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

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# Opgave: Data Science Scenarier
I denne første opgave beder vi dig om at tænke over nogle virkelige processer eller problemer inden for forskellige problemområder, og hvordan du kan forbedre dem ved hjælp af Data Science-processen. Tænk over følgende:

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# Opgave: Data Science Scenarier
I denne første opgave beder vi dig om at tænke på nogle virkelige processer eller problemer inden for forskellige problemområder, og hvordan du kan forbedre dem ved hjælp af Data Science-processen. Tænk over følgende:

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# Introduktion til Dataetik
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/02-Ethics.png)|

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## Skriv en Case Study om Dataetik
## Instruktioner

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# Definering af Data
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/03-DefiningData.png)|

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# Klassificering af datasæt
## Instruktioner

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# En Kort Introduktion til Statistik og Sandsynlighed
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/04-Statistics-Probability.png)|
@ -64,7 +55,7 @@ For at hjælpe os med at forstå fordelingen af data er det nyttigt at tale om *
Grafisk kan vi repræsentere forholdet mellem median og kvartiler i et diagram kaldet **boksplot**:
<img src="images/boxplot_explanation.png" alt="Forklaring af boksplot" width="50%">
<img src="../../../../translated_images/da/boxplot_explanation.4039b7de08780fd4.webp" alt="Forklaring af boksplot" width="50%">
Her beregner vi også **interkvartilafstand** IQR=Q3-Q1 og såkaldte **outliers** - værdier, der ligger uden for grænserne [Q1-1.5*IQR,Q3+1.5*IQR].

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# Lille Diabetesundersøgelse
I denne opgave skal vi arbejde med et lille datasæt af diabetespatienter taget fra [her](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).

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# Introduktion til Data Science
![data i aktion](../../../translated_images/da/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg)

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# Arbejde med data: Relationelle databaser
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/05-RelationalData.png)|

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# Vise lufthavnsdata
Du har fået en [database](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db) bygget på [SQLite](https://sqlite.org/index.html), som indeholder information om lufthavne. Skemaet vises nedenfor. Du vil bruge [SQLite-udvidelsen](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) i [Visual Studio Code](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum) til at vise information om forskellige byers lufthavne.

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# Arbejde med data: Ikke-relationelle data
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/06-NoSQL.png)|

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# Sodavandsfortjenester
## Instruktioner

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# Arbejde med Data: Python og Pandas-biblioteket
| ![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/07-WorkWithPython.png) |

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# Opgave i Databehandling med Python
I denne opgave vil vi bede dig om at uddybe den kode, vi er begyndt at udvikle i vores udfordringer. Opgaven består af to dele:

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# Arbejde med data: Dataklargøring
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/08-DataPreparation.png)|

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# Evaluering af data fra en formular
En kunde har testet en [lille formular](../../../../2-Working-With-Data/08-data-preparation/index.html) for at indsamle nogle grundlæggende oplysninger om deres kundebase. De har givet dig deres resultater for at validere de data, de har indsamlet. Du kan åbne `index.html`-siden i browseren for at se formularen.

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# Arbejde med data
![data love](../../../translated_images/da/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg)

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# Visualisering af mængder
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/09-Visualizing-Quantities.png)|

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# Linjer, Spredningsdiagrammer og Søjler
## Instruktioner

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# Visualisering af fordelinger
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/10-Visualizing-Distributions.png)|

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# Anvend dine færdigheder
## Instruktioner

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# Visualisering af proportioner
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/11-Visualizing-Proportions.png)|

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# Prøv det i Excel
## Instruktioner

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# Visualisering af relationer: Alt om honning 🍯
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/12-Visualizing-Relationships.png)|

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# Dyk ned i bikuben
## Instruktioner

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# Skabe Meningsfulde Visualiseringer
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/13-MeaningfulViz.png)|

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# Byg din egen tilpassede vis
## Instruktioner

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# Dangerous Liaisons datavisualiseringsprojekt
For at komme i gang skal du sikre dig, at du har NPM og Node installeret på din maskine. Installer afhængighederne (npm install), og kør derefter projektet lokalt (npm run serve):

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# Dangerous Liaisons data visualiseringsprojekt
For at komme i gang skal du sikre dig, at du har NPM og Node kørende på din maskine. Installer afhængighederne (npm install) og kør derefter projektet lokalt (npm run serve):

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

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# Linjer, Spredningsdiagrammer og Søjlediagrammer
## Instruktioner

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

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# Anvend dine færdigheder
## Instruktioner

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# Visualisering af proportioner
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# Visualisering af relationer: Alt om honning 🍯
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/12-Visualizing-Relationships.png)|

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# Skabe Meningsfulde Visualiseringer
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/13-MeaningfulViz.png)|

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# Visualiseringer
![en bi på en lavendelblomst](../../../translated_images/da/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg)

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# Introduktion til Data Science Livscyklus
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/14-DataScience-Lifecycle.png)|

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# Vurdering af et datasæt
En klient har henvendt sig til jeres team for hjælp til at undersøge en taxikundes sæsonmæssige forbrugsvaner i New York City.

