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مثبتة لجعل المهارات الجديدة "تترسخ". +[![فتح في GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) -**شكر جزيل لمؤلفينا:** [جاسمين جريناواي](https://www.twitter.com/paladique)، [ديمتري سوشنيكوف](http://soshnikov.com)، [نيتيا ناراسيمهان](https://twitter.com/nitya)، [جالين ماكجي](https://twitter.com/JalenMcG)، [جين لوبر](https://twitter.com/jenlooper)، [مود ليفي](https://twitter.com/maudstweets)، [تيفاني سوتير](https://twitter.com/TiffanySouterre)، [كريستوفر هاريسون](https://www.twitter.com/geektrainer). +[![رخصة GitHub](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE) +[![مساهمو GitHub](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/) +[![مشاكل GitHub](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/) +[![طلبات السحب على GitHub](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/) +[![مرحبًا بطلبات السحب](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) -**🙏 شكر خاص 🙏 لمؤلفينا ومراجعين المحتوى من [سفراء الطلاب في Microsoft](https://studentambassadors.microsoft.com/)،** ومن بينهم أريان أرورا، [أديتيا جارج](https://github.com/AdityaGarg00)، [ألوندرا سانشيز](https://www.linkedin.com/in/alondra-sanchez-molina/)، [أنكيتا سينغ](https://www.linkedin.com/in/ankitasingh007)، [أنوبام ميشرا](https://www.linkedin.com/in/anupam--mishra/)، [أربيتا داس](https://www.linkedin.com/in/arpitadas01/)، شيل بيهاري دوباي، [ديبري نسوفور](https://www.linkedin.com/in/dibrinsofor)، [ديشيتا بهاسين](https://www.linkedin.com/in/dishita-bhasin-7065281bb)، [مجد صافي](https://www.linkedin.com/in/majd-s/)، [ماكس بلوم](https://www.linkedin.com/in/max-blum-6036a1186/)، [ميغيل كوريا](https://www.linkedin.com/in/miguelmque/)، [محمد افتخار (إفتو) ابن جلال](https://twitter.com/iftu119)، [ناورين تبسم](https://www.linkedin.com/in/nawrin-tabassum)، [ريموند وانغسا بوترا](https://www.linkedin.com/in/raymond-wp/)، [روهيت ياداف](https://www.linkedin.com/in/rty2423)، سامريدي شارما، [سانيا سينها](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200)، [شينا نارولا](https://www.linkedin.com/in/sheena-narua-n/)، [توقير أحمد](https://www.linkedin.com/in/tauqeerahmad5201/)، يوغيندراسينغ باوار، [فيدوشي غوبتا](https://www.linkedin.com/in/vidushi-gupta07/)، [جاسلين سوندي](https://www.linkedin.com/in/jasleen-sondhi/) +[![مشاهدو GitHub](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/) +[![تفرعات GitHub](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/) +[![نجوم GitHub](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/) + +[![](https://dcbadge.vercel.app/api/server/ByRwuEEgH4)](https://discord.gg/zxKYvhSnVp?WT.mc_id=academic-000002-leestott) + +[![منتدى مطوري Azure AI Foundry](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) + +يسر فريق دعاة السحابة في مايكروسوفت أن يقدموا منهجًا دراسيًا لمدة 10 أسابيع يتضمن 20 درسًا حول علم البيانات. يحتوي كل درس على اختبارات قبل وبعد الدرس، وتعليمات مكتوبة لإكمال الدرس، وحل، ومهمة. تتيح لك طريقتنا التعليمية القائمة على المشاريع التعلم أثناء البناء، وهي طريقة مثبتة لجعل المهارات الجديدة "تترسخ". + +**شكر خاص لمؤلفينا:** [جاسمين جريناواي](https://www.twitter.com/paladique)، [ديمتري سوشنيكوف](http://soshnikov.com)، [نيتيا ناراسيمهان](https://twitter.com/nitya)، [جيلين ماكجي](https://twitter.com/JalenMcG)، [جين لوبر](https://twitter.com/jenlooper)، [مود ليفي](https://twitter.com/maudstweets)، [تيفاني سوتير](https://twitter.com/TiffanySouterre)، [كريستوفر هاريسون](https://www.twitter.com/geektrainer). + +**🙏 شكر خاص 🙏 لمؤلفينا، المراجعين، والمساهمين في المحتوى من [سفراء الطلاب في مايكروسوفت](https://studentambassadors.microsoft.com/),** لا سيما أريان أرورا، [أديتيا جارج](https://github.com/AdityaGarg00)، [ألوندرا سانشيز](https://www.linkedin.com/in/alondra-sanchez-molina/)، [أنكيتا سينغ](https://www.linkedin.com/in/ankitasingh007)، [أنوبام ميشرا](https://www.linkedin.com/in/anupam--mishra/)، [أربيتا داس](https://www.linkedin.com/in/arpitadas01/)، شيل بيهاري دوباي، [ديبري نسوفور](https://www.linkedin.com/in/dibrinsofor)، [ديشيتا بهاسين](https://www.linkedin.com/in/dishita-bhasin-7065281bb)، [مجد صافي](https://www.linkedin.com/in/majd-s/)، [ماكس بلوم](https://www.linkedin.com/in/max-blum-6036a1186/)، [ميغيل كوريا](https://www.linkedin.com/in/miguelmque/)، [محمد