{"id":6755,"date":"2025-04-25T17:06:12","date_gmt":"2025-04-25T15:06:12","guid":{"rendered":"https:\/\/francestat.com\/?page_id=6755"},"modified":"2026-03-10T11:46:03","modified_gmt":"2026-03-10T10:46:03","slug":"elearning-simca","status":"publish","type":"page","link":"https:\/\/francestat.com\/index.php\/elearning-simca\/","title":{"rendered":"eLearning SIMCA"},"content":{"rendered":"

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<\/div>

eLearning SIMCA<\/h2><\/div><\/div>[vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column width=\u00a0\u00bb1\/3″][vc_single_image image=\u00a0\u00bb3159″ alignment=\u00a0\u00bbcenter\u00a0\u00bb][vc_empty_space height=\u00a0\u00bb10px\u00a0\u00bb][vc_btn title=\u00a0\u00bbDemande d’informations\u00a0\u00bb align=\u00a0\u00bbcenter\u00a0\u00bb link=\u00a0\u00bburl:http%3A%2F%2Ffrancestat.com%2Findex.php%2Fcontact%2F|title:Demande%20d’informations\u00a0\u00bb][vc_empty_space height=\u00a0\u00bb10px\u00a0\u00bb][\/vc_column][vc_column width=\u00a0\u00bb2\/3″][vc_column_text css=\u00a0\u00bb.vc_custom_1745595789127{background-color: #f4f4f4 !important;}\u00a0\u00bb]Gr\u00e2ce aux techniques multivari\u00e9es les plus r\u00e9centes pr\u00e9sentes dans SIMCA, les participants apprendront \u00e0 interpr\u00e9ter des donn\u00e9es complexes rapidement et en toute confiance. D\u00e9couvrez les secrets de la visualisation de tableaux de donn\u00e9es et apprenez \u00e0 cr\u00e9er des mod\u00e8les pr\u00e9dictifs robustes qui transforment les donn\u00e9es en d\u00e9cisions. Le logiciel d’analyse multivari\u00e9e de donn\u00e9es SIMCA est utilis\u00e9 tout au long de la formation. SIMCA offre un moyen simple d’analyser vos donn\u00e9es, de visualiser les r\u00e9sultats, de comprendre les relations entre diff\u00e9rents ensembles de donn\u00e9es et de documenter les r\u00e9sultats \u00e0 l’aide de rapports personnalisables. L’analyse multivari\u00e9e des donn\u00e9es s’applique \u00e0 un certain nombre de questions essentielles telles que la pr\u00e9sentation des donn\u00e9es, la classification, la discrimination, la mod\u00e9lisation pr\u00e9dictive et l’analyse multi-blocs.\u00a0L’objectif de ce cours est de montrer comment convertir des tableaux de donn\u00e9es complexes en mod\u00e8les et graphiques robustes et fiables, faciles \u00e0 interpr\u00e9ter et \u00e0 visualiser.\u00a0\u00c0 l’issue de ce cours, les participants sauront\u00a0:<\/p>\n
    \n
  • Importer et contextualiser diff\u00e9rents types de donn\u00e9es<\/li>\n
  • Organiser, visualiser et pr\u00e9traiter diff\u00e9rents types de donn\u00e9es<\/li>\n
  • Comprendre les principes de base des m\u00e9thodes ACP, PLS et OPLS<\/li>\n
  • R\u00e9aliser une mod\u00e9lisation multivari\u00e9e sur leurs propres donn\u00e9es<\/li>\n
  • Utiliser les statistiques et les diagnostics de mod\u00e9lisation pour s\u2019assurer de la qualit\u00e9 du mod\u00e8le<\/li>\n
  • Identifier et interpr\u00e9ter les param\u00e8tres importants du mod\u00e8le<\/li>\n
  • Cr\u00e9er des mod\u00e8les pour le contr\u00f4le des processus en temps r\u00e9el<\/li>\n<\/ul>\n

    Le cours se termine par un test final qui peut \u00eatre pass\u00e9 plusieurs fois. Une attestation de r\u00e9ussite est d\u00e9livr\u00e9e.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][vc_column_text css=\u00a0\u00bb.vc_custom_1773139557739{background-color: #f4f4f4 !important;}\u00a0\u00bb]R\u00e9f\u00e9rence<\/strong> : EL-SIMCA
    \nDur\u00e9e<\/strong> : environ 12 heures par Internet
    \nLangue<\/strong> : anglais
    \nLicence du logiciel SIMCA<\/strong> : fournie pour 6 mois
    \nTarif<\/strong> : 500 \u20ac ht par inscrit
    \nPublic concern\u00e9<\/strong> : toute personne d\u00e9sirant comprendre, analyser et mod\u00e9liser de larges volumes de donn\u00e9es
    \nPopulation vis\u00e9e<\/strong> : techniciens, ing\u00e9nieurs, chercheurs
    \nPr\u00e9-requis<\/strong> : connaissances des statistiques de base
    \nM\u00e9thodes et moyens<\/strong> : explications th\u00e9oriques et applications pratiques
    \nM\u00e9thodes d’\u00e9valuation des acquis<\/strong> : exercices r\u00e9guliers en cours de formation[\/vc_column_text][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column width=\u00a0\u00bb1\/2″][vc_column_text css=\u00a0\u00bb.vc_custom_1745998283312{background-color: #f4f4f4 !important;}\u00a0\u00bb]Th\u00e8mes de la le\u00e7on 1<\/u><\/p>\n