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# Data Science Livscyklus: Analyse
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/15-Analyzing.png)|

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# Udforskning efter svar
Dette er en fortsættelse af den tidligere lektions [opgave](../14-Introduction/assignment.md), hvor vi kort kiggede på datasættet. Nu vil vi tage et dybere kig på dataene.

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# Data Science Livscyklus: Kommunikation
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/16-Communicating.png)|

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# Fortæl en historie
## Instruktioner

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# Data Science Livscyklus
![kommunikation](../../../translated_images/da/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg)

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# Introduktion til Data Science i Skyen
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/17-DataScience-Cloud.png)|

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# Markedsundersøgelse
## Instruktioner

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# Data Science i skyen: Den "Low code/No code" tilgang
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/18-DataScience-Cloud.png)|

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# Low code/No code Data Science-projekt på Azure ML
## Instruktioner

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# Data Science i skyen: Den "Azure ML SDK" måde
|![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/19-DataScience-Cloud.png)|

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# Data Science-projekt ved brug af Azure ML SDK
## Instruktioner

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# Data Science i skyen
![cloud-picture](../../../translated_images/da/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg)

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# Data Science i den Virkelige Verden
| ![ Sketchnote af [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-RealWorld.png) |

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# Udforsk et Planetary Computer-datasæt
## Instruktioner

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# Data Science i det virkelige liv
Anvendelser af data science i forskellige industrier.

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# AGENTS.md
## Projektoversigt

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# Microsoft Open Source Adfærdskodeks
Dette projekt har vedtaget [Microsoft Open Source Adfærdskodeks](https://opensource.microsoft.com/codeofconduct/).

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# Bidrag til Data Science for Beginners
Tak for din interesse i at bidrage til Data Science for Beginners-kurset! Vi værdsætter bidrag fra fællesskabet.

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# Installationsvejledning
Denne vejledning hjælper dig med at opsætte dit miljø til at arbejde med Data Science for Beginners-kurset.

