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# Andmeteaduse määratlemine
| ![ Sketchnote autorilt [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/01-Definitions.png) |

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# Ülesanne: Andmeteaduse stsenaariumid
Selles esimeses ülesandes palume teil mõelda mõnele päriselulisele protsessile või probleemile erinevates valdkondades ja sellele, kuidas saaksite seda parandada andmeteaduse protsessi abil. Mõelge järgmistele küsimustele:

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# Ülesanne: Andmeteaduse stsenaariumid
Selles esimeses ülesandes palume teil mõelda mõnele päriselulisele protsessile või probleemile erinevates valdkondades ja sellele, kuidas saaksite seda parandada andmeteaduse protsessi abil. Mõelge järgmistele punktidele:

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# Sissejuhatus andme-eetikasse
|![ Sketchnote autorilt [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/02-Ethics.png)|

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## Kirjuta andme-eetika juhtumiuuring
## Juhised

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

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# Andmekogumite klassifitseerimine
## Juhised

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# Lühike sissejuhatus statistikasse ja tõenäosusteooriasse
|![ Sketchnote autorilt [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/04-Statistics-Probability.png)|

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# Väike Diabeediuuring
Selles ülesandes töötame väikese diabeedipatsientide andmestikuga, mis on võetud [siit](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).

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# Sissejuhatus andmeteadusesse
![andmed tegevuses](../../../translated_images/et/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg)

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# Andmetega töötamine: relatsioonandmebaasid
|![ Sketchnote autorilt [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/05-RelationalData.png)|

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# Lennujaamade andmete kuvamine
Teile on antud [andmebaas](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db), mis on loodud [SQLite](https://sqlite.org/index.html) abil ja sisaldab teavet lennujaamade kohta. Skeem on allpool kuvatud. Kasutate [SQLite laiendust](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) [Visual Studio Code'is](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum), et kuvada teavet erinevate linnade lennujaamade kohta.

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# Töötamine andmetega: Mitte-relatsioonilised andmed
|![ Sketchnote autorilt [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/06-NoSQL.png)|

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# Soda Kasumid
## Juhised

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# Töötamine andmetega: Python ja Pandas teek
| ![ Sketchnote autorilt [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/07-WorkWithPython.png) |

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# Ülesanne: Andmetöötlus Pythonis
Selles ülesandes palume teil täiendada koodi, mida oleme väljakutsetes alustanud. Ülesanne koosneb kahest osast:

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# Töötamine andmetega: Andmete ettevalmistamine
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/08-DataPreparation.png)|

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# Vormilt andmete hindamine
Klient on testinud [väikest vormi](../../../../2-Working-With-Data/08-data-preparation/index.html), et koguda oma kliendibaasi kohta põhiandmeid. Nad on toonud oma tulemused teie juurde, et valideerida kogutud andmeid. Saate avada `index.html` lehe brauseris, et vormi vaadata.

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# Andmetega töötamine
![andmete armastus](../../../translated_images/et/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg)

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

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# Jooned, hajusdiagrammid ja tulpdiagrammid
## Juhised

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# Jaotuste visualiseerimine
|![ Sketchnote autorilt [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/10-Visualizing-Distributions.png)|

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# Rakenda oma oskusi
## Juhised

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

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# Proovi Excelis
## Juhised

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# Suhete visualiseerimine: Kõik mee kohta 🍯
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/12-Visualizing-Relationships.png)|

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# Sukeldumine mesitarusse
## Juhised

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# Tähendusrikaste visualisatsioonide loomine
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/13-MeaningfulViz.png)|

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# Loo oma kohandatud visualisatsioon
## Juhised

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# Ohtlike suhete andmete visualiseerimise projekt
Alustamiseks veendu, et NPM ja Node töötavad sinu arvutis. Paigalda sõltuvused (npm install) ja käivita projekt kohalikult (npm run serve):

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# Ohtlike suhete andmete visualiseerimise projekt
Alustamiseks veendu, et NPM ja Node töötavad sinu arvutis. Paigalda sõltuvused (npm install) ja käivita projekt kohalikult (npm run serve):

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

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# Jooned, hajusdiagrammid ja tulpdiagrammid
## Juhised

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

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# Rakenda oma oskusi
## Juhised

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

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# Suhete visualiseerimine: Kõik mesist 🍯
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/12-Visualizing-Relationships.png)|

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# Tähendusrikaste visualisatsioonide loomine
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/13-MeaningfulViz.png)|

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# Visualisatsioonid
![mesilane lavendlil](../../../translated_images/et/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg)

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# Sissejuhatus andmeteaduse elutsüklisse
|![ Sketchnote autorilt [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/14-DataScience-Lifecycle.png)|

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# Andmehulga hindamine
Klient on pöördunud teie meeskonna poole, et uurida taksoklientide hooajalisi kulutamisharjumusi New Yorgis.

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# Andmeteaduse elutsükkel: Analüüsimine
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/15-Analyzing.png)|

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# Vastuste otsimine
See on jätk eelmise tunni [ülesandele](../14-Introduction/assignment.md), kus vaatasime andmekogumit põgusalt. Nüüd uurime andmeid põhjalikumalt.

