{"id":5266,"date":"2023-07-27T09:36:00","date_gmt":"2023-07-27T07:36:00","guid":{"rendered":"https:\/\/www.kode-solutions.net\/?p=5266"},"modified":"2024-09-19T09:36:55","modified_gmt":"2024-09-19T07:36:55","slug":"una-chiacchierata-conilaria-ceppa-data-scientist-in-kode-dal-2014","status":"publish","type":"post","link":"https:\/\/www.kode-solutions.net\/it\/una-chiacchierata-conilaria-ceppa-data-scientist-in-kode-dal-2014\/","title":{"rendered":"Una chiacchierata con:\nIlaria Ceppa, Data Scientist in Kode dal 2014"},"content":{"rendered":"\r\n\t
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Una chiacchierata con:\nIlaria Ceppa, Data Scientist in Kode dal 2014<\/h1>\n\n\t\t
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Author:<\/div>\r\n\t\t\t\t\t
Kode s.r.l.<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t
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27.07.2023<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t
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News<\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\r\n\t\t\t
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Il lavoro di Data Scientist \u00e8 sempre pi\u00f9 diffuso eppure \u00e8 ancora un mistero per molti. Nonostante esistano ormai tanti percorsi di formazione dedicati e non si faccia che parlare di dati e Intelligenza Artificiale, la concretezza di questo mestiere sfugge ancora a molti. Chiediamo aiuto alla nostra Data Scientist Ilaria Ceppa.<\/p><\/div>\r\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t<\/div>\n\n\n\n

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<\/br>Ilaria Ceppa, Data Scientist<\/figcaption><\/figure>\n<\/body><\/html><\/div>\n<\/div>\n\n\t\t\t\t\t<\/div>\r\n\t<\/div>\r\n\r\n\t\n\n\n
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In cosa consiste il tuo lavoro di Data Scientist?<\/strong><\/strong><\/h2>\n\n\n\n
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Principalmente il mio lavoro serve per trovare soluzioni a problemi concreti. Sembra molto generico ma in effetti la Data Science si applica a settori molto diversi e con progetti che hanno una grande variet\u00e0 di obiettivi. L\u2019unico punto in comune \u00e8 la richiesta che fa nascere il progetto: ovvero l’intenzione di sfruttare il potenziale di conoscenza che hanno i dati ormai presenti in grande quantit\u00e0 (e spesso con grande caos) in tutti i tipi di aziende.<\/p>\n\n\n\n

Il primo passo del mio lavoro \u00e8 sempre basato sullo studio: prima di tutto devo capire le necessit\u00e0 del cliente, devo capire (ove possibile) che dati ha a disposizione, per poi ragionare sulla soluzione ideale. Non lavoro mai da sola, ma preferisco sempre iniziare con una fase di studio autonomo, per poter scambiare con i colleghi portando gi\u00e0 qualche proposta e avere cognizione di causa del contesto e delle opzioni che la letteratura offre. <\/p>\n\n\n\n

E\u2019 a quel punto, per me, che il confronto con competenze diverse diventa una chiave di volta importante per poter risolvere con una soluzione ottimale il problema del cliente. <\/p>\n\n\n\n

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Quando si parla di data science si pensa subito a algoritmi, codici\u2026 il lavoro non consiste in questo?<\/strong><\/strong><\/h2>\n\n\n\n
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S\u00ec, ma raramente andiamo a lavorare subito sulla scrittura dei codici per la costruzione delle soluzioni, prima bisogna pensare al modo (o ai modi) in cui potremmo risolvere il problema che abbiamo di fronte. Certo quando abbiamo un sample del dataset \u00e8 pi\u00f9 facile che si ragioni e al contempo si testi la fattibilit\u00e0 e l\u2019efficacia della soluzione ipotizzata, ma \u00e8 raro. Peccato, perch\u00e9 spesso quando arriviamo all\u2019integrazione scopriamo che quello che i dati ti consentono di fare non sempre corrisponde a quello che si era pensato. In questo senso il nostro lavoro richiede una grande prontezza e capacit\u00e0 di adattamento alla realt\u00e0.<\/p>\n\n\n\n

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Questo significa non solo adattare le soluzioni che pensiamo di sviluppare al sistema usato in azienda o alle caratteristiche dei dati che abbiamo trovato. Significa anche far evolvere strumenti classici di Machine Learning, come una PCA, per rendere i suoi indicatori parlanti al team del cliente, e con questo intendo ingegneri, manager o direttori di impianto\u2026 persone con esigenze e riferimenti completamente diversi. Parte integrante della Data Science \u00e9 rendere le intuizioni vere e proprie informazioni che consentono l\u2019intervento operativo ad esempio su un macchinario durante un processo produttivo.<\/p>\n\n\n\n

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Puoi fare il modello di Intelligenza Artificiale pi\u00f9 bello e complesso del mondo ma se nessuno lo capisce, lascia il tempo che trova. Per me come rendere la data science utile nella vita reale \u00e8 parte integrante della data science: non \u00e8 sufficiente avere i dati a disposizione; non basta neppure avere delle analisi, se non capisci come interpretarli per prendere decisioni. Anche questo step \u00e8 Data Science.<\/p>\n<\/div>\n\n\n\n

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Parliamo di formazione, con che percorso sei arrivata qui?<\/strong><\/strong><\/h2>\n\n\n\n
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Mi sono orientata fin dagli studi in questa direzione: mi piaceva l\u2019analisi dei dati, la volevo fare, ma ero iscritta ad informatica e non c’era ancora un percorso formativo dedicato alla data science neppure fra le lauree specialistiche. C’erano solo degli esami che si avvicinavano al tema; ad esempio ho seguito Data Mining, NLP, Robotica\u2026 Ho seguito anche un esame di Intelligenza Artificiale che, per\u00f2, per quanto agli albori, mi ha introdotto ai concetti di base, facendomi nascere il desiderio di approfondire le tecniche pi\u00f9 avanzate che oggi utilizzo.<\/p>\n\n\n\n

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Alla specialistica mi sono imbarcata nel corso di Machine Learning (che si chiamava Apprendimento Automatico) e da l\u00ec ho studiato e approfondito il tema.<\/p>\n\n\n\n

In fin dei conti \u00e8 quello che faccio anche oggi: una parte fondamentale del mio lavoro \u00e8 continuare a studiare perch\u00e9, per quanto io abbia una personalit\u00e0 pragmatica (cerco approfondimenti quando mi si pone davanti un problema, non prima), ogni giorno esce una cosa nuova che pu\u00f2 essere quella giusta per il mio progetto. La curiosit\u00e0 e la voglia di trovare la soluzione pi\u00f9 performante \u00e8 una grande spinta ogni giorno. <\/p>\n\n\n\n

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<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Data Science is a team work of different expertise to find the meaningful solutions<\/p>\n","protected":false},"author":6,"featured_media":2773,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[52],"tags":[],"class_list":["post-5266","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"yoast_head":"\nIlaria Ceppa: Data Scientist for meaningful solutions - Kode<\/title>\n<meta name=\"description\" content=\"Mainly being a Data Scientis is about finding solutions to concrete problems. It includes the extraction of meaningful insights for the user. 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