%2Futilitr%3A0.7.0%C2%BB&vault.secret=%C2%AButilitr%2Futilitr%C2%BB",2031 "articleUrl": "https://www.book.utilitr.org/ggplot2.html",2032 },2033 {2034 "name": "Produire des documents avec R Markdown",2035 "abstract":2036 "Produire avec R des documents contenant à la fois du texte, des extraits de code R et les résultats de l’exécution de programmes.",2037 "authors": ["UtilitR"],2038 "types": ["Tutoriel R"],2039 "tags": ["learn", "consolidate"],2040 "category": "training courses with R",2041 "imageUrl": utilitrImgUrl,2042 "deploymentUrl":2043 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%AButilitr%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fminio.lab.sspcloud.fr%2Fpierrelamarche%2Futilitr%2Finit_utilitr.sh%C2%BB&service.image.version=%C2%ABinseefrlab%2Futilitr%3A0.7.0%C2%BB&vault.secret=%C2%AButilitr%2Futilitr%C2%BB",2044 "articleUrl": "https://www.book.utilitr.org/rmarkdown.html",2045 },2046 {2047 "name": "Rapports automatisés avec R Markdown",2048 "abstract":2049 "Produire un ou plusieurs rapports automatisés, reproductibles, faciles à actualiser en cas de modification des données et en faisant varier des paramètres.",2050 "authors": ["UtilitR"],2051 "types": ["Tutoriel R"],2052 "tags": ["learn", "consolidate"],2053 "category": "training courses with R",2054 "imageUrl": utilitrImgUrl,2055 "deploymentUrl":2056 "https://datalab.sspcloud.fr/launcher/inseefrlab-helm-charts-datascience/rstudio?autoLaunch=true&onyxia.friendlyName=%C2%AButilitr%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fminio.lab.sspcloud.fr%2Fpierrelamarche%2Futilitr%2Finit_utilitr.sh%C2%BB&service.image.version=%C2%ABinseefrlab%2Futilitr%3A0.7.0%C2%BB&vault.secret=%C2%AButilitr%2Futilitr%C2%BB",2057 "articleUrl": "https://www.book.utilitr.org/rapports-auto.html",2058 },2059 ],2060 },2061 ],2062 },2063 {2064 "name": "Travail collaboratif avec Git et RStudio",2065 "abstract":2066 "Formation au travail collaboratif et au contrôle de version à l'aide des logiciels Git et RStudio",2067 "imageUrl": gitImgUrl,2068 "parts": [2069 {2070 "name": "Introduction",2071 "abstract":2072 "Présentation générale de la formation et ressources additionnelles",2073 "authors": [2074 "Lino Galiana",2075 "Mathias André",2076 "Romain Lesur",2077 "Annie Moineau",2078 "Olivier Meslin",2079 ],2080 "types": ["Tutoriel R"],2081 "tags": ["learn"],2082 "category": "best practices",2083 "imageUrl": gitImgUrl,2084 "articleUrl":2085 "https://collaboratif-git-formation-insee.netlify.app/index.html",2086 },2087 {2088 "name": "Pourquoi utiliser le contrôle de version ?",2089 "abstract":2090 "Présentation des avantages individuels et collectifs à implémenter le contrôle de version pour les projets de code",2091 "authors": [2092 "Lino Galiana",2093 "Mathias André",2094 "Romain Lesur",2095 "Annie Moineau",2096 "Olivier Meslin",2097 ],2098 "types": ["Tutoriel R"],2099 "tags": ["learn"],2100 "category": "best practices",2101 "imageUrl": gitImgUrl,2102 "articleUrl":2103 "https://collaboratif-git-formation-insee.netlify.app/pourquoi-utiliser-la-gestion-de-version.html",2104 },2105 {2106 "name": "Utiliser Git avec RStudio",2107 "abstract": "Configurer un projet Git avec RStudio",2108 "authors": [2109 "Lino Galiana",2110 "Mathias André",2111 "Romain Lesur",2112 "Annie Moineau",2113 "Olivier Meslin",2114 ],2115 "types": ["Tutoriel R"],2116 "tags": ["learn"],2117 "category": "best practices",2118 "imageUrl": gitImgUrl,2119 "articleUrl":2120 "https://collaboratif-git-formation-insee.netlify.app/configurer-un-projet-git-avec-rstudio.html",2121 "deploymentUrl":2122 "https://datalab.sspcloud.fr/launcher/ide/rstudio?autoLaunch=true",2123 },2124 {2125 "name": "Des bases de Git",2126 "abstract": "Concepts essentiels de Git et exercices pratiques",2127 "authors": [2128 "Lino Galiana",2129 "Mathias André",2130 "Romain Lesur",2131 "Annie Moineau",2132 "Olivier Meslin",2133 ],2134 "types": ["Tutoriel R"],2135 "tags": ["learn"],2136 "category": "best practices",2137 "imageUrl": gitImgUrl,2138 "articleUrl":2139 "https://collaboratif-git-formation-insee.netlify.app/des-bases-de-git.html",2140 },2141 {2142 "name": "GitLab",2143 "abstract":2144 "Aperçu d'une des plateformes majeures de partage de code : GitLab",2145 "authors": [2146 "Lino Galiana",2147 "Mathias André",2148 "Romain Lesur",2149 "Annie Moineau",2150 "Olivier Meslin",2151 ],2152 "types": ["Tutoriel R"],2153 "tags": ["learn"],2154 "category": "best practices",2155 "imageUrl": gitImgUrl,2156 "articleUrl":2157 "https://collaboratif-git-formation-insee.netlify.app/gitlab.html",2158 },2159 {2160 "name": "Organiser le travail collaboratif",2161 "abstract":2162 "Collaborer efficacement à l'aide des branches et des merge requests",2163 "authors": [2164 "Lino Galiana",2165 "Mathias André",2166 "Romain Lesur",2167 "Annie Moineau",2168 "Olivier Meslin",2169 ],2170 "types": ["Tutoriel R"],2171 "tags": ["learn"],2172 "category": "best practices",2173 "imageUrl": gitImgUrl,2174 "articleUrl":2175 "https://collaboratif-git-formation-insee.netlify.app/orgagit.html",2176 },2177 ],2178 },...