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# Data Science for Beginners - En læreplan
# Data Science for Beginners - Et Læreplan
[![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)
Azure Cloud Advocates hos Microsoft er glade for at tilbyde en 10-ugers, 20-lektioners læreplan, der handler om Data Science. Hver lektion inkluderer quizzer før og efter lektionen, skriftlige instruktioner til at fuldføre lektionen, en løsning og en opgave. Vores projektbaserede pædagogik giver dig mulighed for at lære, mens du bygger, en bevist måde for nye færdigheder at "sidde fast".
Azure Cloud Advocates hos Microsoft er glade for at tilbyde en 10-ugers, 20-lektions læreplan, der handler om Data Science. Hver lektion inkluderer quizzer før og efter lektionen, skriftlige instruktioner til at gennemføre lektionen, en løsning og en opgave. Vores projektbaserede pædagogik giver dig mulighed for at lære, mens du bygger, en bevist metode til at få nye færdigheder til at "sidde fast".
**Hjertelig tak til vores forfattere:** [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).
**🙏 Særlige tak 🙏 til vores [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) forfattere, anmeldere og indholdsbidragydere,** især 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),
**🙏 Særlige tak 🙏 til vores [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) forfattere, anmeldere og indholdsleverandører,** især 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/da/00-Title.8af36cd35da1ac55.webp)|
|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/da/00-Title.8af36cd35da1ac55.webp)|
|:---:|
| Data Science For Beginners - _Sketchnote af [@nitya](https://twitter.com/nitya)_ |
### 🌐 Understøttelse af flere sprog
### 🌐 Flere Sprog Understøttelse
#### Understøttet via GitHub Action (Automatiseret & Altid Opdateret)
<!-- 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](./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](../it/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](./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](../it/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)
> **Foretrækker du at klone lokalt?**
> Dette repositorium inkluderer 50+ sprogoversættelser, som væsentligt øger downloadstørrelsen. For at klone uden oversættelser, brug sparse checkout:
> Dette repository inkluderer mere end 50 sprogoversættelser, som øger downloadstørrelsen betydeligt. For at klone uden oversættelser, brug sparse checkout:
> ```bash
> git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git
> cd Data-Science-For-Beginners
@ -55,152 +46,152 @@ Azure Cloud Advocates hos Microsoft er glade for at tilbyde en 10-ugers, 20-lekt
> Dette giver dig alt, hvad du behøver for at gennemføre kurset med en meget hurtigere download.
<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
**Hvis du ønsker at få flere oversættelsessprog understøttet, er de listet [her](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
**Hvis du ønsker at få yderligere oversættelsessprog understøttet, er de opført [her](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
#### Deltag i vores fællesskab
#### Deltag i vores fællesskab
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
Vi har en igangværende Discord lær med AI-serie, lær mere og deltag i os på [Learn with AI Series](https://aka.ms/learnwithai/discord) fra 18. - 30. september 2025. Du vil få tips og tricks til brug af GitHub Copilot for Data Science.
Vi har en igangværende Discord lær med AI serie, lær mere og deltag hos [Learn with AI Series](https://aka.ms/learnwithai/discord) fra 18. - 30. september 2025. Du får tips og tricks til brug af GitHub Copilot til Data Science.
![Learn with AI series](../../../../translated_images/da/1.2b28cdc6205e26fe.webp)
![Learn with AI series](../../translated_images/da/1.2b28cdc6205e26fe.webp)
# Er du studerende?
Kom i gang med følgende ressourcer:
- [Student Hub side](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) På denne side finder du begynderressourcer, studenterpakker og endda måder at få en gratis certifikatvoucher på. Dette er en side, du vil bogmærke og tjekke jævnligt, da vi skifter indhold mindst månedligt.
- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Deltag i et globalt fællesskab af studenterambassadører, det kan være din vej ind i Microsoft.
- [Student Hub siden](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) På denne side finder du begynderressourcer, studenterpakker og endda måder at få en gratis certifikatvoucher på. Dette er en side, du ønsker at bogmærke og tjekke fra tid til anden, da vi skifter indhold mindst månedligt.
- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Bliv medlem af et globalt fællesskab af student ambassadors, dette kunne være din vej ind i Microsoft.
# Kom godt i gang
## 📚 Dokumentation
- **[Installationsguide](INSTALLATION.md)** - Trin-for-trin installationsinstruktioner for begyndere
- **[Brugsvejledning](USAGE.md)** - Eksempler og typiske arbejdsgange
- **[Installationsguide](INSTALLATION.md)** - Trin-for-trin opsætningsinstruktioner for begyndere
- **[Brugsvejledning](USAGE.md)** - Eksempler og almindelige arbejdsgange
- **[Fejlfinding](TROUBLESHOOTING.md)** - Løsninger på almindelige problemer
- **[Bidragende guide](CONTRIBUTING.md)** - Sådan bidrager du til dette projekt
- **[For lærere](for-teachers.md)** - Undervisningsvejledning og ressourcer til klasseværelset
- **[Bidragsvejledning](CONTRIBUTING.md)** - Hvordan man bidrager til dette projekt
- **[For undervisere](for-teachers.md)** - Undervisningsvejledning og klasseværelsesressourcer
## 👨‍🎓 For studerende
> **Helt begynder:** Ny inden for data science? Start med vores [begyndervenlige eksempler](examples/README.md)! Disse enkle, godt kommenterede eksempler hjælper dig med at forstå det grundlæggende, før du dykker ned i hele læreplanen.