افتخار (إفتو) ابن جلال](https://twitter.com/iftu119)، [نورين تبسم](https://www.linkedin.com/in/nawrin-tabassum)، [ريموند وانغسا بوترا](https://www.linkedin.com/in/raymond-wp/)، [روهيت ياداف](https://www.linkedin.com/in/rty2423)، سمريدي شارما، [سانيا سينها](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200)، +[شينا نارولا](https://www.linkedin.com/in/sheena-narua-n/)، [توقير أحمد](https://www.linkedin.com/in/tauqeerahmad5201/)، يوغيندراسينغ باوار، [فيدوشي غوبتا](https://www.linkedin.com/in/vidushi-gupta07/)، [جاسلين سوندي](https://www.linkedin.com/in/jasleen-sondhi/) |![رسم توضيحي بواسطة @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.ar.png)| |:---:| @@ -21,16 +38,16 @@ Azure Cloud Advocates في Microsoft يقدمون منهجًا دراسيًا ل ### 🌐 دعم متعدد اللغات -#### مدعوم عبر GitHub Action (تلقائي ودائم التحديث) +#### مدعوم عبر GitHub Action (تلقائي ومحدث دائمًا) -[الفرنسية](../fr/README.md) | [الإسبانية](../es/README.md) | [الألمانية](../de/README.md) | [الروسية](../ru/README.md) | [العربية](./README.md) | [الفارسية](../fa/README.md) | [الأردية](../ur/README.md) | [الصينية (المبسطة)](../zh/README.md) | [الصينية (التقليدية، ماكاو)](../mo/README.md) | [الصينية (التقليدية، هونغ كونغ)](../hk/README.md) | [الصينية (التقليدية، تايوان)](../tw/README.md) | [اليابانية](../ja/README.md) | [الكورية](../ko/README.md) | [الهندية](../hi/README.md) | [البنغالية](../bn/README.md) | [الماراثية](../mr/README.md) | [النيبالية](../ne/README.md) | [البنجابية (غورموخي)](../pa/README.md) | [البرتغالية (البرتغال)](../pt/README.md) | [البرتغالية (البرازيل)](../br/README.md) | [الإيطالية](../it/README.md) | [البولندية](../pl/README.md) | [التركية](../tr/README.md) | [اليونانية](../el/README.md) | [التايلاندية](../th/README.md) | [السويدية](../sv/README.md) | [الدانماركية](../da/README.md) | [النرويجية](../no/README.md) | [الفنلندية](../fi/README.md) | [الهولندية](../nl/README.md) | [العبرية](../he/README.md) | [الفيتنامية](../vi/README.md) | [الإندونيسية](../id/README.md) | [الماليزية](../ms/README.md) | [التاغالوغية (الفلبينية)](../tl/README.md) | [السواحيلية](../sw/README.md) | [الهنغارية](../hu/README.md) | [التشيكية](../cs/README.md) | [السلوفاكية](../sk/README.md) | [الرومانية](../ro/README.md) | [البلغارية](../bg/README.md) | [الصربية (السيريلية)](../sr/README.md) | [الكرواتية](../hr/README.md) | [السلوفينية](../sl/README.md) | [الأوكرانية](../uk/README.md) | [البورمية (ميانمار)](../my/README.md) +[العربية](./README.md) | [البنغالية](../bn/README.md) | [البلغارية](../bg/README.md) | [البورمية (ميانمار)](../my/README.md) | [الصينية (المبسطة)](../zh/README.md) | [الصينية (التقليدية، هونغ كونغ)](../hk/README.md) | [الصينية (التقليدية، ماكاو)](../mo/README.md) | [الصينية (التقليدية، تايوان)](../tw/README.md) | [الكرواتية](../hr/README.md) | [التشيكية](../cs/README.md) | [الدانماركية](../da/README.md) | [الهولندية](../nl/README.md) | [الإستونية](../et/README.md) | [الفنلندية](../fi/README.md) | [الفرنسية](../fr/README.md) | [الألمانية](../de/README.md) | [اليونانية](../el/README.md) | [العبرية](../he/README.md) | [الهندية](../hi/README.md) | [الهنغارية](../hu/README.md) | [الإندونيسية](../id/README.md) | [الإيطالية](../it/README.md) | [اليابانية](../ja/README.md) | [الكورية](../ko/README.md) | [الليتوانية](../lt/README.md) | [الماليزية](../ms/README.md) | [الماراثية](../mr/README.md) | [النيبالية](../ne/README.md) | [النرويجية](../no/README.md) | [الفارسية](../fa/README.md) | [البولندية](../pl/README.md) | [البرتغالية (البرازيل)](../br/README.md) | [البرتغالية (البرتغال)](../pt/README.md) | [البنجابية (غورموخي)](../pa/README.md) | [الرومانية](../ro/README.md) | [الروسية](../ru/README.md) | [الصربية (السيريلية)](../sr/README.md) | [السلوفاكية](../sk/README.md) | [السلوفينية](../sl/README.md) | [الإسبانية](../es/README.md) | [السواحيلية](../sw/README.md) | [السويدية](../sv/README.md) | [التاغالوغية (الفلبينية)](../tl/README.md) | [التاميلية](../ta/README.md) | [التايلاندية](../th/README.md) | [التركية](../tr/README.md) | [الأوكرانية](../uk/README.md) | [الأردية](../ur/README.md) | [الفيتنامية](../vi/README.md) **إذا كنت ترغب في دعم لغات إضافية، يمكنك الاطلاع على القائمة [هنا](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** -#### انضم