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# Definizione di Data Science
| ![ Sketchnote di [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/01-Definitions.png) |

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# Compito: Scenari di Data Science
In questo primo compito, ti chiediamo di riflettere su alcuni processi o problemi reali in diversi ambiti e su come puoi migliorarli utilizzando il processo di Data Science. Pensa ai seguenti punti:

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# Assegnazione: Scenari di Data Science
In questo primo compito, ti chiediamo di riflettere su un processo o problema reale in diversi ambiti e su come puoi migliorarlo utilizzando il processo di Data Science. Pensa ai seguenti punti:

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# Introduzione all'etica dei dati
|![ Sketchnote di [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/02-Ethics.png)|

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## Scrivi un Caso di Studio sull'Etica dei Dati
## Istruzioni

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# Definizione dei Dati
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# Classificazione dei Dataset
## Istruzioni

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# Una Breve Introduzione alla Statistica e alla Probabilità
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@ -64,7 +55,7 @@ Per aiutarci a comprendere la distribuzione dei dati, è utile parlare di **quar
Graficamente possiamo rappresentare la relazione tra mediana e quartili in un diagramma chiamato **box plot**:
<img src="images/boxplot_explanation.png" alt="Spiegazione del Box Plot" width="50%">
<img src="../../../../translated_images/it/boxplot_explanation.4039b7de08780fd4.webp" alt="Spiegazione del Box Plot" width="50%">
Qui calcoliamo anche l'**intervallo interquartile** IQR=Q3-Q1 e i cosiddetti **outlier** - valori che si trovano al di fuori dei limiti [Q1-1.5*IQR,Q3+1.5*IQR].

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# Piccolo Studio sul Diabete
In questo compito, lavoreremo con un piccolo dataset di pazienti diabetici preso da [qui](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).

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# Introduzione alla Scienza dei Dati
![dati in azione](../../../translated_images/it/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg)

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# Lavorare con i dati: database relazionali
|![ Sketchnote di [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/05-RelationalData.png)|

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# Visualizzazione dei dati sugli aeroporti
Ti è stato fornito un [database](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db) basato su [SQLite](https://sqlite.org/index.html) che contiene informazioni sugli aeroporti. Lo schema è mostrato di seguito. Utilizzerai l'[estensione SQLite](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) in [Visual Studio Code](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum) per visualizzare informazioni sugli aeroporti di diverse città.

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# Lavorare con i dati: Dati non relazionali
|![ Sketchnote di [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/06-NoSQL.png)|

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# Profitti della Soda
## Istruzioni

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# Lavorare con i Dati: Python e la Libreria Pandas
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# Compito per l'elaborazione dei dati in Python
In questo compito, ti chiederemo di approfondire il codice che abbiamo iniziato a sviluppare nelle nostre sfide. Il compito è suddiviso in due parti:

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# Lavorare con i dati: Preparazione dei dati
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# Valutazione dei dati di un modulo
Un cliente ha testato un [piccolo modulo](../../../../2-Working-With-Data/08-data-preparation/index.html) per raccogliere alcuni dati di base sulla propria clientela. Ha portato i risultati a te per validare i dati raccolti. Puoi aprire la pagina `index.html` nel browser per dare un'occhiata al modulo.

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# Lavorare con i Dati
![data love](../../../translated_images/it/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg)

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# Visualizzare le quantità
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# Linee, Dispersioni e Barre
## Istruzioni

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# Visualizzare le Distribuzioni
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# Applica le tue competenze
## Istruzioni

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# Visualizzare le Proporzioni
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# Provalo in Excel
## Istruzioni

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# Visualizzare le Relazioni: Tutto sul Miele 🍯
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# Esplora l'alveare
## Istruzioni

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# Creare Visualizzazioni Significative
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# Crea la tua visualizzazione personalizzata
## Istruzioni

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# Progetto di visualizzazione dati Dangerous Liaisons
Per iniziare, assicurati di avere NPM e Node installati e funzionanti sulla tua macchina. Installa le dipendenze (npm install) e poi esegui il progetto localmente (npm run serve):

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# Progetto di visualizzazione dati Dangerous Liaisons
Per iniziare, assicurati di avere NPM e Node installati e funzionanti sulla tua macchina. Installa le dipendenze (npm install) e poi esegui il progetto localmente (npm run serve):

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# Visualizzare le Quantità
|![ Sketchnote di [(@sketchthedocs)](https://sketchthedocs.dev) ](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/09-Visualizing-Quantities.png)|
|:---:|

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# Linee, Dispersioni e Barre
## Istruzioni

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# Visualizzare le distribuzioni
|![ Sketchnote di [(@sketchthedocs)](https://sketchthedocs.dev) ](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/10-Visualizing-Distributions.png)|

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# Applica le tue competenze
## Istruzioni

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# Visualizzare le Proporzioni
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# Visualizzare le Relazioni: Tutto sul Miele 🍯
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# Creare Visualizzazioni Significative
|![ Sketchnote di [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/13-MeaningfulViz.png)|

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# Visualizzazioni
![un'ape su un fiore di lavanda](../../../translated_images/it/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg)

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# Introduzione al Ciclo di Vita della Data Science
|![ Sketchnote di [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/14-DataScience-Lifecycle.png)|

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# Valutare un Dataset
Un cliente si è rivolto al tuo team per ricevere aiuto nell'analisi delle abitudini stagionali di spesa dei clienti dei taxi a New York City.

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# Il ciclo di vita della Data Science: Analisi
|![ Sketchnote di [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/15-Analyzing.png)|

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# Esplorare per trovare risposte
Questa è una continuazione del [compito](../14-Introduction/assignment.md) della lezione precedente, in cui abbiamo dato un'occhiata veloce al set di dati. Ora esamineremo i dati in modo più approfondito.