> **[Studerende](https://aka.ms/student-page)**: for at bruge denne læreplan på egen hånd, lav en fork af hele repoen og gennemfør øvelserne selv, startende med en quiz før forelæsningen. Læs derefter forelæsningen og gennemfør resten af aktiviteterne. Prøv at skabe projekterne ved at forstå lektionerne snarere end at kopiere løsningskoden; dog er denne kode tilgængelig i /solutions mapperne i hver projektorienteret lektion. En anden idé er at danne en studiegruppe med venner og gennemgå indholdet sammen. Til yderligere studie anbefaler vi [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
> **Fuldstændige begyndere**: Ny til data science? Start med vores [begynder-venlige eksempler](examples/README.md)! Disse simple, velkommenterede eksempler vil hjælpe dig med at forstå det grundlæggende, før du går i dybden med hele læreplanen.
> **[Studerende](https://aka.ms/student-page)**: for at bruge denne læreplan på egen hånd, forgrene hele repo'et og gennemfør øvelserne på egen hånd, startende med en quiz før forelæsningen. Læs derefter forelæsningen og gennemfør resten af aktiviteterne. Prøv at skabe projekterne ved at forstå lektionerne frem for at kopiere løsningskoden; denne kode findes dog i /solutions mapperne i hver projektorienteret lektion. En anden idé er at danne en studiegruppe med venner og gennemgå indholdet sammen. Til yderligere studie anbefaler vi [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
**Hurtig start:**
1. Tjek [Installationsguiden](INSTALLATION.md) for at sætte dit miljø op
2. Gennemgå [Brugsvejledning](USAGE.md) for at lære, hvordan du arbejder med læreplanen
3. Start med lektion 1 og arbejd dig igennem sekventielt
4. Deltag i vores [Discord fællesskab](https://aka.ms/ds4beginners/discord) for support
2. Gennemgå [Brugsvejledningen](USAGE.md) for at lære, hvordan du arbejder med læreplanen
3. Start med Lektion 1 og arbejd dig sekventielt igennem
4. Deltag i vores [Discord-fællesskab](https://aka.ms/ds4beginners/discord) for support
## 👩‍🏫 For lærere
> **Lærere**: vi har [inkluderet nogle forslag](for-teachers.md) til, hvordan denne læreplan kan bruges. Vi vil meget gerne have din feedback [i vores diskussionsforum](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
## 👩‍🏫 For undervisere
> **Undervisere**: vi har [inkluderet nogle forslag](for-teachers.md) til, hvordan man bruger denne læreplan. Vi vil meget gerne have din feedback [i vores diskussionsforum](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
## Mød teamet
[![Promo video](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Promo video")
**Gif af** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal)
> 🎥 Klik på billedet ovenfor for en video om projektet og folkene, der har skabt det!
> 🎥 Klik på billedet ovenfor for en video om projektet og de personer, der skabte det!
## Pædagogik
Vi har valgt to pædagogiske principper, mens vi byggede dette pensum: at sikre, at det er projektbaseret, og at det indeholder hyppige quizzer. Ved slutningen af denne serie vil eleverne have lært grundlæggende principper inden for datavidenskab, herunder etiske begreber, dataklargøring, forskellige måder at arbejde med data på, datavisualisering, dataanalyse, virkelige anvendelse af datavidenskab og mere.
Vi har valgt to pædagogiske principper, mens vi byggede denne læseplan: at sikre, at den er projektbaseret, og at den inkluderer hyppige quizzer. Ved slutningen af denne serie vil eleverne have lært grundlæggende principper for data science, inklusive etiske koncepter, datapreparation, forskellige måder at arbejde med data på, datavisualisering, dataanalyse, virkelige anvendelsestilfælde af data science og meget mere.
Derudover sætter en lavrisiko quiz før en klasse elevens intention mod at lære et emne, mens en anden quiz efter klassen sikrer yderligere fastholdelse. Dette pensum er designet til at være fleksibelt og sjovt og kan tages helt eller delvist. Projekterne starter små og bliver gradvist mere komplekse i løbet af den 10-ugers cyklus.
Derudover sætter en lavrisiko-quiz før en klasse elevens intention mod at lære et emne, mens en anden quiz efter klassen sikrer yderligere fastholdelse. Denne læseplan er designet til at være fleksibel og sjov, og kan tages i sin helhed eller delvist. Projekterne starter småt og bliver gradvist mere komplekse mod slutningen af den 10-ugers cyklus.
> Find vores [Adfærdskodeks](CODE_OF_CONDUCT.md), [Bidragning](CONTRIBUTING.md), [Oversættelse](TRANSLATIONS.md) retningslinjer. Vi byder dine konstruktive tilbagemeldinger velkommen!
> Find vores [Adfærdskodeks](CODE_OF_CONDUCT.md), [Bidrag](CONTRIBUTING.md), [Oversættelse](TRANSLATIONS.md) retningslinjer. Vi byder konstruktiv feedback velkommen!
## Hver lektion inkluderer:
- Valgfri sketchnote
- Valgfri skitsenote
- Valgfri supplerende video
- Opvarmningsquiz før lektionen
- Skriftlig lektion
- For projektbaserede lektioner, trin-for-trin vejledninger til, hvordan man bygger projektet
- For-lesson opvarmningsquiz
- Skreven lektion
- For projektbaserede lektioner, trin-for-trin guider til, hvordan man bygger projektet
- Videnstjek
- En udfordring
- Supplerende læsning
- Opgave
- [Quiz efter lektionen](https://ff-quizzes.netlify.app/en/)
- [Post-lesson quiz](https://ff-quizzes.netlify.app/en/)
> **En note om quizzer**: Alle quizzer findes i Quiz-App-mappen, med i alt 40 quizzer med tre spørgsmål hver. De er linket fra lektionerne, men quiz-appen kan køres lokalt eller deployeres til Azure; følg instruktionerne i `quiz-app` mappen. De bliver gradvist oversat.
> **En note om quizzer**: Alle quizzer findes i Quiz-App mappen, med i alt 40 quizzer med tre spørgsmål hver. De er linket fra lektionerne, men quiz-appen kan køre lokalt eller implementeres til Azure; følg instruktionerne i `quiz-app` mappen. De bliver gradvist lokaliseret.