إلى مجتمعنا -[![Azure AI Discord](https://dcbadge.limes.pink/api/server/kzRShWzttr)](https://aka.ms/ds4beginners/discord) +#### انضم إلى مجتمعنا +[![Discord Azure AI](https://dcbadge.limes.pink/api/server/kzRShWzttr)](https://aka.ms/ds4beginners/discord) -لدينا سلسلة تعلم مع الذكاء الاصطناعي مستمرة، تعرف عليها وانضم إلينا في [سلسلة تعلم مع الذكاء الاصطناعي](https://aka.ms/learnwithai/discord) من 18 - 30 سبتمبر، 2025. ستحصل على نصائح وحيل لاستخدام GitHub Copilot في علم البيانات. +لدينا سلسلة تعلم مع الذكاء الاصطناعي مستمرة على Discord، تعرف على المزيد وانضم إلينا في [سلسلة تعلم مع الذكاء الاصطناعي](https://aka.ms/learnwithai/discord) من 18 - 30 سبتمبر، 2025. ستحصل على نصائح وحيل لاستخدام GitHub Copilot في علم البيانات. ![سلسلة تعلم مع الذكاء الاصطناعي](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.ar.jpg) @@ -38,56 +55,56 @@ Azure Cloud Advocates في Microsoft يقدمون منهجًا دراسيًا ل ابدأ باستخدام الموارد التالية: -- [صفحة مركز الطلاب](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) في هذه الصفحة، ستجد موارد للمبتدئين، حزم الطلاب وحتى طرق للحصول على قسيمة شهادة مجانية. هذه صفحة يجب أن تضيفها إلى المفضلة وتراجعها من وقت لآخر حيث نقوم بتغيير المحتوى شهريًا على الأقل. -- [سفراء الطلاب في Microsoft](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) انضم إلى مجتمع عالمي من سفراء الطلاب، قد تكون هذه فرصتك للدخول إلى Microsoft. +- [صفحة مركز الطلاب](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) في هذه الصفحة، ستجد موارد للمبتدئين، حزم الطلاب وحتى طرق للحصول على قسيمة شهادة مجانية. هذه صفحة يجب عليك وضع إشارة مرجعية عليها والتحقق منها من وقت لآخر حيث نقوم بتحديث المحتوى شهريًا. +- [سفراء الطلاب في مايكروسوفت](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) انضم إلى مجتمع عالمي من سفراء الطلاب، قد تكون هذه فرصتك للدخول إلى مايكروسوفت. # البدء ## 📚 الوثائق -- **[دليل التثبيت](INSTALLATION.md)** - تعليمات إعداد خطوة بخطوة للمبتدئين -- **[دليل الاستخدام](USAGE.md)** - أمثلة وسير العمل الشائعة +- **[دليل التثبيت](INSTALLATION.md)** - تعليمات الإعداد خطوة بخطوة للمبتدئين +- **[دليل الاستخدام](USAGE.md)** - أمثلة وسير عمل شائعة - **[استكشاف الأخطاء وإصلاحها](TROUBLESHOOTING.md)** - حلول للمشاكل الشائعة - **[دليل المساهمة](CONTRIBUTING.md)** - كيفية المساهمة في هذا المشروع -- **[للمعلمين](for-teachers.md)** - إرشادات التدريس وموارد الفصل الدراسي +- **[للمعلمين](for-teachers.md)** - إرشادات التدريس وموارد الفصول الدراسية ## 👨‍🎓 للطلاب -> **المبتدئين تمامًا**: جديد في علم البيانات؟ ابدأ مع [الأمثلة المناسبة للمبتدئين](examples/README.md)! هذه الأمثلة البسيطة والمشروحة جيدًا ستساعدك على فهم الأساسيات قبل التعمق في المنهج الكامل. -> **[الطلاب](https://aka.ms/student-page)**: لاستخدام هذا المنهج بنفسك، قم بعمل نسخة من المستودع بالكامل وأكمل التمارين بنفسك، بدءًا من اختبار ما قبل المحاضرة. ثم اقرأ المحاضرة وأكمل بقية الأنشطة. حاول إنشاء المشاريع من خلال فهم الدروس بدلاً من نسخ كود الحل؛ ومع ذلك، يتوفر هذا الكود في مجلدات /solutions في كل درس قائم على المشروع. فكرة أخرى هي تشكيل مجموعة دراسة مع الأصدقاء ومراجعة المحتوى معًا. لمزيد من الدراسة، نوصي بـ [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). +> **للمبتدئين تمامًا**: جديد في علم البيانات؟ ابدأ مع [الأمثلة الصديقة للمبتدئين](examples/README.md)! ستساعدك هذه الأمثلة البسيطة والمشروحة جيدًا على فهم الأساسيات قبل التعمق في المنهج الكامل. +> **[الطلاب](https://aka.ms/student-page)**: لاستخدام هذا المنهج بمفردك، قم بعمل نسخة من المستودع بالكامل وأكمل التمارين بنفسك، بدءًا من اختبار ما قبل المحاضرة. ثم اقرأ المحاضرة وأكمل بقية الأنشطة. حاول إنشاء المشاريع من خلال فهم الدروس بدلاً من نسخ كود الحل؛ ومع ذلك، يتوفر هذا الكود في مجلدات /solutions في كل درس قائم على المشاريع. فكرة أخرى هي تشكيل مجموعة دراسية مع أصدقائك ومراجعة المحتوى معًا. لمزيد من الدراسة، نوصي بـ [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). **البدء السريع:** 1. تحقق من [دليل التثبيت](INSTALLATION.md) لإعداد بيئتك 2. راجع [دليل الاستخدام](USAGE.md) لتتعلم كيفية العمل مع المنهج 3. ابدأ بالدرس الأول واعمل بشكل متسلسل -4. انضم إلى [مجتمع Discord](https://aka.ms/ds4beginners/discord) للحصول على الدعم +4. انضم إلى [مجتمع Discord الخاص بنا](https://aka.ms/ds4beginners/discord) للحصول على الدعم ## 👩‍🏫 للمعلمين -> **المعلمين**: لقد قمنا [بتضمين بعض