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# Il Ciclo di Vita della Data Science: Comunicazione
|![ Sketchnote di [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/16-Communicating.png)|

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# Racconta una storia
## Istruzioni

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# Il ciclo di vita della Data Science
![comunicazione](../../../translated_images/it/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg)

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# Introduzione alla Data Science nel Cloud
|![ Sketchnote di [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/17-DataScience-Cloud.png)|

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# Ricerca di Mercato
## Istruzioni

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# Data Science nel Cloud: Il metodo "Low code/No code"
|![ Sketchnote di [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/18-DataScience-Cloud.png)|

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# Progetto di Data Science Low code/No code su Azure ML
## Istruzioni

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# Data Science nel Cloud: Il metodo "Azure ML SDK"
|![ Sketchnote di [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/19-DataScience-Cloud.png)|

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# Progetto di Data Science con Azure ML SDK
## Istruzioni

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# Data Science nel Cloud
![cloud-picture](../../../translated_images/it/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg)

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# Data Science nel Mondo Reale
| ![ Sketchnote di [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-RealWorld.png) |

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# Esplora un Dataset del Planetary Computer
## Istruzioni

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# Data Science nel Mondo Reale
Applicazioni pratiche della data science in diversi settori.

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# AGENTS.md
## Panoramica del Progetto

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# Codice di Condotta per l'Open Source di Microsoft
Questo progetto ha adottato il [Codice di Condotta per l'Open Source di Microsoft](https://opensource.microsoft.com/codeofconduct/).

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# Contribuire a Data Science for Beginners
Grazie per il tuo interesse nel contribuire al curriculum di Data Science for Beginners! Accogliamo con piacere i contributi della comunità.

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# Guida all'Installazione
Questa guida ti aiuterà a configurare l'ambiente per lavorare con il curriculum "Data Science for Beginners".