## 🎓 Begynder-venlige eksempler
**Ny til datavidenskab?** Vi har oprettet et specielt [eksempelbibliotek](examples/README.md) med enkel, velkommenteret kode for at hjælpe dig i gang:
**Ny til Data Science?** Vi har lavet en speciel [eksempelmapppe](examples/README.md) med simpel, velkommenteret kode for at hjælpe dig i gang:
- 🌟 **Hello World** - Dit første datavidenskabsprogram
- 🌟 **Hello World** - Dit første data science program
- 📂 **Indlæsning af data** - Lær at læse og udforske datasæt
- 📊 **Simpel analyse** - Beregn statistik og find mønstre
- 📈 **Grundlæggende visualisering** - Lav diagrammer og grafer
- 🔬 **Virkeligt projekt** - Fuld arbejdsgang fra start til slut
- 📈 **Grundlæggende visualisering** - Skab diagrammer og grafer
- 🔬 **Virkelighedsnært projekt** - Fuld arbejdsproces fra start til slut
Hvert eksempel indeholder detaljerede kommentarer, der forklarer hvert trin, hvilket gør det perfekt til absolutte begyndere!
Hvert eksempel inkluderer detaljerede kommentarer, der forklarer hvert trin, hvilket gør det perfekt for absolutte begyndere!
👉 **[Start med eksemplerne](examples/README.md)** 👈
## Lektioner
|![ Sketchnote af @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/da/00-Roadmap.4905d6567dff4753.webp)|
|![ Sketchnote af @sketchthedocs https://sketchthedocs.dev](../../translated_images/da/00-Roadmap.4905d6567dff4753.webp)|
|:---:|
| Data Science For Beginners: Roadmap - _Sketchnote af [@nitya](https://twitter.com/nitya)_ |
| Lektion Nummer | Emne | Lektion Gruppereing | Læringsmål | Linket Lektion | Forfatter |
| Lektion nummer | Emne | Lektion gruppe | Læringsmål | Linket lektion | Forfatter |
| :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: |
| 01 | Definition af datavidenskab | [Introduktion](1-Introduction/README.md) | Lær grundlæggende begreber bag datavidenskab og hvordan det relaterer sig til kunstig intelligens, maskinlæring og big data. | [lektion](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) |
| 02 | Etik i datavidenskab | [Introduktion](1-Introduction/README.md) | Begreber, udfordringer og rammer for dataetik. | [lektion](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) |
| 03 | Definition af data | [Introduktion](1-Introduction/README.md) | Hvordan data klassificeres og dets almindelige kilder. | [lektion](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) |
| 04 | Introduktion til statistik & sandsynlighed | [Introduktion](1-Introduction/README.md) | Matematiske teknikker inden for sandsynlighed og statistik til at forstå data. | [lektion](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) |
| 01 | Definering af Data Science | [Introduktion](1-Introduction/README.md) | Lær de grundlæggende koncepter bag data science og hvordan det relaterer sig til kunstig intelligens, maskinlæring og big data. | [lektion](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) |
| 02 | Data Science etik | [Introduktion](1-Introduction/README.md) | Dataetik koncepter, udfordringer og rammer. | [lektion](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) |
| 03 | Definering af Data | [Introduktion](1-Introduction/README.md) | Hvordan data klassificeres og dets almindelige kilder. | [lektion](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) |
| 04 | Introduktion til statistik & sandsynlighed | [Introduktion](1-Introduction/README.md) | Matematiske teknikker inden for sandsynlighed og statistik til forståelse af data. | [lektion](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) |
| 05 | Arbejde med relationelle data | [Arbejde med data](2-Working-With-Data/README.md) | Introduktion til relationelle data og grundlæggende udforskning og analyse af relationelle data med Structured Query Language, også kendt som SQL (udtales “see-quell”). | [lektion](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | |
| 06 | Arbejde med NoSQL-data | [Arbejde med data](2-Working-With-Data/README.md) | Introduktion til ikke-relationelle data, deres forskellige typer og grundlæggende udforskning og analyse af dokumentdatabaser. | [lektion](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)|
| 07 | Arbejde med Python | [Arbejde med data](2-Working-With-Data/README.md) | Grundlæggende brug af Python til dataudforskning med biblioteker som Pandas. En grundlæggende forståelse af Python programmering anbefales. | [lektion](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) |
| 08 | Dataklargøring | [Arbejde med data](2-Working-With-Data/README.md) | Emner om datateknikker til rengøring og omdannelse af data for at håndtere udfordringer med manglende, unøjagtige eller ufuldstændige data. | [lektion](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) |
| 09 | Visualisering af mængder | [Datavisualisering](3-Data-Visualization/README.md) | Lær hvordan du bruger Matplotlib til at visualisere fugledata 🦆 | [lektion](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) |
| 10 | Visualisering af datadistributioner | [Datavisualisering](3-Data-Visualization/README.md) | Visualisering af observationer og trends inden for et interval. | [lektion](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) |
| 11 | Visualisering af forhold | [Datavisualisering](3-Data-Visualization/README.md) | Visualisering af diskrete og grupperede procenter. | [lektion](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) |
| 12 | Visualisering af relationer | [Datavisualisering](3-Data-Visualization/README.md) | Visualisering af forbindelser og korrelationer mellem datasæt og deres variable. | [lektion](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) |
| 06 | Arbejde med NoSQL data | [Arbejde med data](2-Working-With-Data/README.md) | Introduktion til ikke-relationelle data, dens forskellige typer og grundlæggende udforskning og analyse af dokumentdatabaser. | [lektion](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)|