    Pourquoi l’analyse de donn\u00e9es ?<\/p>\n

    Qu’est-ce que l’analyse de donn\u00e9es ?<\/p>\n

    Pourquoi l’analyse de donn\u00e9es multivari\u00e9e ?<\/p>\n

    Qu’est-ce que l’analyse de donn\u00e9es multivari\u00e9e (MVDA) ?<\/p>\n

    MVDA avec SIMCA<\/p>\n

    Quand l’analyse de donn\u00e9es multivari\u00e9e est-elle utilis\u00e9e ?<\/p>\n

    Th\u00e8mes de la le\u00e7on 2<\/u><\/p>\n

    Qu’est-ce qu’un mod\u00e8le ?<\/p>\n

    Blocs de donn\u00e9es, observations et variables<\/p>\n

    Contextualisation des donn\u00e9es dans SIMCA<\/p>\n

    Quelles sont les m\u00e9thodes couramment utilis\u00e9es pour analyser les donn\u00e9es ?<\/p>\n

    Repr\u00e9sentation g\u00e9om\u00e9trique de l’ACP<\/p>\n

    Cr\u00e9ation d’un mod\u00e8le d’ACP dans SIMCA<\/p>\n

    D\u00e9monstration de SIMCA \u00e0 l’aide du jeu de donn\u00e9es Healthcare<\/p>\n

    R\u00e9sum\u00e9 de la le\u00e7on<\/p>\n

    Th\u00e8mes de la le\u00e7on 3<\/u><\/p>\n

    Qu’est-ce qu’une valeur aberrante ?<\/p>\n

    Une valeur aberrante d\u00e9value-t-elle un mod\u00e8le ?<\/p>\n

    Outils d\u2019\u00e9valuation du mod\u00e8le pour l’ACP<\/p>\n

    Combien de composantes un mod\u00e8le d’ACP doit-il comporter ?<\/p>\n

    D\u00e9monstration de SIMCA \u00e0 l’aide du jeu de donn\u00e9es Foods<\/p>\n

    Pratique de l\u2019ACP<\/p>\n

    R\u00e9sum\u00e9 des param\u00e8tres et propri\u00e9t\u00e9s de l’ACP<\/p>\n

    Quelques cas d’utilisation<\/p>\n

    Th\u00e8mes de la le\u00e7on 4<\/u><\/p>\n

    Importance de la pr\u00e9paration des donn\u00e9es<\/p>\n

    Pr\u00e9traitement des donn\u00e9es<\/p>\n

    D\u00e9tection des donn\u00e9es anormales<\/p>\n

    Pr\u00e9sentation dans SIMCA<\/p>\n

    D\u00e9tection des valeurs aberrantes dans SIMCA[\/vc_column_text][\/vc_column][vc_column width=\u00a0\u00bb1\/2″][vc_column_text css=\u00a0\u00bb.vc_custom_1745998320676{background-color: #f4f4f4 !important;}\u00a0\u00bb]Th\u00e8mes de la le\u00e7on 5<\/u><\/p>\n

    Pr\u00e9sentation des trois cas d’utilisation<\/p>\n

    Application de l’ACP : D\u00e9tection de la contrefa\u00e7on<\/p>\n

    Application de l’ACP : Caract\u00e9risation des mati\u00e8res premi\u00e8res<\/p>\n

    Application de l’ACP : Surveillance d’un proc\u00e9d\u00e9 continu<\/p>\n

    Th\u00e8mes de la le\u00e7on 6<\/u><\/p>\n

    Qu’est-ce que la r\u00e9gression MVDA ?<\/p>\n

    Applications de la r\u00e9gression MVDA<\/p>\n

    Repr\u00e9sentation g\u00e9om\u00e9trique d’OPLS<\/p>\n

    Cr\u00e9ation d’un mod\u00e8le OPLS dans SIMCA<\/p>\n

    D\u00e9monstration de SIMCA \u00e0 l’aide des donn\u00e9es Healthcare<\/p>\n

    Diagnostic du mod\u00e8le de r\u00e9gression MVDA<\/p>\n

    R\u00e9sum\u00e9 de l\u2019OPLS<\/p>\n

    Th\u00e8mes de la le\u00e7on 7<\/u><\/p>\n

    Introduction<\/p>\n

    D\u00e9monstration de SIMCA \u00e0 l’aide des donn\u00e9es Healthcare<\/p>\n

    Cas d’utilisation BeerNIR<\/p>\n

    Relier les donn\u00e9es NIR \u00e0 la teneur en alcool<\/p>\n

    R\u00e9sum\u00e9 de la r\u00e9gression MVDA<\/p>\n

    Th\u00e8mes de la le\u00e7on 8<\/u><\/p>\n

    Introduction<\/p>\n

    Outils de documentation et de cr\u00e9ation de rapports disponibles dans SIMCA<\/p>\n

    Th\u00e8mes de la le\u00e7on 9<\/u><\/p>\n

    Introduction aux quatre cas d’utilisation<\/p>\n

    Application OPLS\u00a0: Analyse discriminante<\/p>\n

    Application OPLS\u00a0: Calibration multivari\u00e9e<\/p>\n

    Application OPLS\u00a0: Mod\u00e9lisation de proc\u00e9d\u00e9s par lots<\/p>\n

    Application OPLS\u00a0: Mod\u00e9lisation SIMCA pour le d\u00e9veloppement de cultures cellulaires Ambr<\/p>\n

    R\u00e9pertoire d’exercices pratiques SIMCA<\/u><\/p>\n

    Test final<\/u>[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"

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