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# Andmeteaduse elutsükkel: Kommunikatsioon
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/16-Communicating.png)|

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# Räägi lugu
## Juhised

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# Andmeteaduse elutsükkel
![kommunikatsioon](../../../translated_images/et/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg)

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# Sissejuhatus andmeteadusesse pilves
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/17-DataScience-Cloud.png)|

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# Turu-uuring
## Juhised

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# Andmeteadus pilves: "Vähe koodi/Ilma koodita" lähenemine
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/18-DataScience-Cloud.png)|

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# Madala koodi/Ilma koodita andmeteaduse projekt Azure ML-is
## Juhised

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# Andmeteadus pilves: "Azure ML SDK" meetod
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/19-DataScience-Cloud.png)|

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# Andmeteaduse projekt Azure ML SDK-ga
## Juhised

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# Andmeteadus pilves
![cloud-picture](../../../translated_images/et/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg)

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# Andmeteadus päriselus
| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-RealWorld.png) |

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# Uuri Planetary Computer andmehulka
## Juhised

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# Andmeteadus päriselus
Andmeteaduse rakendused erinevates tööstusharudes.

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# AGENTS.md
## Projekti Ülevaade

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# Microsofti avatud lähtekoodi käitumisjuhend
See projekt on omaks võtnud [Microsofti avatud lähtekoodi käitumisjuhendi](https://opensource.microsoft.com/codeofconduct/).

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# Panustamine algajate andmeteaduse projekti
Täname, et olete huvitatud panustamisest algajate andmeteaduse õppekavasse! Me tervitame kogukonna panuseid.

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# Paigaldusjuhend
See juhend aitab teil seadistada oma keskkonda, et töötada algajatele mõeldud andmeteaduse õppekavaga.