| 07 | Arbejde med Python | [Arbejde med data](2-Working-With-Data/README.md) | Grundlæggende brug af Python til dataudforskning med biblioteker som Pandas. Grundlæggende forståelse af Python programmering anbefales. | [lektion](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) |
| 08 | Datapreparation | [Arbejde med data](2-Working-With-Data/README.md) | Emner om datateknikker til rengøring og omdannelse af data for at håndtere udfordringer med manglende, unøjagtige eller ufuldstændige data. | [lektion](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) |
| 09 | Visualisering af mængder | [Datavisualisering](3-Data-Visualization/README.md) | Lær at bruge Matplotlib til at visualisere fugledata 🦆 | [lektion](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) |
| 10 | Visualisering af datadistributioner | [Datavisualisering](3-Data-Visualization/README.md) | Visualisering af observationer og tendenser inden for et interval. | [lektion](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) |
| 11 | Visualisering af procenter | [Datavisualisering](3-Data-Visualization/README.md) | Visualisering af diskrete og grupperede procenter. | [lektion](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) |
| 12 | Visualisering af relationer | [Datavisualisering](3-Data-Visualization/README.md) | Visualisering af forbindelser og korrelationer mellem datasæt og deres variabler. | [lektion](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) |
| 13 | Meningsfulde visualiseringer | [Datavisualisering](3-Data-Visualization/README.md) | Teknikker og vejledning til at gøre dine visualiseringer værdifulde for effektiv problemløsning og indsigt. | [lektion](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) |
| 14 | Introduktion til datavidenskabslivscyklussen | [Livscyklus](4-Data-Science-Lifecycle/README.md) | Introduktion til datavidenskabslivscyklussen og dets første trin med erhvervelse og udvinding af data. | [lektion](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) |
| 15 | Analyse | [Livscyklus](4-Data-Science-Lifecycle/README.md) | Denne fase i datavidenskabslivscyklussen fokuserer på teknikker til at analysere data. | [lektion](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | |
| 16 | Kommunikation | [Livscyklus](4-Data-Science-Lifecycle/README.md) | Denne fase i datavidenskabslivscyklussen fokuserer på at præsentere indsigt fra data på en måde, der gør det lettere for beslutningstagere at forstå. | [lektion](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | |
| 17 | Datavidenskab i skyen | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Denne serie lektioner introducerer datavidenskab i skyen og dens fordele. | [lektion](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) og [Maud](https://twitter.com/maudstweets) |
| 18 | Datavidenskab i skyen | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Træning af modeller ved brug af Low Code værktøjer. |[lektion](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) og [Maud](https://twitter.com/maudstweets) |
| 19 | Datavidenskab i skyen | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Implementering af modeller med Azure Machine Learning Studio. | [lektion](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) og [Maud](https://twitter.com/maudstweets) |
| 20 | Datavidenskab i praksis | [In the Wild](6-Data-Science-In-Wild/README.md) | Datavidenskabsdrevede projekter i den virkelige verden. | [lektion](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) |
| 14 | Introduktion til Data Science livscyklus | [Livscyklus](4-Data-Science-Lifecycle/README.md) | Introduktion til data science livscyklus og dets første trin med at erhverve og udtrække data. | [lektion](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) |
| 15 | Analyse | [Livscyklus](4-Data-Science-Lifecycle/README.md) | Denne fase af data science livscyklussen fokuserer på teknikker til at analysere data. | [lektion](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | |
| 16 | Kommunikation | [Livscyklus](4-Data-Science-Lifecycle/README.md) | Denne fase af data science livscyklussen fokuserer på at præsentere indsigt fra data på en måde, som gør det nemmere for beslutningstagere at forstå. | [lektion](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | |
| 17 | Data Science i skyen | [Skydata](5-Data-Science-In-Cloud/README.md) | Denne serie lektioner introducerer data science i skyen og dens fordele. | [lektion](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) og [Maud](https://twitter.com/maudstweets) |
| 18 | Data Science i skyen | [Skydata](5-Data-Science-In-Cloud/README.md) | Træning af modeller med Low Code værktøjer. |[lektion](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) og [Maud](https://twitter.com/maudstweets) |
| 19 | Data Science i skyen | [Skydata](5-Data-Science-In-Cloud/README.md) | Udrulning af modeller med Azure Machine Learning Studio. | [lektion](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) og [Maud](https://twitter.com/maudstweets) |
| 20 | Data Science i det fri | [I det fri](6-Data-Science-In-Wild/README.md) | Data science drevne projekter i den virkelige verden. | [lektion](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) |
## GitHub Codespaces
Følg disse trin for at åbne dette eksempel i en Codespace:
1. Klik på Code dropdown-menuen og vælg Open with Codespaces option.
2. Vælg + New codespace nederst i panelet.
1. Klik på Code drop-down menuen og vælg mulighederne Åbn med Codespaces.
2. Vælg + Ny codespace nederst i panelet.
For mere info, se [GitHub dokumentationen](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace).