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# Data Science per Principianti - Un Curriculum
[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198)
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[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
Gli Azure Cloud Advocates di Microsoft sono lieti di offrire un curriculum di 10 settimane, con 20 lezioni, tutto dedicato alla Data Science. Ogni lezione include quiz pre-lezione e post-lezione, istruzioni scritte per completare la lezione, una soluzione e un compito. La nostra pedagogia basata su progetti permette di imparare costruendo, un modo comprovato per far 'assorbire' nuove competenze.
Gli Azure Cloud Advocates di Microsoft sono lieti di offrire un curriculum di 10 settimane, 20 lezioni tutto dedicato alla Data Science. Ogni lezione include quiz pre-lezione e post-lezione, istruzioni scritte per completare la lezione, una soluzione e un compito. La nostra pedagogia basata su progetti permette di imparare costruendo, un modo provato per far sì che le nuove competenze "rimangano".
**Un sentito ringraziamento ai nostri autori:** [Jasmine Greenaway](https://www.twitter.com/paladique), [Dmitry Soshnikov](http://soshnikov.com), [Nitya Narasimhan](https://twitter.com/nitya), [Jalen McGee](https://twitter.com/JalenMcG), [Jen Looper](https://twitter.com/jenlooper), [Maud Levy](https://twitter.com/maudstweets), [Tiffany Souterre](https://twitter.com/TiffanySouterre), [Christopher Harrison](https://www.twitter.com/geektrainer).
**Un sentito grazie ai nostri autori:** [Jasmine Greenaway](https://www.twitter.com/paladique), [Dmitry Soshnikov](http://soshnikov.com), [Nitya Narasimhan](https://twitter.com/nitya), [Jalen McGee](https://twitter.com/JalenMcG), [Jen Looper](https://twitter.com/jenlooper), [Maud Levy](https://twitter.com/maudstweets), [Tiffany Souterre](https://twitter.com/TiffanySouterre), [Christopher Harrison](https://www.twitter.com/geektrainer).
**🙏 Ringraziamenti speciali 🙏 ai nostri autori, revisori e contributori di contenuti, [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** in particolare Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200),
**🙏 Ringraziamenti speciali 🙏 ai nostri autori, revisori e contributori di contenuti [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** in particolare Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200),
[Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/)
|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/it/00-Title.8af36cd35da1ac55.webp)|
|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/it/00-Title.8af36cd35da1ac55.webp)|
|:---:|
| Data Science Per Principianti - _Sketchnote di [@nitya](https://twitter.com/nitya)_ |
| Data Science per Principianti - _Sketchnote di [@nitya](https://twitter.com/nitya)_ |
### 🌐 Supporto Multilingue
#### Supportato tramite GitHub Action (Automatico e Sempre Aggiornato)
#### Supportato tramite GitHub Action (Automatizzato e Sempre Aggiornato)
<!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](./README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md)
[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](../zh-HK/README.md) | [Chinese (Traditional, Macau)](../zh-MO/README.md) | [Chinese (Traditional, Taiwan)](../zh-TW/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](./README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../pt-BR/README.md) | [Portuguese (Portugal)](../pt-PT/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md)
> **Preferisci Clonare Localmente?**
> **Preferisci clonare localmente?**
> Questo repository include più di 50 traduzioni linguistiche che aumentano significativamente la dimensione del download. Per clonare senza traduzioni, usa sparse checkout:
> Questo repository include più di 50 traduzioni che aumentano significativamente la dimensione del download. Per clonare senza traduzioni, usa sparse checkout:
> ```bash
> git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git
> cd Data-Science-For-Beginners
> git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
> ```
> Questo ti dà tutto il necessario per completare il corso con un download molto più veloce.
> Questo ti dà tutto il necessario per completare il corso con un download molto più rapido.
<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
**Se desideri avere supporto per ulteriori lingue, quelle supportate sono elencate [qui](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
**Se desideri avere supporto per ulteriori lingue di traduzione, le lingue supportate sono elencate [qui](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
#### Unisciti alla nostra Comunità
#### Unisciti alla nostra comunità
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
Abbiamo in corso una serie Discord "Impara con l'AI", scopri di più e unisciti a noi su [Learn with AI Series](https://aka.ms/learnwithai/discord) dal 18 al 30 settembre 2025. Riceverai consigli e trucchi per usare GitHub Copilot per la Data Science.
Abbiamo in corso una serie su Discord "impara con l'AI", scopri di più e unisciti a noi su [Learn with AI Series](https://aka.ms/learnwithai/discord) dal 18 al 30 settembre 2025. Riceverai suggerimenti e trucchi sull'uso di GitHub Copilot per la Data Science.
![Learn with AI series](../../../../translated_images/it/1.2b28cdc6205e26fe.webp)
![Learn with AI series](../../translated_images/it/1.2b28cdc6205e26fe.webp)
# Sei uno studente?
Inizia con le seguenti risorse:
- [Pagina Student Hub](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) In questa pagina troverai risorse per principianti, pacchetti per studenti e anche modi per ottenere un voucher gratuito per la certificazione. Questa è una pagina che vorrai aggiungere ai preferiti e controllare di tanto in tanto poiché cambiamo il contenuto almeno mensilmente.
- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Unisciti a una comunità globale di ambasciatori studenteschi, questo potrebbe essere il tuo modo per entrare in Microsoft.
- [Pagina Student Hub](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) In questa pagina troverai risorse per principianti, pacchetti per studenti e persino modi per ottenere un voucher per la certificazione gratuito. Questa è una pagina che vuoi aggiungere ai preferiti e controllare di tanto in tanto, poiché aggiorniamo i contenuti almeno mensilmente.
- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Unisciti a una comunità globale di ambasciatori studenti, questo potrebbe essere il tuo modo per entrare in Microsoft.
# Iniziare
# Come iniziare
## 📚 Documentazione
- **[Guida all'Installazione](INSTALLATION.md)** - Istruzioni passo dopo passo per configurare l'ambiente per principianti
- **[Guida all'Uso](USAGE.md)** - Esempi e flussi di lavoro comuni