الاقتراحات](for-teachers.md) حول كيفية استخدام هذا المنهج. نود أن نسمع ملاحظاتكم [في منتدى المناقشة الخاص بنا](https://github.com/microsoft/Data-Science-For-Beginners/discussions)! +> **المعلمين**: لقد قمنا [بتضمين بعض الاقتراحات](for-teachers.md) حول كيفية استخدام هذا المنهج. نود أن نحصل على ملاحظاتكم [في منتدى النقاش الخاص بنا](https://github.com/microsoft/Data-Science-For-Beginners/discussions)! ## تعرف على الفريق [![فيديو ترويجي](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "فيديو ترويجي") -**Gif بواسطة** [موهيت جايسال](https://www.linkedin.com/in/mohitjaisal) +**الرسوم المتحركة بواسطة** [موهيت جايسال](https://www.linkedin.com/in/mohitjaisal) > 🎥 انقر على الصورة أعلاه لمشاهدة فيديو عن المشروع والأشخاص الذين قاموا بإنشائه! -## طريقة التدريس +## البيداغوجيا +لقد اخترنا مبدأين تربويين أثناء بناء هذا المنهج: التأكد من أنه قائم على المشاريع وأنه يتضمن اختبارات قصيرة متكررة. بحلول نهاية هذه السلسلة، سيكون الطلاب قد تعلموا المبادئ الأساسية لعلم البيانات، بما في ذلك المفاهيم الأخلاقية، إعداد البيانات، طرق مختلفة للعمل مع البيانات، تصور البيانات، تحليل البيانات، حالات استخدام علم البيانات في العالم الحقيقي، والمزيد. -لقد اخترنا مبدأين تعليميين أثناء بناء هذا المنهج: التأكد من أنه قائم على المشاريع وأنه يتضمن اختبارات متكررة. بحلول نهاية هذه السلسلة، سيتعلم الطلاب المبادئ الأساسية لعلم البيانات، بما في ذلك المفاهيم الأخلاقية، إعداد البيانات، طرق مختلفة للعمل مع البيانات، تصور البيانات، تحليل البيانات، حالات استخدام علم البيانات في العالم الحقيقي، والمزيد. -بالإضافة إلى ذلك، فإن إجراء اختبار بسيط قبل الحصة يساعد الطالب على التركيز على تعلم الموضوع، بينما يضمن اختبار ثانٍ بعد الحصة تعزيز الاحتفاظ بالمعلومات. تم تصميم هذا المنهج ليكون مرنًا وممتعًا ويمكن دراسته بالكامل أو جزئيًا. تبدأ المشاريع صغيرة وتصبح أكثر تعقيدًا مع نهاية دورة العشرة أسابيع. +بالإضافة إلى ذلك، فإن الاختبار القصير ذو المخاطر المنخفضة قبل الحصة يوجه نية الطالب نحو تعلم الموضوع، بينما يضمن الاختبار الثاني بعد الحصة تعزيز الفهم. تم تصميم هذا المنهج ليكون مرنًا وممتعًا ويمكن تناوله بالكامل أو جزئيًا. تبدأ المشاريع صغيرة وتصبح أكثر تعقيدًا بحلول نهاية الدورة التي تستمر 10 أسابيع. > اكتشف [مدونة السلوك](CODE_OF_CONDUCT.md)، [إرشادات المساهمة](CONTRIBUTING.md)، [إرشادات الترجمة](TRANSLATIONS.md). نحن نرحب بملاحظاتكم البناءة! ## كل درس يتضمن: -- رسم توضيحي اختياري +- رسم تخطيطي اختياري - فيديو إضافي اختياري - اختبار تمهيدي قبل الدرس - درس مكتوب -- بالنسبة للدروس القائمة على المشاريع، إرشادات خطوة بخطوة لبناء المشروع -- اختبارات المعرفة +- بالنسبة للدروس القائمة على المشاريع، إرشادات خطوة بخطوة حول كيفية بناء المشروع +- فحوصات المعرفة - تحدي - قراءة إضافية - واجب @@ -97,7 +114,7 @@ Azure Cloud Advocates في Microsoft يقدمون منهجًا دراسيًا ل ## 🎓 أمثلة مناسبة للمبتدئين -**جديد في علم البيانات؟** لقد أنشأنا دليلًا خاصًا [examples directory](examples/README.md) يحتوي على أكواد بسيطة ومشروحة جيدًا لمساعدتك على البدء: +**جديد في علم البيانات؟** لقد أنشأنا دليل [أمثلة خاص](examples/README.md) يحتوي على أكواد بسيطة ومشروحة جيدًا لمساعدتك على البدء: - 🌟 **Hello World** - أول برنامج لك في علم البيانات - 📂 **تحميل البيانات** - تعلم كيفية قراءة واستكشاف مجموعات البيانات @@ -111,58 +128,58 @@ Azure Cloud Advocates في Microsoft يقدمون منهجًا دراسيًا ل ## الدروس -|![ رسم توضيحي بواسطة @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ar.png)| +|![ رسم تخطيطي بواسطة @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ar.png)| |:---:| -| علم البيانات للمبتدئين: خارطة الطريق - _رسم توضيحي بواسطة [@nitya](https://twitter.com/nitya)_ | +| علم البيانات للمبتدئين: خارطة الطريق - _رسم تخطيطي بواسطة [@nitya](https://twitter.com/nitya)_ | | رقم الدرس | الموضوع | مجموعة الدروس | أهداف التعلم | رابط الدرس | المؤلف | | :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: | -| 01 | تعريف علم البيانات | [المقدمة](1-Introduction/README.md) | تعلم المفاهيم الأساسية لعلم البيانات وكيف يرتبط بالذكاء الاصطناعي، التعلم الآلي، والبيانات الضخمة. | [الدرس](1-Introduction/01-defining-data-science/README.md) [الفيديو](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | -| 02 | أخلاقيات علم البيانات | [المقدمة](1-Introduction/README.md) | مفاهيم أخلاقيات البيانات، التحديات، والأطر. | [الدرس](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | +| 01 | تعريف علم البيانات | [المقدمة](1-Introduction/README.md) | تعلم المفاهيم الأساسية وراء علم البيانات وكيف يرتبط بالذكاء الاصطناعي، التعلم الآلي، والبيانات الضخمة. | [الدرس](1-Introduction/01-defining-data-science/README.md) [الفيديو](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | +| 02 | أخلاقيات علم البيانات | [المقدمة](1-Introduction/README.md) | مفاهيم أخلاقيات البيانات، التحديات والأطر. | [الدرس](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | | 03 | تعريف البيانات | [المقدمة](1-Introduction/README.md) | كيفية تصنيف البيانات ومصادرها الشائعة. | [الدرس](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) | -| 04 | مقدمة في الإحصاء والاحتمالات | [المقدمة](1-Introduction/README.md) | التقنيات الرياضية للإحصاء والاحتمالات لفهم البيانات. | [الدرس](1-Introduction/04-stats-and-probability/README.md) [الفيديو](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | -| 05 | العمل مع البيانات العلائقية | [العمل مع البيانات](2-Working-With-Data/README.md) | مقدمة في البيانات العلائقية وأساسيات استكشاف وتحليل البيانات العلائقية باستخدام لغة SQL. | [الدرس](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | -| 06 | العمل مع بيانات NoSQL | [العمل مع البيانات](2-Working-With-Data/README.md) | مقدمة في البيانات غير العلائقية، أنواعها المختلفة، وأساسيات استكشاف وتحليل قواعد بيانات الوثائق. | [الدرس](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| -| 07 | العمل مع بايثون | [العمل مع البيانات](2-Working-With-Data/README.md) | أساسيات استخدام بايثون لاستكشاف البيانات باستخدام مكتبات مثل Pandas. يوصى بفهم أساسي لبرمجة بايثون. | [الدرس](2-Working-With-Data/07-python/README.md) [الفيديو](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | -| 08 | إعداد البيانات | [العمل مع البيانات](2-Working-With-Data/README.md) | مواضيع حول تقنيات تنظيف وتحويل البيانات للتعامل مع تحديات البيانات المفقودة أو غير الدقيقة أو غير المكتملة. | [الدرس](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | +| 04 | مقدمة في الإحصاء والاحتمالات | [المقدمة](1-Introduction/README.md) | التقنيات الرياضية للاحتمالات والإحصاء لفهم البيانات. | [الدرس](1-Introduction/04-stats-and-probability/README.md) [الفيديو](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | +| 05 | العمل مع البيانات العلائقية | [العمل مع البيانات](2-Working-With-Data/README.md) | مقدمة في البيانات العلائقية وأساسيات استكشاف وتحليل البيانات العلائقية باستخدام لغة الاستعلام الهيكلية، المعروفة أيضًا بـ SQL (تُنطق "سي-كويل"). | [الدرس](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | +| 06 | العمل مع بيانات NoSQL | [العمل مع البيانات](2-Working-With-Data/README.md) | مقدمة في البيانات غير العلائقية، أنواعها المختلفة وأساسيات استكشاف وتحليل قواعد بيانات الوثائق. | [الدرس](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| +| 07 | العمل مع Python | [العمل مع البيانات](2-Working-With-Data/README.md) | أساسيات استخدام Python لاستكشاف البيانات باستخدام مكتبات مثل Pandas. يوصى بفهم أساسي لبرمجة Python. | [الدرس](2-Working-With-Data/07-python/README.md) [الفيديو](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | +| 08 | إعداد البيانات | [العمل مع البيانات](2-Working-With-Data/README.md) | مواضيع حول تقنيات تنظيف وتحويل البيانات للتعامل مع تحديات البيانات المفقودة، غير الدقيقة، أو غير المكتملة. | [الدرس](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | | 09 | تصور الكميات | [تصور البيانات](3-Data-Visualization/README.md) | تعلم كيفية استخدام Matplotlib لتصور بيانات الطيور 🦆 | [الدرس](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) | -| 10 | تصور توزيع البيانات | [تصور البيانات](3-Data-Visualization/README.md) | تصور الملاحظات والاتجاهات ضمن فترة زمنية. | [الدرس](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) | +| 10 | تصور توزيع البيانات | [تصور البيانات](3-Data-Visualization/README.md) | تصور الملاحظات والاتجاهات ضمن نطاق معين. | [الدرس](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) | | 11 | تصور النسب | [تصور البيانات](3-Data-Visualization/README.md) | تصور النسب المئوية المنفصلة والمجمعة. | [الدرس](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) | | 12 | تصور العلاقات | [تصور البيانات](3-Data-Visualization/README.md) | تصور الروابط والارتباطات بين مجموعات البيانات ومتغيراتها. | [الدرس](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) | | 13 | تصورات