## VSCode Remote - Containers
Følg disse trin for at åbne dette repo i en container ved at bruge din lokale maskine og VSCode med VS Code Remote - Containers udvidelsen:
## VSCode Remote - Containere
Følg disse trin for at åbne dette repositorium i en container ved hjælp af din lokale maskine og VSCode ved hjælp af VS Code Remote - Containers udvidelsen:
1. Hvis dette er første gang du bruger en udviklingscontainer, skal du sikre dig, at dit system opfylder forudsætningerne (fx Docker installeret) i [komm-i-gang-dokumentationen](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started).
1. Hvis dette er første gang du bruger en udviklingscontainer, skal du sikre, at dit system opfylder forudsætningerne (f.eks. at Docker er installeret) i [kom godt i gang dokumentationen](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started).
For at bruge dette repository kan du enten åbne repod i et isoleret Docker-volume:
For at bruge dette repositorium kan du enten åbne repositoriet i et isoleret Docker-volumen:
**Bemærk**: Under motorhjelmen bruges kommandoen Remote-Containers: **Clone Repository in Container Volume...** til at klone koden i et Docker-volume i stedet for lokalt filsystem. [Volumes](https://docs.docker.com/storage/volumes/) er den foretrukne mekanisme til at bevare containerdata.
**Bemærk**: Under motorhjelmen vil dette bruge Remote-Containers: **Clone Repository in Container Volume...** kommandoen til at klone kildekoden i et Docker-volumen i stedet for det lokale filsystem. [Volumener](https://docs.docker.com/storage/volumes/) er den foretrukne mekanisme til at bevare containerdata.
Eller åbne en lokalt klonet eller downloadet version af repot:
Eller åbne en lokalt klonet eller downloadet version af repositoriet:
- Klon dette repository til dit lokale filsystem.
- Tryk F1 og vælg kommandoen **Remote-Containers: Open Folder in Container...**.
- Vælg den klonede kopi af denne mappe, vent på at containeren starter, og prøv tingene.
- Klon dette repositorium til dit lokale filsystem.
- Tryk på F1 og vælg kommandoen **Remote-Containers: Open Folder in Container...**.
- Vælg den klonede kopi af denne mappe, vent på at containeren starter, og prøv tingene af.
## Offline adgang
Du kan køre denne dokumentation offline ved at bruge [Docsify](https://docsify.js.org/#/). Fork dette repo, [installer Docsify](https://docsify.js.org/#/quickstart) på din lokale maskine, og i rodbiblioteket af dette repo, skriv `docsify serve`. Websitet vil blive serveret på port 3000 på din localhost: `localhost:3000`.
Du kan køre denne dokumentation offline ved at bruge [Docsify](https://docsify.js.org/#/). Fork dette repositorium, [installer Docsify](https://docsify.js.org/#/quickstart) på din lokale maskine, skriv derefter i rodmappen af dette repositorium `docsify serve`. Websitet vil blive serveret på port 3000 på din localhost: `localhost:3000`.
> Bemærk, notebooks bliver ikke gengivet via Docsify, så når du skal køre en notebook, gør det separat i VS Code med en Python kernel.
> Bemærk, at notebooks ikke bliver gengivet via Docsify, så når du skal køre en notebook, skal det gøres separat i VS Code med en Python-kernel.
## Andre pensum
## Andre læseplaner
Vores team producerer andre pensum! Tag et kig på:
Vores team producerer andre læseplaner! Tjek:
<!-- CO-OP TRANSLATOR OTHER COURSES START -->
### LangChain
@ -217,7 +208,7 @@ Vores team producerer andre pensum! Tag et kig på:
---
### Generativ AI-serie
### Generativ AI Serie
[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
@ -236,27 +227,27 @@ Vores team producerer andre pensum! Tag et kig på:
---
### Copilot-serie
### Copilot Serie
[![Copilot for AI Paired Programming](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 for 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)
[![Copilot Adventure](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 -->
## Få hjælp
## Få Hjælp
**Oplever du problemer?** Se vores [Fejlfinding Guide](TROUBLESHOOTING.md) for løsninger på almindelige problemer.
Hvis du sidder fast eller har spørgsmål om at bygge AI-apps. Deltag i diskussioner om MCP med medlærende og erfarne udviklere. Det er et støttende fællesskab, hvor spørgsmål er velkomne, og viden deles frit.
Hvis du sidder fast eller har spørgsmål om at bygge AI-apps. Deltag sammen med andre lærende og erfarne udviklere i diskussioner om MCP. Det er et støttende fællesskab, hvor spørgsmål er velkomne, og viden deles frit.
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
Hvis du har produktfeedback eller fejl under udviklingen, besøg:
Hvis du har feedback på produktet eller fejler under opbygning, besøg:
[![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 -->
**Ansvarsfraskrivelse**:
Dette dokument er blevet oversat ved hjælp af AI-oversættelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selvom vi bestræber os på nøjagtighed, bedes du være opmærksom på, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det oprindelige dokument på dets modersmål bør betragtes som den autoritative kilde. For kritisk information anbefales professionel human oversættelse. Vi påtager os intet ansvar for misforståelser eller fejltolkninger, der måtte opstå som følge af brugen af denne oversættelse.
**Ansvarsfraskrivelse**:
Dette dokument er blevet oversat ved hjælp af AI-oversættelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selvom vi bestræber os på nøjagtighed, bedes du være opmærksom på, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det originale dokument på dets modersmål bør betragtes som den autoritative kilde. For kritisk information anbefales professionel menneskelig oversættelse. Vi er ikke ansvarlige for eventuelle misforståelser eller fejltolkninger, der opstår som følge af brugen af denne oversættelse.
<!-- CO-OP TRANSLATOR DISCLAIMER END -->