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# Andmeteadus algajatele – õppekava
# Andmeteadus algajatele - Õppekava
[![Ava GitHub Codespacesis](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198)
[![GitHub litsents](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE)
[![GitHub kaasautorid](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/)
[![GitHub panustajad](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/)
[![GitHub probleemid](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/)
[![GitHub tõmbepäringud](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/)
[![Tõmbepäringud on teretulnud](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
[![GitHub jälgijad](https://img.shields.io/github/watchers/microsoft/Data-Science-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/Data-Science-For-Beginners/watchers/)
[![GitHub forksid](https://img.shields.io/github/forks/microsoft/Data-Science-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/Data-Science-For-Beginners/network/)
[![GitHub hargnemised](https://img.shields.io/github/forks/microsoft/Data-Science-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/Data-Science-For-Beginners/network/)
[![GitHub tähed](https://img.shields.io/github/stars/microsoft/Data-Science-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/Data-Science-For-Beginners/stargazers/)
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
[![Microsoft Foundry arendajate foorum](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)
[![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)
Microsofti Azure pilveesindajad pakuvad 10-nädalast, 20-õpetusega õppekava, mis on pühendatud andmeteadusele. Iga õppetund sisaldab eel- ja järeltunni viktoriine, kirjeldatud juhiseid õppetunni sooritamiseks, lahenduse ning ülesande. Meie projektipõhine õpetamismeetod võimaldab õppida koos ehitamisega, mis on tõestatud viis uute oskuste kinnistamiseks.
Microsofti Azure pilvmeeskond on rõõmus pakkuda 10-nädalast, 20-õppetunniga õppekava, mis käsitleb andmeteadust. Iga õppetund sisaldab eeltundi ja järeltundi katseid, kirjalikke juhiseid õppetunni lõpetamiseks, lahendust ja ülesannet. Meie projektipõhine pedagoogika võimaldab õppida ehitades, mis on tõestatud viis uute oskuste kinnistamiseks.
**Südamlikud tänud meie autoritele:** [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).
**Sügav tänu meie autoritele:** [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).
**🙏 Erilised tänud 🙏 meie [Microsofti tudengisaadikute](https://studentambassadors.microsoft.com/) autoritele, ülevaatajate ja sisuloojatele,** eelkõige 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),
**🙏 Eriline tänu 🙏 meie [Microsofti üliõpilasambassadöridele](https://studentambassadors.microsoft.com/) autoritele, retsensentidele ja sisuloojatele,** oluliselt 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 autor @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/et/00-Title.8af36cd35da1ac55.webp)|
|![Sketchnote autor @sketchthedocs https://sketchthedocs.dev](../../translated_images/et/00-Title.8af36cd35da1ac55.webp)|
|:---:|
| Andmeteadus algajatele – _sketchnote autorilt [@nitya](https://twitter.com/nitya)_ |
| Andmeteadus algajatele - _Sketchnote autorilt [@nitya](https://twitter.com/nitya)_ |
### 🌐 Mitmekeelsuse tugi
#### Töötletud GitHub Action abil (automatiseeritud ja alati ajakohane)
#### Toetatud GitHub Action abil (automatiseeritud ja alati ajakohane)
<!-- 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](./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](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](./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)
> **Eelistate kloonida lokaalselt?**
> **Eelistad kloonida kohalikult?**
> See hoidla sisaldab 50+ keele tõlkeid, mis suurendavad oluliselt allalaaditavat suurust. Tõlgeteta kloonimiseks kasutage erilist sparse checkout’i:
> See hoidla sisaldab üle 50 keele tõlked, mis suurendavad märkimisväärselt allalaadimise suurust. Tõlgeteta kloonimiseks kasuta hõredat checkouti:
> ```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'
> ```
> See annab teile kõik vajaliku kursuse lõpetamiseks palju kiirema allalaadimisega.
> Saad kõike vajalikku kursuse läbimiseks palju kiiremalt.
<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
**Kui soovite, et lisaks oleks toetatud rohkem tõlkekeeli, on need loetletud [siin](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
**Kui soovid, et toetataks täiendavaid tõlkekeeli, siis need on loetletud [siin](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
#### Liitu meie kogukonnaga
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
Meil on käimas Discordi õppesari „Õpi koos AI-ga“, lisateabe ja osalemise leiate aadressilt [Õpi koos AI-ga sari](https://aka.ms/learnwithai/discord) ajavahemikul 18.–30. september 2025. Saate näpunäiteid ja nippe GitHub Copiloti kasutamiseks andmeteaduses.
Meil on käimas Discordi õppesari AI-ga, rohkem infot ja liitumiseks külasta [Õpi AI-ga sarja](https://aka.ms/learnwithai/discord) 18.-30. septembril 2025. Saad näpunäiteid ja nippe GitHub Copiloti kasutamiseks andmeteaduses.
![Õpi koos AI-ga sari](../../../../translated_images/et/1.2b28cdc6205e26fe.webp)
![Õpi AI-ga sari](../../translated_images/et/1.2b28cdc6205e26fe.webp)
# Kas oled tudeng?
# Oled tudeng?
Alusta järgmiste ressurssidega:
- [Tudengite keskus](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) Sellel lehel leiad algajatele mõeldud ressursid, tudengipakid ja isegi võimalusi saada tasuta sertifikaadi sooduskupong. See on leht, mida soovid järjehoidjates hoida ja aeg-ajalt kontrollida, sest sisu uuendatakse vähemalt kord kuus.
- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Liitu ülemaailmse tudengisaadikute kogukonnaga, mis võib olla sinu võimalus Microsofti minna.
- [Tudengikeskuse leht](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) Sellel lehel leiad algajatele mõeldud ressursid, tudengipakid ja isegi võimalusi saada tasuta sertifikaadi kupong. See on leht, mida soovid järjehoidjatesse lisada ja aeg-ajalt vaadata, kuna sisu vahetub vähemalt kord kuus.
- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Liitu tudengite ülemaailmse kogukonnaga, see võib olla sinu tee Microsofti.
# Alustamine
## 📚 Dokumentatsioon
- **[Paigaldusjuhend](INSTALLATION.md)** – samm-sammult juhised algajatele
- **[Kasutusjuhend](USAGE.md)** – näited ja tavalised töövood
- **[Probleemide lahendamine](TROUBLESHOOTING.md)** – lahendused levinud probleemidele