hlsl.gatherRGBA.offset.dx10.frag

Source:hlsl.gatherRGBA.offset.dx10.frag

1SamplerState g_sSamp : register(s0);2uniform sampler2D g_sSamp2d;3Texture1D g_tTex1df4a : register(t1);4uniform Texture1D <float4> g_tTex1df4 : register(t0);5Texture1D <int4> g_tTex1di4;6Texture1D <uint4> g_tTex1du4;7Texture2D <float4> g_tTex2df4;8Texture2D <int4> g_tTex2di4;9Texture2D <uint4> g_tTex2du4;10Texture3D <float4> g_tTex3df4;11Texture3D <int4> g_tTex3di4;12Texture3D <uint4> g_tTex3du4;13TextureCube <float4> g_tTexcdf4;14TextureCube <int4> g_tTexcdi4;15TextureCube <uint4> g_tTexcdu4;16struct PS_OUTPUT17{18 float4 Color : SV_Target0;19 float Depth : SV_Depth;20};21uniform float c1;22uniform float2 c2;23uniform float3 c3;24uniform float4 c4;25uniform int o1;26uniform int2 o2;27uniform int3 o3;28uniform int4 o4;29PS_OUTPUT main()30{31 PS_OUTPUT psout;32 uint status;33 // no 1D gathers34 float4 txval001 = g_tTex2df4 . GatherRed(g_sSamp, c2, o2);35 int4 txval011 = g_tTex2di4 . GatherRed(g_sSamp, c2, o2);36 uint4 txval021 = g_tTex2du4 . GatherRed(g_sSamp, c2, o2);37 float4 txval004 = g_tTex2df4 . GatherRed(g_sSamp, c2, o2, o2, o2, o2);38 int4 txval014 = g_tTex2di4 . GatherRed(g_sSamp, c2, o2, o2, o2, o2);39 uint4 txval024 = g_tTex2du4 . GatherRed(g_sSamp, c2, o2, o2, o2, o2);40 41 // float4 txval00s = g_tTex2df4 . GatherRed(g_sSamp, c2, o2, status);42 // int4 txval01s = g_tTex2di4 . GatherRed(g_sSamp, c2, o2, status);43 // uint4 txval02s = g_tTex2du4 . GatherRed(g_sSamp, c2, o2, status);44 // float4 txval004s = g_tTex2df4 . GatherRed(g_sSamp, c2, o2, o2, o2, o2, status);45 // int4 txval014s = g_tTex2di4 . GatherRed(g_sSamp, c2, o2, o2, o2, o2, status);46 // uint4 txval024s = g_tTex2du4 . GatherRed(g_sSamp, c2, o2, o2, o2, o2, status);47 float4 txval101 = g_tTex2df4 . GatherGreen(g_sSamp, c2, o2);48 int4 txval111 = g_tTex2di4 . GatherGreen(g_sSamp, c2, o2);49 uint4 txval121 = g_tTex2du4 . GatherGreen(g_sSamp, c2, o2);50 float4 txval104 = g_tTex2df4 . GatherGreen(g_sSamp, c2, o2, o2, o2, o2);51 int4 txval114 = g_tTex2di4 . GatherGreen(g_sSamp, c2, o2, o2, o2, o2);52 uint4 txval124 = g_tTex2du4 . GatherGreen(g_sSamp, c2, o2, o2, o2, o2);53 // float4 txval10s = g_tTex2df4 . GatherGreen(g_sSamp, c2, o2, status);54 // int4 