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## Sikkerhed
Microsoft tager sikkerheden af vores softwareprodukter og -tjenester alvorligt, hvilket inkluderer alle kildekoderepositorier, der administreres gennem vores GitHub-organisationer, som inkluderer [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) og [vores GitHub-organisationer](https://opensource.microsoft.com/).

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# Support
## Sådan indberetter du problemer og får hjælp

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# Fejlfindingsguide
Denne guide giver løsninger på almindelige problemer, du kan støde på, mens du arbejder med Data Science for Beginners-kurset.

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# Brugsvejledning
Denne vejledning giver eksempler og almindelige arbejdsgange til brug af Data Science for Beginners-kurset.

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- Introduktion
- [Definition af Data Science](../1-Introduction/01-defining-data-science/README.md)
- [Etik i Data Science](../1-Introduction/02-ethics/README.md)

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# Begynder-venlige Data Science Eksempler
Velkommen til eksempelmappen! Denne samling af enkle, velkommenterede eksempler er designet til at hjælpe dig i gang med data science, selv hvis du er helt nybegynder.

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## For undervisere
Vil du gerne bruge dette pensum i dit klasseværelse? Vær så god!

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# Quizzer
Disse quizzer er før- og efterforelæsningsquizzer for data science-kurset på https://aka.ms/datascience-beginners

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Find alle sketchnotes her!
## Kreditering

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# Määritelmä: Tietojenkäsittelytiede
| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/01-Definitions.png) |

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# Tehtävä: Tieteenalojen skenaariot
Tässä ensimmäisessä tehtävässä pyydämme sinua pohtimaan joitakin tosielämän prosesseja tai ongelmia eri ongelma-alueilla ja sitä, kuinka voit parantaa niitä käyttämällä tieteenalan prosessia. Mieti seuraavia kysymyksiä:

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# Tehtävä: Data Science -skenaariot
Tässä ensimmäisessä tehtävässä pyydämme sinua pohtimaan joitakin tosielämän prosesseja tai ongelmia eri ongelma-alueilla ja sitä, miten voit parantaa niitä Data Science -prosessin avulla. Mieti seuraavia kysymyksiä:

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# Johdanto datan etiikkaan
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/02-Ethics.png)|

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## Kirjoita Tapaustutkimus Dataetiikasta
## Ohjeet

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

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# Datan luokittelu
## Ohjeet

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# Tilastotiede ja todennäköisyys: Lyhyt johdanto
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/04-Statistics-Probability.png)|
@ -64,7 +55,7 @@ Datan jakauman ymmärtämiseksi on hyödyllistä puhua **kvartiileista**:
Graafisesti voimme esittää mediaanin ja kvartiilien suhteen diagrammissa, jota kutsutaan **laatikko- ja viiksikaavioksi**:
<img src="images/boxplot_explanation.png" alt="Laatikko- ja viiksikaavio" width="50%">
<img src="../../../../translated_images/fi/boxplot_explanation.4039b7de08780fd4.webp" alt="Laatikko- ja viiksikaavio" width="50%">
Tässä laskemme myös **kvartiilivälin** IQR=Q3-Q1 ja niin sanotut **poikkeamat** – arvot, jotka ovat alueen [Q1-1.5*IQR, Q3+1.5*IQR] ulkopuolella.

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# Pieni diabetes-tutkimus
Tässä tehtävässä työskentelemme pienen diabetespotilaiden datasetin kanssa, joka on otettu [täältä](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).

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# Johdatus Data Scienceen
![data käytännössä](../../../translated_images/fi/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg)

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# Työskentely datan kanssa: Relaatiotietokannat
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/05-RelationalData.png)|

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# Lentokenttätietojen näyttäminen
Sinulle on annettu [tietokanta](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db), joka on rakennettu [SQLite](https://sqlite.org/index.html) -alustalle ja sisältää tietoa lentokentistä. Tietokannan rakenne on esitetty alla. Käytät [SQLite-laajennusta](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) [Visual Studio Code](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum) -ohjelmassa näyttääksesi tietoa eri kaupunkien lentokentistä.

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# Työskentely datan kanssa: Ei-relationaalinen data
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# Virvoitusjuomien Voitot
## Ohjeet

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# Työskentely datan kanssa: Python ja Pandas-kirjasto
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# Tehtävä: Datan käsittely Pythonilla
Tässä tehtävässä pyydämme sinua jatkamaan koodin kehittämistä, jota olemme aloittaneet haasteissamme. Tehtävä koostuu kahdesta osasta:

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# Työskentely datan kanssa: Datan valmistelu
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/08-DataPreparation.png)|

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# Lomakkeen tietojen arviointi
Asiakas on testannut [pientä lomaketta](../../../../2-Working-With-Data/08-data-preparation/index.html) kerätäkseen perustietoja asiakaskunnastaan. He ovat tuoneet sinulle keräämänsä tiedot, jotta voit validoida ne. Voit avata `index.html`-sivun selaimessa nähdäksesi lomakkeen.

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# Työskentely datan kanssa
![data love](../../../translated_images/fi/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg)

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# Määrien visualisointi
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/09-Visualizing-Quantities.png)|

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# Viivat, hajontakaaviot ja pylväät
## Ohjeet

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# Visualisoi jakaumat
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/10-Visualizing-Distributions.png)|

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# Käytä taitojasi
## Ohjeet

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# Visualisoi osuuksia
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/11-Visualizing-Proportions.png)|

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# Kokeile Excelissä
## Ohjeet

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# Suhteiden visualisointi: Kaikki hunajasta 🍯
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/12-Visualizing-Relationships.png)|

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# Sukellus mehiläispesään
## Ohjeet

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# Merkityksellisten Visualisointien Luominen
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/13-MeaningfulViz.png)|

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