- **[Risoluzione Problemi](TROUBLESHOOTING.md)** - Soluzioni ai problemi comuni
- **[Guida alla Contribuzione](CONTRIBUTING.md)** - Come contribuire a questo progetto
- **[Per Insegnanti](for-teachers.md)** - Indicazioni didattiche e risorse per la classe
- **[Guida all'installazione](INSTALLATION.md)** - Istruzioni passo-passo per principianti
- **[Guida all'uso](USAGE.md)** - Esempi e flussi di lavoro comuni
- **[Risoluzione dei problemi](TROUBLESHOOTING.md)** - Soluzioni ai problemi comuni
- **[Guida alla contribuzione](CONTRIBUTING.md)** - Come contribuire a questo progetto
- **[Per insegnanti](for-teachers.md)** - Indicazioni didattiche e risorse per la classe
## 👨‍🎓 Per gli Studenti
> **Principianti Completi**: Nuovo nel campo della data science? Inizia con i nostri [esempi per principianti](examples/README.md)! Questi esempi semplici e ben commentati ti aiuteranno a comprendere le basi prima di affrontare l'intero curriculum.
> **[Studenti](https://aka.ms/student-page)**: per usare questo curriculum autonomamente, fai il fork dell'intero repository e completa gli esercizi da solo, iniziando con un quiz pre-lezione. Poi leggi la lezione e completa il resto delle attività. Cerca di creare i progetti comprendendo le lezioni piuttosto che copiando il codice della soluzione; tuttavia, questo codice è disponibile nelle cartelle /solutions in ogni lezione orientata al progetto. Un'altra idea potrebbe essere formare un gruppo di studio con amici e affrontare i contenuti insieme. Per ulteriori studi, raccomandiamo [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
## 👨‍🎓 Per studenti
> **Principianti assoluti**: Nuovo alla data science? Inizia con i nostri [esempi per principianti](examples/README.md)! Questi esempi semplici e ben commentati ti aiuteranno a comprendere le basi prima di immergerti nel curriculum completo.
> **[Studenti](https://aka.ms/student-page)**: per usare questo curriculum da soli, fai il fork del repository completo e completa gli esercizi in autonomia, iniziando con un quiz pre-lezione. Poi leggi la lezione e completa il resto delle attività. Cerca di creare i progetti comprendendo le lezioni più che copiando il codice soluzione; comunque quel codice è disponibile nelle cartelle /solutions di ogni lezione orientata al progetto. Un’altra idea potrebbe essere formare un gruppo di studio con amici e affrontare insieme i contenuti. Per ulteriori approfondimenti, consigliamo [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
**Avvio rapido:**
1. Consulta la [Guida all'Installazione](INSTALLATION.md) per configurare il tuo ambiente
2. Rivedi la [Guida all'Uso](USAGE.md) per imparare a lavorare con il curriculum
3. Inizia dalla Lezione 1 e procedi in ordine
4. Unisciti alla nostra [community Discord](https://aka.ms/ds4beginners/discord) per supporto
1. Consulta la [Guida all'installazione](INSTALLATION.md) per configurare l'ambiente
2. Revisione della [Guida all'uso](USAGE.md) per imparare come lavorare con il curriculum
3. Inizia con la Lezione 1 e procedi sequenzialmente
4. Unisciti alla nostra [comunità Discord](https://aka.ms/ds4beginners/discord) per supporto
## 👩‍🏫 Per gli Insegnanti
> **Insegnanti**: abbiamo [incluso alcuni suggerimenti](for-teachers.md) su come utilizzare questo curriculum. Ci piacerebbe ricevere il vostro feedback [nel nostro forum di discussione](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
## 👩‍🏫 Per insegnanti
> **Insegnanti**: abbiamo [incluso alcuni suggerimenti](for-teachers.md) su come usare questo curriculum. Ci piacerebbe ricevere il tuo feedback [nel nostro forum di discussione](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
## Incontra il Team
[![Video promozionale](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Video promozionale")
**Gif di** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal)
@ -104,11 +95,11 @@ Inizia con le seguenti risorse:
## Pedagogia
Abbiamo scelto due principi pedagogici durante la creazione di questo curriculum: assicurare che sia basato su progetti e che includa quiz frequenti. Al termine di questa serie, gli studenti avranno appreso i principi base della data science, compresi i concetti etici, la preparazione dei dati, diversi modi di lavorare con i dati, la visualizzazione dei dati, l'analisi dei dati, casi d'uso reali della data science e altro ancora.
Abbiamo scelto due principi pedagogici durante la costruzione di questo curriculum: assicurare che sia basato su progetti e che includa quiz frequenti. Alla fine di questa serie, gli studenti avranno appreso i principi base della scienza dei dati, inclusi concetti etici, preparazione dei dati, diversi modi di lavorare con i dati, visualizzazione dei dati, analisi dei dati, casi d'uso reali della scienza dei dati e altro.
Inoltre, un quiz a basso rischio prima della lezione permette allo studente di orientarsi verso l'apprendimento di un argomento, mentre un secondo quiz dopo la lezione ne garantisce una ulteriore ritenzione. Questo curriculum è stato progettato per essere flessibile e divertente e può essere seguito integralmente o in parte. I progetti iniziano con esempi semplici e diventano sempre più complessi entro la fine del ciclo di 10 settimane.
Inoltre, un quiz a basso impatto prima di una lezione stabilisce l'intenzione dello studente verso l'apprendimento di un argomento, mentre un secondo quiz dopo la lezione assicura una maggiore ritenzione. Questo curriculum è stato progettato per essere flessibile e divertente e può essere seguito interamente o parzialmente. I progetti partono da livelli semplici e diventano sempre più complessi entro la fine del ciclo di 10 settimane.
> Trova il nostro [Codice di Condotta](CODE_OF_CONDUCT.md), le linee guida per [Contributi](CONTRIBUTING.md), [Traduzioni](TRANSLATIONS.md). Accogliamo volentieri i tuoi feedback costruttivi!
> Trova il nostro [Codice di Condotta](CODE_OF_CONDUCT.md), le linee guida per il [Contributo](CONTRIBUTING.md), la [Traduzione](TRANSLATIONS.md). Accogliamo con piacere i tuoi feedback costruttivi!
## Ogni lezione include:
@ -116,23 +107,23 @@ Inoltre, un quiz a basso rischio prima della lezione permette allo studente di o
- Video supplementare opzionale
- Quiz di riscaldamento pre-lezione
- Lezione scritta
- Per le lezioni basate su progetti, guide passo-passo su come costruire il progetto
- Per le lezioni basate su progetti, guide passo passo su come costruire il progetto
- Verifiche di conoscenza
- Una sfida
- Letture supplementari
- Lettura supplementare
- Compito
- [Quiz post-lezione](https://ff-quizzes.netlify.app/en/)
> **Una nota sui quiz**: Tutti i quiz sono contenuti nella cartella Quiz-App, per un totale di 40 quiz con tre domande ciascuno. Sono linkati all'interno delle lezioni, ma l'app quiz può essere eseguita localmente o distribuita su Azure; segui le istruzioni nella cartella `quiz-app`. Stanno venendo progressivamente localizzati.