ذات معنى | [تصور البيانات](3-Data-Visualization/README.md) | تقنيات وإرشادات لجعل تصوراتك ذات قيمة لحل المشكلات بشكل فعال واستخلاص الأفكار. | [الدرس](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) | -| 14 | مقدمة في دورة حياة علم البيانات | [دورة الحياة](4-Data-Science-Lifecycle/README.md) | مقدمة في دورة حياة علم البيانات وخطوتها الأولى في جمع واستخراج البيانات. | [الدرس](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | +| 14 | مقدمة في دورة حياة علم البيانات | [دورة الحياة](4-Data-Science-Lifecycle/README.md) | مقدمة في دورة حياة علم البيانات وخطوتها الأولى في الحصول على البيانات واستخراجها. | [الدرس](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | | 15 | التحليل | [دورة الحياة](4-Data-Science-Lifecycle/README.md) | تركز هذه المرحلة من دورة حياة علم البيانات على تقنيات تحليل البيانات. | [الدرس](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | | | 16 | التواصل | [دورة الحياة](4-Data-Science-Lifecycle/README.md) | تركز هذه المرحلة من دورة حياة علم البيانات على تقديم الأفكار المستخلصة من البيانات بطريقة تسهل على صناع القرار فهمها. | [الدرس](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | | 17 | علم البيانات في السحابة | [بيانات السحابة](5-Data-Science-In-Cloud/README.md) | تقدم هذه السلسلة من الدروس علم البيانات في السحابة وفوائده. | [الدرس](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) و [Maud](https://twitter.com/maudstweets) | | 18 | علم البيانات في السحابة | [بيانات السحابة](5-Data-Science-In-Cloud/README.md) | تدريب النماذج باستخدام أدوات Low Code. |[الدرس](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) و [Maud](https://twitter.com/maudstweets) | | 19 | علم البيانات في السحابة | [بيانات السحابة](5-Data-Science-In-Cloud/README.md) | نشر النماذج باستخدام Azure Machine Learning Studio. | [الدرس](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) و [Maud](https://twitter.com/maudstweets) | -| 20 | علم البيانات في العالم الواقعي | [في العالم الواقعي](6-Data-Science-In-Wild/README.md) | مشاريع مدفوعة بعلم البيانات في العالم الواقعي. | [الدرس](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) | +| 20 | علم البيانات في العالم الحقيقي | [في العالم الحقيقي](6-Data-Science-In-Wild/README.md) | مشاريع مدفوعة بعلم البيانات في العالم الحقيقي. | [الدرس](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) | ## GitHub Codespaces اتبع هذه الخطوات لفتح هذا المثال في Codespace: -1. انقر على القائمة المنسدلة Code واختر خيار Open with Codespaces. +1. انقر على قائمة Code المنسدلة واختر خيار Open with Codespaces. 2. اختر + New codespace في أسفل اللوحة. -للمزيد من المعلومات، تحقق من [وثائق GitHub](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace). +لمزيد من المعلومات، تحقق من [وثائق GitHub](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace). ## VSCode Remote - Containers -اتبع هذه الخطوات لفتح هذا المستودع في حاوية باستخدام جهازك المحلي وVSCode باستخدام إضافة VS Code Remote - Containers: +اتبع هذه الخطوات لفتح هذا المستودع في حاوية باستخدام جهازك المحلي وVSCode باستخدام امتداد VS Code Remote - Containers: 1. إذا كانت هذه هي المرة الأولى التي تستخدم فيها حاوية تطوير، يرجى التأكد من أن نظامك يلبي المتطلبات الأساسية (مثل تثبيت Docker) في [وثائق البدء](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started). -لاستخدام هذا المستودع، يمكنك فتحه في وحدة تخزين Docker معزولة: +لاستخدام هذا المستودع، يمكنك فتح المستودع في وحدة تخزين Docker معزولة: -**ملاحظة**: في الخلفية، سيتم استخدام أمر Remote-Containers: **Clone Repository in Container Volume...** لنسخ الكود المصدر في وحدة تخزين Docker بدلاً من نظام الملفات المحلي. [وحدات التخزين](https://docs.docker.com/storage/volumes/) هي الآلية المفضلة لتخزين بيانات الحاوية. +**ملاحظة**: في الخلفية، سيتم استخدام أمر Remote-Containers: **Clone Repository in Container Volume...** لاستنساخ الكود المصدر في وحدة تخزين Docker بدلاً من نظام الملفات المحلي. [الوحدات](https://docs.docker.com/storage/volumes/) هي الآلية المفضلة للحفاظ على بيانات الحاوية. -أو افتح نسخة محلية مستنسخة أو محملة من المستودع: +أو افتح نسخة مستنسخة أو محملة محليًا من المستودع: -- انسخ هذا المستودع إلى نظام الملفات المحلي لديك. -- اضغط F1 واختر الأمر **Remote-Containers: Open Folder in Container...