- **[Panustamisjuhend](CONTRIBUTING.md)** – kuidas sellesse projekti panustada
- **[Õpetajatele](for-teachers.md)** – juhised ja klassiruumi ressursid
- **[Paigaldusjuhend](INSTALLATION.md)** - samm-sammuline juhend algajatele
- **[Kasutusjuhend](USAGE.md)** - näited ja levinumad töövood
- **[Probleemide lahendamine](TROUBLESHOOTING.md)** - lahendused sagedastele probleemidele
- **[Panustamise juhend](CONTRIBUTING.md)** - kuidas sellesse projekti panustada
- **[Õpetajatele](for-teachers.md)** - õpetamisjuhised ja klassiruumi ressursid
## 👨‍🎓 Tudengitele
> **Täielikud algajad:** Uus andmeteaduses? Alusta meie [algajasõbralike näidiste](examples/README.md) juurest! Need lihtsad ja hästi kommenteeritud näited aitavad mõista põhialuseid enne kogu õppekavaga süvenemist.
> **[Tudengid](https://aka.ms/student-page):** selle õppekava iseseisvaks kasutamiseks tee fork kogu hoidlast ja soorita harjutused iseseisvalt, alustades eel-loengu viktoriiniga. Seejärel loe loeng läbi ja täida ülejäänud tegevused. Püüa projekte luua, mõistes õppetunde, mitte kopeerides lahendustekoodi; see kood on siiski saadaval iga projektipõhise õppetunni /solutions kaustas. Teine idee oleks sõpradega õpirühm moodustada ja sisu koos läbi käia. Täiendava õppimise jaoks soovitame [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
> **Täielikud algajad**: Uus andmeteaduses? Alusta meie [algajatele sobivatest näidetest](examples/README.md)! Need lihtsad ja hästi kommenteeritud näited aitavad sul mõista põhitõdesid enne kogu õppekavasse süvenemist.
> **[Tudengid](https://aka.ms/student-page)**: et kasutada seda õppekava iseseisvalt, tehtle kogu hoidla omale koopiaks (fork) ja lahenda harjutused iseseisvalt, alustades eeloengu testiga. Seejärel loe loeng ja lõpeta ülejäänud tegevused. Proovi projekte luua, mõistes õppetunde, mitte lihtsalt lahenduste koodi kopeerides; lahenduskood on kättesaadav iga projektipõhise õppetunni /solutions kaustas. Teine idee on moodustada sõpradega õpperühm ja minna sisu läbi koos. Süvendatud õpingute jaoks soovitame [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
**Kiire algus:**
1. Vaata [Paigaldusjuhendit](INSTALLATION.md) oma keskkonna seadistamiseks
2. Tutvu [Kasutusjuhendiga](USAGE.md), et õppida õppekavaga töötamist
3. Alusta 1. õppest ja tööta järjest edasi
4. Liitu meie [Discord-kogukonnaga](https://aka.ms/ds4beginners/discord) toe saamiseks
1. Tutvu [paigaldusjuhendiga](INSTALLATION.md), et seada üles oma keskkond
2. Vaata [kasutusjuhendit](USAGE.md), et õppida curriculumiga töötamist
3. Alusta 1. õppetunnist ja liigu järjestikku edasi
4. Liitu meie [Discordi kogukonnaga](https://aka.ms/ds4beginners/discord), et saada tuge
## 👩‍🏫 Õpetajatele
> **Õpetajad:** oleme lisanud [mõned soovitused](for-teachers.md) selle õppekava kasutamiseks. Ootame teie tagasisidet [meie arutelufoorumis](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
> **Õpetajad**: oleme lisanud [mõningad soovitused](for-teachers.md) selle õppekava kasutamiseks. Ootame hea meelega teie tagasisidet [meie arutelufoorumis](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
## Kohtuge meeskonnaga
## Tutvu meeskonnaga
[![Reklaamvideo](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Reklaamvideo")
[![Promo video](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Promo video")
**Gif autor** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal)
> 🎥 Klõpsake ülaloleval pildil, et vaadata videot projektist ja inimestest, kes selle lõid!
> 🎥 Klõpsake ülalolevat pilti, et vaadata video projekti ja selle looja(te) kohta!
## Pedagoogika
Selle õppekava koostamisel oleme valinud kaks pedagoogilist põhimõtet: tagada, et see põhineks projektidel, ja et see sisaldaks sagedasi viktoriine. Selle sarja lõpuks on õpilased õppinud andmeteaduse põhialuseid, sealhulgas eetilisi mõisteid, andmete ettevalmistamist, erinevaid andmetöötlusviise, andmete visualiseerimist, andmeanalüüsi, andmeteaduse praktilisi kasutusjuhte ja palju muud.
Olemasoleva õppekava koostamisel oleme valinud kaks pedagoogilist põhimõtet: tagame, et see oleks projektipõhine ja sisaldaks sagedasi viktoriine. Selle sarja lõpuks on õpilased omandanud andmeteaduse põhilised põhimõtted, sealhulgas eetilised kontseptsioonid, andmete ettevalmistamine, erinevad viisid andmetega töötamiseks, andmete visualiseerimine, andmete analüüs, andmeteaduse praktilised kasutusjuhud ja palju muud.
Lisaks seab madala panusega viktoriin enne tundi õppija eesmärgi antud teemat õppida, samas kui teine viktoriin pärast tundi aitab teadmisi kinnistada. See õppekava on loodud olema paindlik ja lõbus ning seda saab läbida tervikuna või osaliselt. Projektid algavad väikesest ja muutuvad 10-nädalase tsükli lõpuks järjest keerukamaks.
Lisaks seab enne tundi toimuv madala panusega viktoriin õppija kavatsuseks teema õppimise, samas kui teine viktoriin pärast tundi tagab parema säilitamise. See õppekava on loodud olema paindlik ja lõbus ning seda saab võtta kas tervikuna või osaliselt. Projektid algavad väikestena ja muutuvad 10-nädalase tsükli lõpuks järjest keerukamaks.
> Leiate meie [käitumisjuhendi](CODE_OF_CONDUCT.md), [panustamise](CONTRIBUTING.md), [tõlke](TRANSLATIONS.md) juhendid. Hindame teie konstruktiivset tagasisidet!
> Leia meie [käitumisjuhend](CODE_OF_CONDUCT.md), [panustamise](CONTRIBUTING.md), [tõlke](TRANSLATIONS.md) juhised. Ootame teie konstruktiivset tagasisidet!
## Igas õppetükis on:
## Iga õppetund sisaldab:
- Valikuline sketšinotis
- Valikuline lisavideo
- Soojenduseks viktoriin enne tundi
- Kirjalik õppetükk
- Projektipõhiste õppetükkide puhul samm-sammult juhised projekti ehitamiseks
- Teadmiste kontroll
- Väljakutse
- Lisalugemine
- Kodune ülesanne
- [Viktoriin pärast tundi](https://ff-quizzes.netlify.app/en/)
- Valikulist sketšimärkust
- Valikulist lisavideot
- Pre-tunniviktoriini soojenduseks
- Kirjalikku õppetundi
- Projektipõhiste õppetundide puhul samm-sammult juhiseid, kuidas projekti üles ehitada
- Teadmiste kontrolli
- Väljakutset
- Lisalugemist
- Kodutööd
- [Pärastundi viktoriini](https://ff-quizzes.netlify.app/en/)
> **Märkused viktoriinide kohta**: Kõik viktoriinid on koondatud kausta Quiz-App, kokku 40 viktoriini, kus igas on kolm küsimust. Neid lingitakse õppetükkide sees, kuid viktoriini rakendus saab käivitada lokaalselt või paigaldada Azure'i; järgige juhiseid `quiz-app` kaustas. Need on järk-järgult lokaliseeritavad.