> **Una nota sui quiz**: Tutti i quiz si trovano nella cartella Quiz-App, per un totale di 40 quiz da tre domande ciascuno. Sono collegati all’interno delle lezioni, ma l’app quiz può essere eseguita localmente o distribuita su Azure; segui le istruzioni nella cartella `quiz-app`. Sono progressivamente localizzati.
## 🎓 Esempi per principianti
## 🎓 Esempi per Principianti
**Nuovo nella Data Science?** Abbiamo creato una speciale [cartella di esempi](examples/README.md) con codice semplice e ben commentato per aiutarti a iniziare:
**Nuovo nella Scienza dei Dati?** Abbiamo creato una speciale [directory di esempi](examples/README.md) con codice semplice e ben commentato per aiutarti a iniziare:
- 🌟 **Hello World** - Il tuo primo programma di data science
- 📂 **Caricamento dei Dati** - Impara a leggere e esplorare i dataset
- 📊 **Analisi Semplice** - Calcola statistiche e scopri modelli
- 📈 **Visualizzazione Base** - Crea grafici e diagrammi
- 🌟 **Hello World** - Il tuo primo programma di scienza dei dati
- 📂 **Caricamento Dati** - Impara a leggere ed esplorare dataset
- 📊 **Analisi Semplice** - Calcola statistiche e trova modelli
- 📈 **Visualizzazione Base** - Crea grafici e tabelle
- 🔬 **Progetto Reale** - Workflow completo dall'inizio alla fine
Ogni esempio include commenti dettagliati che spiegano ogni passaggio, perfetto per principianti assoluti!
@ -142,78 +133,78 @@ Ogni esempio include commenti dettagliati che spiegano ogni passaggio, perfetto
## Lezioni
|![ Sketchnote di @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/it/00-Roadmap.4905d6567dff4753.webp)|
|![ Sketchnote di @sketchthedocs https://sketchthedocs.dev](../../translated_images/it/00-Roadmap.4905d6567dff4753.webp)|
|:---:|
| Data Science Per Principianti: Roadmap - _Sketchnote di [@nitya](https://twitter.com/nitya)_ |
| Scienza dei Dati per Principianti: Roadmap - _Sketchnote di [@nitya](https://twitter.com/nitya)_ |
| Numero Lezione | Argomento | Raggruppamento Lezione | Obiettivi di Apprendimento | Lezione Collegata | Autore |
| :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: |
| 01 | Definire la Data Science | [Introduzione](1-Introduction/README.md) | Impara i concetti base dietro la data science e come è correlata all'intelligenza artificiale, al machine learning e ai big data. | [lezione](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) |
| 02 | Etica nella Data Science | [Introduzione](1-Introduction/README.md) | Concetti, sfide e framework di etica dei dati. | [lezione](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) |
| 03 | Definire i Dati | [Introduzione](1-Introduction/README.md) | Come i dati sono classificati e le loro fonti comuni. | [lezione](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) |
| 04 | Introduzione a Statistica e Probabilità | [Introduzione](1-Introduction/README.md) | Le tecniche matematiche di probabilità e statistica per comprendere i dati. | [lezione](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) |
| 05 | Lavorare con Dati Relazionali | [Lavorare con i Dati](2-Working-With-Data/README.md) | Introduzione ai dati relazionali e le basi dell'esplorazione e analisi di dati relazionali con il Structured Query Language, noto anche come SQL (pronunciato “see-quell”). | [lezione](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | |
| 06 | Lavorare con Dati NoSQL | [Lavorare con i Dati](2-Working-With-Data/README.md) | Introduzione ai dati non relazionali, i loro vari tipi e le basi per esplorare e analizzare database di documenti. | [lezione](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)|
| 07 | Lavorare con Python | [Lavorare con i Dati](2-Working-With-Data/README.md) | Nozioni base sull'uso di Python per l'esplorazione dei dati con librerie come Pandas. Si raccomanda una conoscenza di base della programmazione Python. | [lezione](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) |
| 08 | Preparazione dei Dati | [Lavorare con i Dati](2-Working-With-Data/README.md) | Argomenti su tecniche di pulizia e trasformazione dei dati per gestire sfide di dati mancanti, inaccurati o incompleti. | [lezione](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) |
| 09 | Visualizzazione delle Quantità | [Visualizzazione dei Dati](3-Data-Visualization/README.md) | Impara come usare Matplotlib per visualizzare dati sugli uccelli 🦆 | [lezione](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) |
| 10 | Visualizzazione delle Distribuzioni di Dati | [Visualizzazione dei Dati](3-Data-Visualization/README.md) | Visualizzazione di osservazioni e tendenze all'interno di un intervallo. | [lezione](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) |
| 11 | Visualizzazione delle Proporzioni | [Visualizzazione dei Dati](3-Data-Visualization/README.md) | Visualizzazione di percentuali discrete e raggruppate. | [lezione](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) |
| 12 | Visualizzazione delle Relazioni | [Visualizzazione dei Dati](3-Data-Visualization/README.md) | Visualizzazione di connessioni e correlazioni tra insiemi di dati e delle loro variabili. | [lezione](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) |
| 13 | Visualizzazioni Significative | [Visualizzazione dei Dati](3-Data-Visualization/README.md) | Tecniche e indicazioni per rendere le tue visualizzazioni preziose per una risoluzione efficace dei problemi e per ottenere insight. | [lezione](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) |
| 14 | Introduzione al ciclo di vita della Data Science | [Ciclo di vita](4-Data-Science-Lifecycle/README.md) | Introduzione al ciclo di vita della data science e al suo primo passo di acquisizione ed estrazione dei dati. | [lezione](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) |
| 15 | Analisi | [Ciclo di vita](4-Data-Science-Lifecycle/README.md) | Questa fase del ciclo di vita della data science si concentra sulle tecniche per analizzare i dati. | [lezione](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | |
| 16 | Comunicazione | [Ciclo di vita](4-Data-Science-Lifecycle/README.md) | Questa fase del ciclo di vita della data science si concentra sulla presentazione degli insight dai dati in modo che sia più facile per i decisori comprenderli. | [lezione](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | |
| 17 | Data Science nel Cloud | [Dati Cloud](5-Data-Science-In-Cloud/README.md) | Questa serie di lezioni introduce la data science nel cloud e i suoi benefici. | [lezione](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) e [Maud](https://twitter.com/maudstweets) |