**. -- اختر النسخة المستنسخة من هذا المجلد، انتظر حتى تبدأ الحاوية، وجرب الأمور. +- استنسخ هذا المستودع إلى نظام الملفات المحلي الخاص بك. +- اضغط على F1 واختر الأمر **Remote-Containers: Open Folder in Container...**. +- اختر النسخة المستنسخة من هذا المجلد، انتظر حتى تبدأ الحاوية، وجرب الأشياء. ## الوصول دون اتصال -يمكنك تشغيل هذا التوثيق دون اتصال باستخدام [Docsify](https://docsify.js.org/#/). قم بنسخ هذا المستودع، [تثبيت Docsify](https://docsify.js.org/#/quickstart) على جهازك المحلي، ثم في المجلد الرئيسي لهذا المستودع، اكتب `docsify serve`. سيتم تشغيل الموقع على المنفذ 3000 على localhost: `localhost:3000`. +يمكنك تشغيل هذا التوثيق دون اتصال باستخدام [Docsify](https://docsify.js.org/#/). قم باستنساخ هذا المستودع، [تثبيت Docsify](https://docsify.js.org/#/quickstart) على جهازك المحلي، ثم في المجلد الجذري لهذا المستودع، اكتب `docsify serve`. سيتم تقديم الموقع على المنفذ 3000 على localhost الخاص بك: `localhost:3000`. > ملاحظة، لن يتم عرض دفاتر الملاحظات عبر Docsify، لذا عندما تحتاج إلى تشغيل دفتر ملاحظات، قم بذلك بشكل منفصل في VS Code باستخدام نواة Python. @@ -181,14 +198,14 @@ Azure Cloud Advocates في Microsoft يقدمون منهجًا دراسيًا ل - [Bash للمبتدئين](https://github.com/microsoft/bash-for-beginners) - [التعلم الآلي للمبتدئين](https://aka.ms/ml-beginners) - [الأمن السيبراني للمبتدئين](https://github.com/microsoft/Security-101) -- [تطوير الويب للمبتدئين](https://aka.ms/webdev-beginners) -- [إنترنت الأشياء للمبتدئين](https://aka.ms/iot-beginners) -- [التعلم الآلي للمبتدئين](https://aka.ms/ml-beginners) -- [تطوير XR للمبتدئين](https://aka.ms/xr-dev-for-beginners) -- [إتقان GitHub Copilot للبرمجة المزدوجة باستخدام الذكاء الاصطناعي](https://aka.ms/GitHubCopilotAI) +- [تطوير الويب للمبتدئين](https://aka.ms/webdev-beginners) +- [إنترنت الأشياء للمبتدئين](https://aka.ms/iot-beginners) +- [تعلم الآلة للمبتدئين](https://aka.ms/ml-beginners) +- [تطوير الواقع الممتد للمبتدئين](https://aka.ms/xr-dev-for-beginners) +- [إتقان GitHub Copilot للبرمجة المزدوجة بالذكاء الاصطناعي](https://aka.ms/GitHubCopilotAI) - [تطوير الواقع الممتد للمبتدئين](https://github.com/microsoft/xr-development-for-beginners) - [إتقان GitHub Copilot لمطوري C#/.NET](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) -- [اختر مغامرتك الخاصة مع Copilot](https://github.com/microsoft/CopilotAdventures) +- [اختر مغامرتك مع Copilot](https://github.com/microsoft/CopilotAdventures) ## الحصول على المساعدة @@ -196,13 +213,13 @@ Azure Cloud Advocates في Microsoft يقدمون منهجًا دراسيًا ل إذا واجهت صعوبة أو كانت لديك أي أسئلة حول بناء تطبيقات الذكاء الاصطناعي، انضم إلى: -[![مجتمع Discord الخاص بـ Azure AI Foundry](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) +[![مجتمع Discord لمؤسسة Azure AI Foundry](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) إذا كانت لديك ملاحظات حول المنتج أو أخطاء أثناء البناء، قم بزيارة: -[![منتدى مطوري Azure AI Foundry](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![منتدى مطوري مؤسسة Azure AI Foundry](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- **إخلاء المسؤولية**: -تم ترجمة هذا المستند باستخدام خدمة الترجمة بالذكاء الاصطناعي [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو معلومات غير دقيقة. يجب اعتبار المستند الأصلي بلغته الأصلية المصدر الرسمي. للحصول على معلومات حاسمة، يُوصى بالاستعانة بترجمة بشرية احترافية. نحن غير مسؤولين عن أي سوء فهم أو تفسيرات خاطئة ناتجة عن استخدام هذه الترجمة. \ No newline at end of file +تم ترجمة هذا المستند باستخدام خدمة الترجمة بالذكاء الاصطناعي [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو عدم دقة. يجب اعتبار المستند الأصلي بلغته الأصلية المصدر الرسمي. للحصول على معلومات حاسمة، يُوصى بالاستعانة بترجمة بشرية احترافية. نحن غير مسؤولين عن أي سوء فهم أو تفسيرات خاطئة ناتجة عن استخدام هذه الترجمة. \ No newline at end of file diff --git a/translations/bg/README.md b/translations/bg/README.md index 52e4cd9f..b6c5b2e6 100644 --- a/translations/bg/README.md +++ b/translations/bg/README.md @@ -1,20 +1,36 @@ # Наука за данни за начинаещи - Учебна програма -Azure Cloud Advocates в Microsoft с удоволствие предлагат 10-седмична учебна програма с 20 урока, посветена на науката за данни. Всеки урок включва тестове преди и след урока, писмени инструкции за изпълнение на урока, решение и задача. Нашият подход, базиран на проекти, ви позволява