txval11s = g_tTex2di4 . GatherGreen(g_sSamp, c2, o2, status);55 // uint4 txval12s = g_tTex2du4 . GatherGreen(g_sSamp, c2, o2, status);56 // float4 txval104 = g_tTex2df4 . GatherGreen(g_sSamp, c2, o2, o2, o2, o2, status);57 // int4 txval114 = g_tTex2di4 . GatherGreen(g_sSamp, c2, o2, o2, o2, o2, status);58 // uint4 txval124 = g_tTex2du4 . GatherGreen(g_sSamp, c2, o2, o2, o2, o2, status);59 float4 txval201 = g_tTex2df4 . GatherBlue(g_sSamp, c2, o2);60 int4 txval211 = g_tTex2di4 . GatherBlue(g_sSamp, c2, o2);61 uint4 txval221 = g_tTex2du4 . GatherBlue(g_sSamp, c2, o2);62 float4 txval204 = g_tTex2df4 . GatherBlue(g_sSamp, c2, o2, o2, o2, o2);63 int4 txval214 = g_tTex2di4 . GatherBlue(g_sSamp, c2, o2, o2, o2, o2);64 uint4 txval224 = g_tTex2du4 . GatherBlue(g_sSamp, c2, o2, o2, o2, o2);65 // float4 txval204s = g_tTex2df4 . GatherBlue(g_sSamp, c2, o2, o2, o2, o2, status);66 // int4 txval214s = g_tTex2di4 . GatherBlue(g_sSamp, c2, o2, o2, o2, o2, status);67 // uint4 txval224s = g_tTex2du4 . GatherBlue(g_sSamp, c2, o2, o2, o2, o2, status);68 // float4 txval20s = g_tTex2df4 . GatherBlue(g_sSamp, c2, o2, status);69 // int4 txval21s = g_tTex2di4 . GatherBlue(g_sSamp, c2, o2, status);70 // uint4 txval22s = g_tTex2du4 . GatherBlue(g_sSamp, c2, o2, status);71 float4 txval301 = g_tTex2df4 . GatherAlpha(g_sSamp, c2, o2);72 int4 txval311 = g_tTex2di4 . GatherAlpha(g_sSamp, c2, o2);73 uint4 txval321 = g_tTex2du4 . GatherAlpha(g_sSamp, c2, o2);74 float4 txval304 = g_tTex2df4 . GatherAlpha(g_sSamp, c2, o2, o2, o2, o2);75 int4 txval314 = g_tTex2di4 . GatherAlpha(g_sSamp, c2, o2, o2, o2, o2);76 uint4 txval324 = g_tTex2du4 . GatherAlpha(g_sSamp, c2, o2, o2, o2, o2);77 // float4 txval304s = g_tTex2df4 . GatherAlpha(g_sSamp, c2, o2, o2, o2, o2, status);78 // int4 txval314s = g_tTex2di4 . GatherAlpha(g_sSamp, c2, o2, o2, o2, o2, status);79 // uint4 txval324s = g_tTex2du4 . GatherAlpha(g_sSamp, c2, o2, o2, o2, o2, status);80 // float4 txval30s = g_tTex2df4 . GatherAlpha(g_sSamp, c2, o2, status);81 // int4 txval31s = g_tTex2di4 . GatherAlpha(g_sSamp, c2, o2, status);82 // uint4 txval32s = g_tTex2du4 . GatherAlpha(g_sSamp, c2, o2, status);83 // no 3D gathers with offset84 psout.Color = 1.0;85 psout.Depth = 1.0;86 return psout;...