| 18 | Data Science nel Cloud | [Dati Cloud](5-Data-Science-In-Cloud/README.md) | Addestrare modelli usando strumenti Low Code. |[lezione](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) e [Maud](https://twitter.com/maudstweets) |
| 19 | Data Science nel Cloud | [Dati Cloud](5-Data-Science-In-Cloud/README.md) | Distribuzione di modelli con Azure Machine Learning Studio. | [lezione](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) e [Maud](https://twitter.com/maudstweets) |
| 20 | Data Science sul campo | [Nel campo](6-Data-Science-In-Wild/README.md) | Progetti guidati dalla data science nel mondo reale. | [lezione](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) |
| 01 | Definizione di Scienza dei Dati | [Introduzione](1-Introduction/README.md) | Impara i concetti base della scienza dei dati e come si relaziona con intelligenza artificiale, machine learning e big data. | [lezione](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) |
| 02 | Etica nella Scienza dei Dati | [Introduzione](1-Introduction/README.md) | Concetti, sfide e framework dell’etica dei dati. | [lezione](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) |
| 03 | Definizione di Dati | [Introduzione](1-Introduction/README.md) | Come i dati sono classificati e le loro fonti comuni. | [lezione](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) |
| 04 | Introduzione a Statistica e Probabilità | [Introduzione](1-Introduction/README.md) | Le tecniche matematiche della probabilità e della statistica per comprendere i dati. | [lezione](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) |
| 05 | Lavorare con Dati Relazionali | [Lavorare con i Dati](2-Working-With-Data/README.md) | Introduzione ai dati relazionali e le basi dell'esplorazione e analisi con il Structured Query Language, noto come SQL (pronunciato “see-quell”). | [lezione](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | |
| 06 | Lavorare con Dati NoSQL | [Lavorare con i Dati](2-Working-With-Data/README.md) | Introduzione ai dati non relazionali, i suoi vari tipi e le basi dell'esplorazione e analisi di database di documenti. | [lezione](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)|
| 07 | Lavorare con Python | [Lavorare con i Dati](2-Working-With-Data/README.md) | Basi dell'uso di Python per l'esplorazione dei dati con librerie come Pandas. Si raccomanda una comprensione base della programmazione Python. | [lezione](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) |
| 08 | Preparazione dei Dati | [Lavorare con i Dati](2-Working-With-Data/README.md) | Temi relativi a tecniche di pulizia e trasformazione dei dati per affrontare sfide di dati mancanti, inaccurati o incompleti. | [lezione](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) |
| 09 | Visualizzazione di Quantità | [Data Visualization](3-Data-Visualization/README.md) | Impara a usare Matplotlib per visualizzare dati ornitologici 🦆 | [lezione](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) |
| 10 | Visualizzare Distribuzioni di Dati | [Data Visualization](3-Data-Visualization/README.md) | Visualizzare osservazioni e tendenze all’interno di un intervallo. | [lezione](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) |
| 11 | Visualizzare Proporzioni | [Data Visualization](3-Data-Visualization/README.md) | Visualizzare percentuali discrete e raggruppate. | [lezione](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) |
| 12 | Visualizzare Relazioni | [Data Visualization](3-Data-Visualization/README.md) | Visualizzare connessioni e correlazioni tra insiemi di dati e variabili. | [lezione](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) |
| 13 | Visualizzazioni Significative | [Data Visualization](3-Data-Visualization/README.md) | Tecniche e indicazioni per rendere le tue visualizzazioni preziose per una risoluzione efficace dei problemi e approfondimenti. | [lezione](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) |
| 14 | Introduzione al ciclo di vita della Scienza dei Dati | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Introduzione al ciclo di vita della scienza dei dati e il suo primo passo di acquisizione ed estrazione dei dati. | [lezione](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) |
| 15 | Analisi | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Questa fase del ciclo di vita della scienza dei dati si concentra sulle tecniche per analizzare i dati. | [lezione](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | |
| 16 | Comunicazione | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Questa fase del ciclo di vita della scienza dei dati si concentra sul presentare gli insight dai dati in modo che sia più facile per i decisori comprendere. | [lezione](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | |
| 17 | Scienza dei Dati nel Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Questa serie di lezioni introduce la scienza dei dati nel cloud e i suoi vantaggi. | [lezione](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) e [Maud](https://twitter.com/maudstweets) |
| 18 | Scienza dei Dati nel Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Addestramento modelli usando strumenti Low Code. |[lezione](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) e [Maud](https://twitter.com/maudstweets) |
| 19 | Scienza dei Dati nel Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Distribuzione dei modelli con Azure Machine Learning Studio. | [lezione](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) e [Maud](https://twitter.com/maudstweets) |
| 20 | Scienza dei Dati nel Mondo Reale | [In the Wild](6-Data-Science-In-Wild/README.md) | Progetti di scienza dei dati nel mondo reale. | [lezione](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) |
## GitHub Codespaces
Segui questi passaggi per aprire questo esempio in un Codespace:
1. Clicca sul menu a discesa Code e seleziona l'opzione Open with Codespaces.
1. Clicca sul menu a tendina Code e seleziona l'opzione Open with Codespaces.
2. Seleziona + New codespace in fondo al pannello.
Per maggiori informazioni, consulta la [documentazione GitHub](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace).
Per ulteriori informazioni, consulta la [documentazione GitHub](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace).
## VSCode Remote - Containers
Segui questi passaggi per aprire questo repo in un container usando la tua macchina locale e VSCode con l'estensione VS Code Remote - Containers:
Segui questi passaggi per aprire questo repo in un container usando la tua macchina locale e VSCode con l’estensione VS Code Remote - Containers:
1. Se è la prima volta che usi un container di sviluppo, assicurati che il tuo sistema soddisfi i prerequisiti (ad esempio, avere Docker installato) nella [documentazione di avvio](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started).