> **Märkus viktoriinide kohta**: Kõik viktoriinid on koondatud Quiz-App kausta, kokku 40 viktoriini, milles igas on kolm küsimust. Neile viidatakse õppetundide sees, kuid viktoriinirakendust saab käivitada lokaalselt või Azure’is; täpsemad juhised asuvad `quiz-app` kaustas. Viktoriinid tõlgitakse järk-järgult.
## 🎓 Algajatele sõbralikud näited
## 🎓 Algajasõbralikud näited
**Uus andmeteaduses?** Oleme loonud spetsiaalse [näidiste kataloogi](examples/README.md) lihtsa ja hästi kommenteeritud koodiga, mis aitab teil alustada:
**Oled andmeteadusega uus?** Oleme loonud spetsiaalse [näidiste kataloogi](examples/README.md), kus on lihtne ja hästi kommenteeritud kood, mis aitab sul alustada:
- 🌟 **Tere, maailm!** - Teie esimene andmeteaduse programm
- 📂 **Andmete laadimine** - Õppige andmestike lugemist ja uurimist
- 📊 **Lihtne analüüs** - Arvutage statistikat ja leidke mustreid
- 📈 **Põhiline visualiseerimine** - Looge diagramme ja graafikuid
- 🔬 **Reaalne projekt** - Täielik töövoog algusest lõpuni
- 🌟 **Hello World** - Sinu esimene andmeteaduse programm
- 📂 **Andmete laadimine** - Õpi andmekogumeid lugema ja uurima
- 📊 **Lihtne analüüs** - Arvuta statistikat ja leia mustreid
- 📈 **Põhivisualiseerimine** - Loo diagramme ja graafikuid
- 🔬 **Tegelik projekt** - Täielik töökäik algusest lõpuni
Igas näites on üksikasjalikud kommentaarid, mis selgitavad iga sammu, muudavad selle algajatele ideaalseks!
Igas näites on üksikasjalikud kommentaarid, mis selgitavad igat sammu, muutes selle ideaalseks täiesti algajatele!
👉 **[Alustage näidetega](examples/README.md)** 👈
👉 **[Alusta näidetest](examples/README.md)** 👈
## Õppetükid
## Õppetunnid
|![ Sketchnote autor @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/et/00-Roadmap.4905d6567dff4753.webp)|
|![ Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/et/00-Roadmap.4905d6567dff4753.webp)|
|:---:|
| Andmeteadus algajatele: teekaart - _sketš autorilt [@nitya](https://twitter.com/nitya)_ |
| Andmeteadus algajatele: teekaart - _Sketš @nitya_ |
| Õppetüki number | Teema | Õppetüki grupp | Õpieesmärgid | Lingitud õppetükk | Autor |
| Õppetunni number | Teema | Õppetunni gruppeerimine | Õpitulemused | Lingitud õppetund | Autor |
| :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: |
| 01 | Andmeteaduse määratlemine | [Sissejuhatus](1-Introduction/README.md) | Õppige andmeteaduse põhikontseptsioone ja kuidas see on seotud tehisintellekti, masinõppe ja suurandmetega. | [õppetükk](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) |
| 02 | Andmeteaduse eetika | [Sissejuhatus](1-Introduction/README.md) | Andmete eetika kontseptsioonid, väljakutsed ja raamistikud. | [õppetükk](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) |
| 03 | Andmete määratlemine | [Sissejuhatus](1-Introduction/README.md) | Kuidas andmeid klassifitseeritakse ja selle tavalised allikad. | [õppetükk](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) |
| 04 | Sissejuhatus statistikasse ja tõenäosusõpetusse | [Sissejuhatus](1-Introduction/README.md) | Matemaatilised tõenäosuse ja statistika tehnikad andmete mõistmiseks. | [õppetükk](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) |
| 05 | Töötamine relatsiooniliste andmetega | [Töötamine andmetega](2-Working-With-Data/README.md) | Sissejuhatus relatsioonilistesse andmetesse ja relatsiooniliste andmete uurimise ja analüüsi alused Structured Query Language’i ehk SQL-i (hääldatakse „si-kwell“) abil. | [õppetükk](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | |
| 06 | Töötamine NoSQL andmetega | [Töötamine andmetega](2-Working-With-Data/README.md) | Sissejuhatus mitte-relatsioonilistesse andmetesse, selle erinevatesse tüüpidesse ja dokumentandmebaaside uurimise ja analüüsi alused. | [õppetükk](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)|
| 07 | Töötamine Pythoniga | [Töötamine andmetega](2-Working-With-Data/README.md) | Andmete uurimiseks Pythoni kasutamise alused koos selliste teekidega nagu Pandas. Soovitatav on Pythoni programmeerimise põhiline mõistmine. | [õppetükk](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) |
| 08 | Andmete ettevalmistamine | [Töötamine andmetega](2-Working-With-Data/README.md) | Teemad andmete puhastamise ja teisendamise tehnikatest, mis võimaldavad toime tulla puuduvate, ebatäpsete või puudulike andmetega seotud väljakutsetega. | [õppetükk](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) |
| 09 | Kvantiteetide visualiseerimine | [Andmete visualiseerimine](3-Data-Visualization/README.md) | Õppige kasutama Matplotlibi lindude andmete visualiseerimiseks 🦆 | [õppetükk](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) |
| 10 | Andmete jaotuste visualiseerimine | [Andmete visualiseerimine](3-Data-Visualization/README.md) | Visuaalne vaatlus ja trendide kuvamine vahemikus. | [õppetükk](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) |
| 11 | Proportsioonide visualiseerimine | [Andmete visualiseerimine](3-Data-Visualization/README.md) | Diskreetsete ja rühmitatud protsentide visualiseerimine. | [õppetükk](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) |
| 12 | Suhete visualiseerimine | [Andmete visualiseerimine](3-Data-Visualization/README.md) | Andmekogumite ja nende muutujate vaheliste seoste ja korrelatsioonide visualiseerimine. | [õppetükk](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) |
| 13 | Mõtestatud visualiseeringud | [Andmete visualiseerimine](3-Data-Visualization/README.md) | Tehnikad ja juhised, kuidas teha oma visualiseeringuid väärtuslikeks tõhusaks probleemilahenduseks ja teadmiste saamiseks. | [õppetükk](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) |
| 14 | Sissejuhatus andmeteaduse elutsüklisse | [Elutsükkel](4-Data-Science-Lifecycle/README.md) | Sissejuhatus andmeteaduse elutsüklisse ja selle esimene samm andmete hankimisse ja ekstraktsiooni. | [õppetükk](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) |
| 15 | Analüüs | [Elutsükkel](4-Data-Science-Lifecycle/README.md) | See etapp andmeteaduse elutsüklis keskendub andmete analüüsimise tehnikatele. | [õppetükk](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | |
| 16 | Kommunikatsioon | [Elutsükkel](4-Data-Science-Lifecycle/README.md) | See etapp andmeteaduse elutsüklis keskendub teadmiste esitamisele andmetest viisil, mis muudab otsustajatel nende mõistmise lihtsamaks. | [õppetükk](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | |
| 17 | Andmeteadus pilves | [Pilveandmed](5-Data-Science-In-Cloud/README.md) | See õppesari tutvustab andmeteadust pilves ja selle eeliseid. | [õppetükk](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) ja [Maud](https://twitter.com/maudstweets) |