Using AI Code Generation

1var wpt = require('webpagetest');2var api = new wpt('www.webpagetest.org');3 if (err) return console.error(err);4 console.log('Test submitted to WebPagetest! Check results at %s', data.data.userUrl);5});6var wpt = require('webpagetest');7var api = new wpt('www.webpagetest.org');8}, function(err, data) {9 if (err) return console.error(err);10 console.log('Test submitted to WebPagetest! Check results at %s', data.data.userUrl);11});12var wpt = require('webpagetest');13var api = new wpt('www.webpagetest.org');14}, function(err, data) {15 if (err) return console.error(err);16 console.log('Test submitted to WebPagetest! Check results at %s', data.data.userUrl);17});18var wpt = require('webpagetest');19var api = new wpt('www.webpagetest.org');20}, function(err, data) {21 if (err) return console.error(err);22 console.log('Test submitted to WebPagetest! Check results at %s', data.data.userUrl);23});24var wpt = require('webpagetest');25var api = new wpt('www.webpagetest.org');

Using AI Code Generation

1var wpt = require('webpagetest');2var wpt = new WebPageTest('www.webpagetest.org');3var options = {4};5wpt.runTest(url, options, function(err, data) {6 if (err) return console.error(err);7 console.log('Test Results: %j', data);8});9var wpt = require('webpagetest');10var wpt = new WebPageTest('www.webpagetest.org');11var options = {12};13wpt.runTest(url, options, function(err, data) {14 if (err) return console.error(err);15 console.log('Test Results: %j', data);16});

Using AI Code Generation

1var wpt = require('wpt');2var wpt = new WebPageTest('www.webpagetest.org');3wpt.getLocations(function(err, data) {4 console.log(data);5});6(function (exports, require, module, __filename, __dirname) { var request = req7 at Object.<anonymous> (C:\Users\user\Documents\GitHub\wpt\node_modules\wpt\l8 at Module._compile (module.js:460:26)9 at Object.Module._extensions..js (module.js:478:10)10 at Module.load (module.js:355:32)11 at Function.Module._load (module.js:310:12)12 at Module.require (module.js:365:17)13 at require (module.js:384:17)14 at Object.<anonymous> (C:\Users\user\Documents\GitHub\wpt\test.js:2:12)15 at Module._compile (module.js:460:26)16 at Object.Module._extensions..js (module.js:478:10)

Using AI Code Generation

1var wpt = require('./wpt');2var wpt = new wpt('API_KEY');3 if (err) {4 console.log('error: ' + err);5 } else {6 console.log(data);7 }8});9var wpt = require('./wpt');10var wpt = new wpt('API_KEY');11 if (err) {12 console.log('error: ' + err);13 } else {14 console.log(data);15 }16});17var wpt = require('./wpt');18var wpt = new wpt('API_KEY');19 if (err) {20 console.log('error: ' + err);21 } else {22 console.log(data);23 }24});25var wpt = require('./wpt');26var wpt = new wpt('API_KEY');27 if (err) {28 console.log('error: ' + err);29 } else {30 console.log(data);31 }32});33var wpt = require('./wpt');34var wpt = new wpt('API_KEY');35 if (err) {36 console.log('error: ' + err);37 } else {38 console.log(data);39 }40});41var wpt = require('./wpt');42var wpt = new wpt('API_KEY');43 if (err) {44 console.log('error: ' + err);45 } else {46 console.log(data);47 }48});49var wpt = require('./wpt');50var wpt = new wpt('API_KEY');

Using AI Code Generation

1var wpt = require('webpagetest');2var server = 'www.webpagetest.org';3var wpt = new WebPageTest(server);4var options = {5};6wpt.runTest(url, options, function(err, data) {7 if (err) return console.error(err);8 console.log('Test submitted to WebPagetest for %s', url);9 console.log('View your test at: %sresult/%s/', server, data.data.testId);10});

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.

LambdaTest Learning Hubs:

• JUnit Tutorial
• TestNG Tutorial
• Webdriver Tutorial
• WebDriverIO Tutorial
• Protractor Tutorial
• Selenium 4 Tutorial
• Jenkins Tutorial
• NUnit Tutorial
• Jest Tutorial
• Playwright Tutorial
• Cypress Tutorial
• PyTest Tutorial

YouTube

You could also refer to video tutorials over LambdaTest YouTube channel to get step by step demonstration from industry experts.

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