1. Se è la prima volta che usi un container di sviluppo, assicurati che il sistema soddisfi i prerequisiti (cioè avere Docker installato) nella [documentazione per iniziare](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started).
Per usare questo repository, puoi aprirlo in un volume Docker isolato:
Per usare questo repository, puoi o aprire il repository in un volume Docker isolato:
**Nota**: Sotto il cofano, questo userà il comando Remote-Containers: **Clone Repository in Container Volume...** per clonare il codice sorgente in un volume Docker anziché nel filesystem locale. I [volumi](https://docs.docker.com/storage/volumes/) sono il meccanismo preferito per persistere dati del container.
**Nota**: Dietro le quinte, questo userà il comando Remote-Containers: **Clone Repository in Container Volume...** per clonare il codice sorgente in un volume Docker invece che nel filesystem locale. I [volumi](https://docs.docker.com/storage/volumes/) sono il meccanismo preferito per persistere dati del container.
Oppure apri una copia clonata o scaricata localmente del repository:
- Clona questo repository sul tuo filesystem locale.
- Clona questo repository nel tuo filesystem locale.
- Premi F1 e seleziona il comando **Remote-Containers: Open Folder in Container...**.
- Seleziona la copia clonata di questa cartella, aspetta che il container si avvii e inizia a usarlo.
- Seleziona la copia clonata di questa cartella, attendi l’avvio del container e prova.
## Accesso offline
## Accesso Offline
Puoi eseguire questa documentazione offline usando [Docsify](https://docsify.js.org/#/). Forka questo repo, [installa Docsify](https://docsify.js.org/#/quickstart) sulla tua macchina locale, poi nella cartella radice di questo repo, digita `docsify serve`. Il sito sarà servito sulla porta 3000 sul tuo localhost: `localhost:3000`.
Puoi eseguire questa documentazione offline usando [Docsify](https://docsify.js.org/#/). Fai il fork di questo repo, [installa Docsify](https://docsify.js.org/#/quickstart) sulla tua macchina locale, quindi nella cartella principale di questo repo, digita `docsify serve`. Il sito sarà servito sulla porta 3000 in locale: `localhost:3000`.
> Nota, i notebook non saranno renderizzati tramite Docsify, quindi quando devi eseguire un notebook, fallo separatamente in VS Code con un kernel Python.
> Nota, i notebook non saranno resi via Docsify, quindi quando devi eseguire un notebook, fallo separatamente in VS Code con un kernel Python attivo.
## Altri Curricula
Il nostro team produce altri curricula! Dai un'occhiata a:
Il nostro team produce altri curricula! Dai un’occhiata:
<!-- CO-OP TRANSLATOR OTHER COURSES START -->
### LangChain
[![LangChain4j per Principianti](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
[![LangChain.js per Principianti](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
---
### Azure / Edge / MCP / Agenti
### Azure / Edge / MCP / Agent
[![AZD per Principianti](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
[![Edge AI per Principianti](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
[![MCP per Principianti](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
[![Agenti AI per Principianti](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
[![Agent AI per Principianti](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
---
@ -225,38 +216,38 @@ Il nostro team produce altri curricula! Dai un'occhiata a:
---
### Apprendimento Fondamentale
### Apprendimento di Base
[![ML per Principianti](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
[![Data Science per Principianti](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
[![AI per Principianti](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
[![Cybersecurity per Principianti](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
[![Sviluppo Web per Principianti](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
[![Web Dev per Principianti](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
[![IoT per Principianti](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
[![Sviluppo XR per Principianti](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
---
### Serie Copilot
[![Copilot per Programmazione Assistita da AI](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
[![Copilot per Programmazione Affiancata AI](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
[![Copilot per C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
[![Avventura Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
<!-- CO-OP TRANSLATOR OTHER COURSES END -->
## Ottenere Aiuto
## Ottieni Aiuto
**Hai problemi?** Consulta la nostra [Guida alla Risoluzione dei Problemi](TROUBLESHOOTING.md) per soluzioni ai problemi comuni.
Se rimani bloccato o hai domande sulla creazione di app AI, unisciti a altri studenti e sviluppatori esperti nelle discussioni su MCP. È una comunità di supporto dove le domande sono benvenute e la conoscenza viene condivisa liberamente.
Se rimani bloccato o hai domande sulla creazione di app AI. Unisciti ad altri studenti e sviluppatori esperti nelle discussioni su MCP. È una comunità di supporto dove le domande sono benvenute e la conoscenza viene condivisa liberamente.
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
Se hai feedback sul prodotto o errori durante lo sviluppo visita:
Se hai feedback sul prodotto o errori durante la creazione visita:
[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
---
<!-- CO-OP TRANSLATOR DISCLAIMER START -->
**Disclaimer**:
Questo documento è stato tradotto utilizzando il servizio di traduzione automatica [Co-op Translator](https://github.com/Azure/co-op-translator). Pur impegnandoci per garantire la precisione, si prega di considerare che le traduzioni automatiche potrebbero contenere errori o imprecisioni. Il documento originale nella sua lingua nativa deve essere considerato la fonte autorevole. Per informazioni critiche, si raccomanda una traduzione professionale effettuata da un esperto umano. Non siamo responsabili per eventuali incomprensioni o interpretazioni errate derivanti dall’uso di questa traduzione.
**Avvertenza**:
Questo documento è stato tradotto utilizzando il servizio di traduzione automatica [Co-op Translator](https://github.com/Azure/co-op-translator). Pur impegnandoci per garantire accuratezza, si prega di notare che le traduzioni automatiche possono contenere errori o imprecisioni. Il documento originale nella sua lingua originale deve essere considerato la fonte autorevole. Per informazioni critiche, si raccomanda una traduzione professionale effettuata da un umano. Non ci assumiamo alcuna responsabilità per incomprensioni o interpretazioni errate derivanti dall’uso di questa traduzione.
<!-- CO-OP TRANSLATOR DISCLAIMER END -->