| 18 | Andmeteadus pilves | [Pilveandmed](5-Data-Science-In-Cloud/README.md) | Mudelite treenimine madala kooditaseme tööriistadega. |[õppetükk](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) ja [Maud](https://twitter.com/maudstweets) |
| 19 | Andmeteadus pilves | [Pilveandmed](5-Data-Science-In-Cloud/README.md) | Mudelite juurutamine Azure Machine Learning Studio abil. | [õppetükk](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) ja [Maud](https://twitter.com/maudstweets) |
| 20 | Andmeteadus vabas looduses | [Looduses](6-Data-Science-In-Wild/README.md) | Andmeteadusel põhinevad projektid pärismaailmas. | [õppetükk](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) |
## GitHubi Codespaces
Järgige neid samme, et avada see näidis Codespaces’is:
1. Klõpsake koodi rippmenüüd ja valige valik Ava Codespaces’iga.
2. Valige paani allosas + Uus codespace.
| 01 | Andmeteaduse määratlemine | [Sissejuhatus](1-Introduction/README.md) | Õpi andmeteaduse põhimõisteid ja kuidas see on seotud tehisintellekti, masinõppe ja suurandmetega. | [õppetund](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) |
| 02 | Andmeteaduse eetika | [Sissejuhatus](1-Introduction/README.md) | Andme-eetika kontseptsioonid, väljakutsed ja raamistikud. | [õppetund](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) |
| 03 | Andmete määratlemine | [Sissejuhatus](1-Introduction/README.md) | Kuidas andmeid klassifitseeritakse ja nende levinud allikad. | [õppetund](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) |
| 04 | Sissejuhatus statistikasse ja tõenäosusse | [Sissejuhatus](1-Introduction/README.md) | Matemaatilised tõenäosuse ja statistika tehnikad andmete mõistmiseks. | [õppetund](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) |
| 05 | Töötamine relatsioonandmetega | [Töötamine andmetega](2-Working-With-Data/README.md) | Sissejuhatus relatsioonandmetesse ja andmete uurimise ning analüüsimise põhialused struktureeritud päringukeeles (SQL). | [õppetund](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | |
| 06 | Töötamine NoSQL andmetega | [Töötamine andmetega](2-Working-With-Data/README.md) | Sissejuhatus mitte-relatsioonandmetesse, nende erinevate tüüpide ja dokumentandmebaaside uurimise ning analüüsi põhialused. | [õppetund](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)|
| 07 | Töötamine Pythoniga | [Töötamine andmetega](2-Working-With-Data/README.md) | Python kasutamise põhialused andmete uurimiseks selliste teekidega nagu Pandas. Soovitatav on Python programmeerimise aluste mõistmine. | [õppetund](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) |
| 08 | Andmete ettevalmistamine | [Töötamine andmetega](2-Working-With-Data/README.md) | Andmetehnikad andmete puhastamiseks ja transformeerimiseks, et toime tulla puuduvate, ebatäpsete või puudulike andmetega. | [õppetund](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) |
| 09 | Koguste visualiseerimine | [Andmete visualiseerimine](3-Data-Visualization/README.md) | Õpi kasutama Matplotlibi lindude andmete visualiseerimiseks 🦆 | [õppetund](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) |
| 10 | Andmete jaotuste visualiseerimine | [Andmete visualiseerimine](3-Data-Visualization/README.md) | Visuaalselt kujutame tähelepanekuid ja trende kindlas intervallis. | [õppetund](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) |
| 11 | Proportsioonide visualiseerimine | [Andmete visualiseerimine](3-Data-Visualization/README.md) | Diskreetsete ja grupeeritud protsentide visualiseerimine. | [õppetund](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) |
| 12 | Suhete visualiseerimine | [Andmete visualiseerimine](3-Data-Visualization/README.md) | Andmekogumite ja nende muutujate vaheliste seoste ja korrelatsioonide kujutamine. | [õppetund](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) |
| 13 | Mõtestatud visualiseeringud | [Andmete visualiseerimine](3-Data-Visualization/README.md) | Tehnikad ja juhised, mis aitavad teha visualiseeringud väärtuslikeks tõhusaks probleemilahenduseks ja teadmisteks. | [õppetund](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) |
| 14 | Sissejuhatus andmeteaduse elutsüklisse | [Elutsükkel](4-Data-Science-Lifecycle/README.md) | Sissejuhatus andmeteaduse elutsüklisse ja selle esimene samm: andmete hankimine ja väljavõtmine. | [õppetund](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) |
| 15 | Analüüsimine | [Elutsükkel](4-Data-Science-Lifecycle/README.md) | See faas andmeteaduse elutsüklis keskendub andmete analüüsimise tehnikatele. | [õppetund](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | |
| 16 | Kommunikatsioon | [Elutsükkel](4-Data-Science-Lifecycle/README.md) | See faas andmeteaduse elutsüklis keskendub teadmiste esitamisele selliselt, et see oleks otsustajatele arusaadav. | [õppetund](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | |
| 17 | Andmeteadus pilves | [Pilvandmed](5-Data-Science-In-Cloud/README.md) | See õppesari tutvustab andmeteadust pilves ja selle eeliseid. | [õppetund](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) ja [Maud](https://twitter.com/maudstweets) |
| 18 | Andmeteadus pilves | [Pilvandmed](5-Data-Science-In-Cloud/README.md) | Mudelite treenimine madalakoodiliste tööriistade abil. |[õppetund](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) ja [Maud](https://twitter.com/maudstweets) |
| 19 | Andmeteadus pilves | [Pilvandmed](5-Data-Science-In-Cloud/README.md) | Mudelite juurutamine Azure Machine Learning Studio abil. | [õppetund](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) ja [Maud](https://twitter.com/maudstweets) |
| 20 | Andmeteadus vabamas keskkonnas | [Väljas](6-Data-Science-In-Wild/README.md) | Andmeteadusjuhtumid reaalses maailmas. | [õppetund](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) |
## GitHub Codespaces
Järgige neid samme selle näite avamiseks Codespaces’is:
1. Klõpsake menüüs Code rippmenüüd ja valige Open with Codespaces.
2. Paneeli allosas valige + New codespace.
Lisateabe saamiseks vaadake [GitHubi dokumentatsiooni](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace).
## VSCode Remote - Containerid
Järgige neid samme, et avada see hoidla konteineris, kasutades oma kohalikku masinat ja VSCode'i koos VS Code Remote - Containers laiendiga:
## VSCode Remote - Containers
Järgige neid samme selle hoidla avamiseks konteineris, kasutades oma kohalikku arvutit ja VSCode’i ning Remote - Containers laiendust:
1. Kui kasutate arenduskonteinerit esmakordselt, veenduge, et teie süsteem vastab eeltingimustele (näiteks on installitud Docker), vaadates [alustamise dokumentatsiooni](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started).
1. Kui kasutate arenduscontainerit esimest korda, veenduge, et teie süsteem vastab eeltingimustele (nt Docker on paigaldatud) [käivitamise dokumentatsioonis](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started).
Selle hoidla kasutamiseks võite kas avada hoidla isoleeritud Docker’i mahus:
Seda hoidlat saab kasutada avades selle hoidla isoleeritud Docker mahu sees:
**Märkus**: Tagaplaanil kasutab see Remote-Containers: **Clone Repository in Container Volume...** käsku lähtekoodi kopeerimiseks Docker’i mahus kohaliku failisüsteemi asemel. [Mahud](https://docs.docker.com/storage/volumes/) on soovitatav mehhanism konteineri andmete säilitamiseks.
**Märkus**: Tegelikult kasutatakse Remote-Containers: **Clone Repository in Container Volume...** käsku, mis kloonib lähtekoodi Docker-mahu sisse, mitte kohalikku failisüsteemi. [Mahud](https://docs.docker.com/storage/volumes/) on eelistatud mehhanism konteineri andmete säilitamiseks.
Või avage kohalikult kloonitud või allalaaditud hoidla versioon:
Või avades kohalikult kloonitud või alla laetud hoidla:
- Kloonige see hoidla oma kohalikku failisüsteemi.
- Vajutage F1 ja valige käsk **Remote-Containers: Open Folder in Container...**.
- Valige selle kausta kloonitud koopiad, oodake konteineri käivitumist ja proovige.
- Valige selle kausta kloonitud koopia, oodake konteineri käivitumist ja proovige funktsioone.
## Võrguühenduseta juurdepääs
## Võrgust väljas kasutamine
Seda dokumentatsiooni saate võrguühenduseta käivitada, kasutades [Docsify't](https://docsify.js.org/#/). Hargna see hoidla, [installi Docsify](https://docsify.js.org/#/quickstart) oma kohalikku masinasse, seejärel tippige selle hoidla juurkaustas `docsify serve`. Veebisait käivitatakse pordil 3000 aadressil `localhost:3000`.
Seda dokumentatsiooni saab kasutada ka võrgust väljas, kasutades [Docsify](https://docsify.js.org/#/). Forkige see hoidla, paigaldage oma kohalikku masinasse [Docsify](https://docsify.js.org/#/quickstart), seejärel hoidla root-kataloogis tippige `docsify serve`. Veebileht saadetakse pordi 3000 kaudu aadressil `localhost:3000`.
> Märkus, märkmeid (notebook-e) ei renderdata Docsify abil, nii et kui peate märkmikku kasutama, käitage see eraldi VS Code'is koos Python’i kerneliga.
> Märkus: märkmikud ei renderdata Docsify abil, seega kui peate käivitama märkmiku, tehke seda eraldi VS Code’is Python kerneliga.
## Muud õppekavad
Meie meeskond toodab ka teisi õppekavu! Vaadake:
Meie meeskond toodab ka teisi õppekavasid! Vaadake:
<!-- CO-OP TRANSLATOR OTHER COURSES START -->
### LangChain
[![LangChain4j algajatele](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 algajatele](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 / Agendid
[![AZD algajatele](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 algajatele](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)
[![Edge tehisintellekt algajatele](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 algajatele](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)
[![AI Agendid algajatele](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)
[![Tehisintellekti agendid algajatele](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)
---
@ -225,8 +216,8 @@ Meie meeskond toodab ka teisi õppekavu! Vaadake:
---
### Põhialused
[![ML algajatele](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)
### Põhiteadmised
[![Masinõpe algajatele](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)
[![Andmeteadus algajatele](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)
[![Tehisintellekt algajatele](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)
[![Küberjulgeolek algajatele](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)
@ -239,24 +230,24 @@ Meie meeskond toodab ka teisi õppekavu! Vaadake:
### Copiloti sari
[![Copilot tehisintellekti paarisprogrammeerimiseks](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 C#/.NET jaoks](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)
[![Copiloti seiklus](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)
[![Copiloti seiklused](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 -->
## Abi saamine
**Tekkinud probleeme?** Vaadake meie [Tõrkeotsingu juhendit](TROUBLESHOOTING.md), kus on lahendused sageimatele probleemidele.
**Tekivad probleemid?** Vaadake meie [Tõrkeotsingu juhendit](TROUBLESHOOTING.md) levinud probleemide lahendamiseks.
Kui satute takistustesse või teil on küsimusi tehisintellektirakenduste loomise kohta, liituge koosõppijate ja kogenud arendajatega MCP aruteludes. See on toetav kogukond, kus küsimused on oodatud ja teadmisi jagatakse vabalt.
Kui takerdate või teil on küsimusi AI-rakenduste loomise kohta, liituge MCP arutelufoorumis teiste õppijate ja kogenud arendajatega. See on toetav kogukond, kus küsimused on teretulnud ja teadmisi jagatakse vabalt.
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
Kui teil on toodet puudutavat tagasisidet või vigasid arendamise käigus, külastage:
Kui teil on toote kohta tagasisidet või ehitamise ajal vigu, külastage:
[![Microsoft Foundry arendajate foorum](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 -->
**Vastutusest loobumine**:
See dokument on tõlgitud kasutades tehisintellekti tõlketeenust [Co-op Translator](https://github.com/Azure/co-op-translator). Kuigi me püüame täpsust, palun pidage meeles, et automaatsed tõlked võivad sisaldada vigu või ebatäpsusi. Originaaldokument selle emakeeles tuleks pidada autoriteetseks allikaks. Kriitilise teabe korral soovitatakse kasutada professionaalset inimtõlget. Me ei vastuta tõlgendustest või arusaamatustest, mis võivad tekkida selle tõlke kasutamisest.
**Vastutusest loobumine**:
See dokument on tõlgitud kasutades tehisintellektil põhinevat tõlketeenust [Co-op Translator](https://github.com/Azure/co-op-translator). Kuigi püüame tagada täpsust, palun arvestage, et automatiseeritud tõlgetes võib esineda vigu või ebatäpsusi. Originaaldokument selle emakeeles tuleks pidada autoriteetseks allikaks. Kriitilise tähtsusega teabe puhul soovitatakse kasutada professionaalset inimtõlget. Me ei vastuta käesoleva tõlke kasutamisest tekkida võivate arusaamatuste või valesti mõistmiste eest.
<!-- CO-OP TRANSLATOR DISCLAIMER END -->

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## Turvalisus

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# Tugi
## Kuidas esitada probleeme ja saada abi

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# Tõrkeotsingu juhend
See juhend pakub lahendusi levinud probleemidele, millega võite kokku puutuda Data Science for Beginners õppekava kasutamisel.

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# Kasutusjuhend
See juhend sisaldab näiteid ja tavapäraseid töövooge algajatele mõeldud andmeteaduse õppekava kasutamiseks.

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- Sissejuhatus
- [Andmeteaduse määratlemine](../1-Introduction/01-defining-data-science/README.md)
- [Andmeteaduse eetika](../1-Introduction/02-ethics/README.md)

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# Algajatele sobivad andmeteaduse näited
Tere tulemast näidete kataloogi! See lihtsate ja hästi kommenteeritud näidete kogumik on loodud selleks, et aidata sul alustada andmeteadusega, isegi kui oled täiesti algaja.

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## Haridustöötajatele
Kas soovite seda õppekava oma klassiruumis kasutada? Palun tehke seda julgelt!

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# Viktoriinid
Need viktoriinid on andmeteaduse õppekava eel- ja järelloengute viktoriinid aadressil https://aka.ms/datascience-beginners

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Leia kõik visandmärkmed siit!
## Tunnustused

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

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# Assignment: Data Science Scenarios
For dis first assignment, we wan make you think about some real-life process or wahala for different problem areas, and how you fit take improve am using Data Science process. Think about dis things:

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# Assignment: Data Science Scenarios
For dis first assignment, we wan make you think about some real-life process or problem for different problem area, and how you fit take Data Science process improve am. Think about dis:

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# Introduction to Data Ethics
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/02-Ethics.png)|

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## Write Data Ethics Case Study
## Instructions

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

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# Classify Datasets
## Instructions

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# Small Introduction to Statistics and Probability
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/04-Statistics-Probability.png)|

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# Small Diabetes Study
For dis assignment, we go work wit one small dataset of diabetes patients wey dem collect from [here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).

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# Introduction to Data Science
![data dey work](../../../translated_images/pcm/data.48e22bb7617d8d92.webp)

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# Working with Data: Relational Databases
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/05-RelationalData.png)|

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# How to show airport data
Dem don give you one [database](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db) wey dey use [SQLite](https://sqlite.org/index.html) wey get info about airports. The schema dey show for down. You go use the [SQLite extension](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) for [Visual Studio Code](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum) to show info about airports for different cities.

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# How to Work with Data: Non-Relational Data
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/06-NoSQL.png)|

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# Soda Profits
## Instructions

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# How to Work with Data: Python and Pandas Library
| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/07-WorkWithPython.png) |

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# Assignment for Data Processing for Python
For dis assignment, we go ask you make you explain di code wey we don start to dey develop for our challenges. Di assignment get two parts:

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# Work wit Data: Data Preparation
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/08-DataPreparation.png)|

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# Check Data Wey Dem Collect From Form
One client dey test one [small form](../../../../2-Working-With-Data/08-data-preparation/index.html) to collect some basic info about dia client dem. Dem don carry wetin dem find come meet you make you check di data wey dem don collect. You fit open di `index.html` page for browser to see di form.

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# How to Work with Data
![data love](../../../translated_images/pcm/data-love.a22ef29e6742c852.webp)

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

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# Lines, Scatters and Bars
## Instructions

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

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# Use your skills
## Wetin you go do

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

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# Try am for Excel
## Instructions

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# Visualizing Relationships: All About Honey 🍯
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/12-Visualizing-Relationships.png)|

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# Dive inside di beehive
## Instructions

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# How to Make Visualizations Wey Get Meaning
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/13-MeaningfulViz.png)|

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