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## Sicurezza
Microsoft prende molto seriamente la sicurezza dei propri prodotti software e servizi, inclusi tutti i repository di codice sorgente gestiti attraverso le nostre organizzazioni GitHub, che includono [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin) e [le nostre organizzazioni GitHub](https://opensource.microsoft.com/).

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# Supporto
## Come segnalare problemi e ottenere assistenza

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# Guida alla Risoluzione dei Problemi
Questa guida fornisce soluzioni ai problemi comuni che potresti incontrare mentre lavori con il curriculum "Data Science for Beginners".

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# Guida all'Uso
Questa guida fornisce esempi e flussi di lavoro comuni per utilizzare il curriculum "Data Science for Beginners".

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- Introduzione
- [Definire la Data Science](../1-Introduction/01-defining-data-science/README.md)
- [Etica della Data Science](../1-Introduction/02-ethics/README.md)

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# Esempi di Data Science per Principianti
Benvenuto nella directory degli esempi! Questa raccolta di esempi semplici e ben commentati è pensata per aiutarti a iniziare con la data science, anche se sei un principiante assoluto.

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## Per Educatori
Vorresti utilizzare questo curriculum nella tua classe? Sentiti libero di farlo!

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# Quiz
Questi quiz sono i quiz pre- e post-lezione per il curriculum di data science disponibile su https://aka.ms/datascience-beginners

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Trova tutte le sketchnote qui!
## Crediti

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# Definiowanie Data Science
| ![ Sketchnote autorstwa [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/01-Definitions.png) |

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# Zadanie: Scenariusze Data Science
W tym pierwszym zadaniu prosimy Cię, abyś zastanowił się nad jakimś procesem lub problemem z życia codziennego w różnych obszarach tematycznych i pomyślał, jak można go ulepszyć, korzystając z procesu Data Science. Zastanów się nad następującymi kwestiami:

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# Zadanie: Scenariusze Data Science
W tym pierwszym zadaniu prosimy Cię, abyś zastanowił się nad rzeczywistym procesem lub problemem w różnych obszarach tematycznych i jak można go ulepszyć, korzystając z procesu Data Science. Pomyśl o następujących kwestiach:

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# Wprowadzenie do etyki danych
|![ Sketchnote autorstwa [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/02-Ethics.png)|

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## Napisz studium przypadku dotyczące etyki danych
## Instrukcje

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# Definiowanie danych
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/03-DefiningData.png)|

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# Klasyfikacja Zbiorów Danych
## Instrukcje

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# Krótkie wprowadzenie do statystyki i teorii prawdopodobieństwa
|![ Sketchnote autorstwa [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/04-Statistics-Probability.png)|
@ -64,7 +55,7 @@ Aby lepiej zrozumieć rozkład danych, warto mówić o **kwartylach**:
Graficznie możemy przedstawić zależność między medianą a kwartylami na diagramie zwanym **box plot**:
<img src="images/boxplot_explanation.png" alt="Wyjaśnienie wykresu pudełkowego" width="50%">
<img src="../../../../translated_images/pl/boxplot_explanation.4039b7de08780fd4.webp" alt="Wyjaśnienie wykresu pudełkowego" width="50%">
Tutaj obliczamy również **rozstęp międzykwartylowy** IQR=Q3-Q1 oraz tzw. **wartości odstające** - wartości, które znajdują się poza granicami [Q1-1.5*IQR, Q3+1.5*IQR].

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# Małe badanie nad cukrzycą
W tym zadaniu będziemy pracować z małym zestawem danych pacjentów z cukrzycą, pobranym z [tutaj](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).

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# Wprowadzenie do Data Science
![dane w akcji](../../../translated_images/pl/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg)

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# Praca z danymi: bazy danych relacyjne
|![ Sketchnote autorstwa [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/05-RelationalData.png)|

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# Wyświetlanie danych lotnisk
Otrzymałeś [bazę danych](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db) opartą na [SQLite](https://sqlite.org/index.html), która zawiera informacje o lotniskach. Schemat bazy danych jest przedstawiony poniżej. Użyjesz [rozszerzenia SQLite](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) w [Visual Studio Code](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum), aby wyświetlić informacje o lotniskach w różnych miastach.

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# Praca z danymi: Dane nierelacyjne
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/06-NoSQL.png)|

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# Zyski z napojów gazowanych
## Instrukcje

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# Praca z danymi: Python i biblioteka Pandas
| ![ Sketchnote autorstwa [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/07-WorkWithPython.png) |

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# Zadanie dotyczące przetwarzania danych w Pythonie
W tym zadaniu poprosimy Cię o rozwinięcie kodu, który zaczęliśmy tworzyć w naszych wyzwaniach. Zadanie składa się z dwóch części:

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# Praca z danymi: Przygotowanie danych
|![ Sketchnote autorstwa [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/08-DataPreparation.png)|

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# Ocena danych z formularza
Klient testował [mały formularz](../../../../2-Working-With-Data/08-data-preparation/index.html), aby zebrać podstawowe informacje o swojej bazie klientów. Przekazał Ci swoje wyniki, abyś zweryfikował dane, które zgromadzili. Możesz otworzyć stronę `index.html` w przeglądarce, aby zapoznać się z formularzem.

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# Praca z danymi
![data love](../../../translated_images/pl/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg)

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# Wizualizacja ilości
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# Linie, wykresy punktowe i słupkowe
## Instrukcje

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# Wizualizacja rozkładów
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# Zastosuj swoje umiejętności
## Instrukcje

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# Wizualizacja proporcji
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# Wypróbuj to w Excelu
## Instrukcje

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# Wizualizacja relacji: Wszystko o miodzie 🍯
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# Zanurkuj w ul
## Instrukcje

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# Tworzenie Znaczących Wizualizacji
|![ Sketchnote autorstwa [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/13-MeaningfulViz.png)|

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