да учите, докато създавате, доказан метод за усвояване на нови умения. +[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) + +[![GitHub license](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE) +[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/) +[![GitHub issues](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/) +[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/) +[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) + +[![GitHub watchers](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 forks](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 stars](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/) + +[![](https://dcbadge.vercel.app/api/server/ByRwuEEgH4)](https://discord.gg/zxKYvhSnVp?WT.mc_id=academic-000002-leestott) + +[![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) + +Екипът на Azure Cloud Advocates в Microsoft с удоволствие предлага 10-седмична учебна програма с 20 урока, посветена на науката за данни. Всеки урок включва тестове преди и след урока, писмени инструкции за изпълнение на задачите, решения и задания. Нашият подход, базиран на проекти, ви позволява да учите, докато създавате, доказан метод за усвояване на нови умения. **Сърдечни благодарности на нашите автори:** [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). **🙏 Специални благодарности 🙏 на нашите [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) автори, рецензенти и сътрудници на съдържание,** включително 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/) +[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/) |![Скица от @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.bg.png)| |:---:| @@ -22,24 +38,26 @@ Azure Cloud Advocates в Microsoft с удоволствие предлагат ### 🌐 Поддръжка на много езици -#### Поддържани чрез GitHub Action (Автоматизирано и винаги актуално) +#### Поддържано чрез GitHub Action (Автоматизирано и винаги актуално) -[Френски](../fr/README.md) | [Испански](../es/README.md) | [Немски](../de/README.md) | [Руски](../ru/README.md) | [Арабски](../ar/README.md) | [Персийски (фарси)](../fa/README.md) | [Урду](../ur/README.md) | [Китайски (опростен)](../zh/README.md) | [Китайски (традиционен, Макао)](../mo/README.md) | [Китайски (традиционен, Хонконг)](../hk/README.md) | [Китайски (традиционен, Тайван)](../tw/README.md) | [Японски](../ja/README.md) | [Корейски](../ko/README.md) | [Хинди](../hi/README.md) | [Бенгалски](../bn/README.md) | [Маратхи](../mr/README.md) | [Непалски](../ne/README.md) | [Пенджабски (Гурмуки)](../pa/README.md) | [Португалски (Португалия)](../pt/README.md) | [Португалски (Бразилия)](../br/README.md) | [Италиански](../it/README.md) | [Полски](../pl/README.md) | [Турски](../tr/README.md) | [Гръцки](../el/README.md) | [Тайландски](../th/README.md) | [Шведски](../sv/README.md) | [Датски](../da/README.md) | [Норвежки](../no/README.md) | [Фински](../fi/README.md) | [Холандски](../nl/README.md) | [Иврит](../he/README.md) | [Виетнамски](../vi/README.md) | [Индонезийски](../id/README.md) | [Малайски](../ms/README.md) | [Тагалог (Филипински)](../tl/README.md) | [Суахили](../sw/README.md) | [Унгарски](../hu/README.md) | [Чешки](../cs/README.md) | [Словашки](../sk/README.md) | [Румънски](../ro/README.md) | [Български](./README.md) | [Сръбски (кирилица)](../sr/README.md) | [Хърватски](../hr/README.md) | [Словенски](../sl/README.md) | [Украински](../uk/README.md) | [Бирмански (Мианмар)](../my/README.md) + +[Арабски](../ar/README.md) | [Бенгалски](../bn/README.md) | [Български](./README.md) | [Бирмански (Мианмар)](../my/README.md) | [Китайски (опростен)](../zh/README.md) | [Китайски (традиционен, Хонконг)](../hk/README.md) | [Китайски (традиционен, Макао)](../mo/README.md) | [Китайски (традиционен, Тайван)](../tw/README.md) | [Хърватски](../hr/README.md) | [Чешки](../cs/README.md) | [Датски](../da/README.md) | [Нидерландски](../nl/README.md) | [Естонски](../et/README.md) | [Фински](../fi/README.md) | [Френски](../fr/README.md) | [Немски](../de/README.md) | [Гръцки](../el/README.md) | [Иврит](../he/README.md) | [Хинди](../hi/README.md) | [Унгарски](../hu/README.md) | [Индонезийски](../id/README.md) | [Италиански](../it/README.md) | [Японски](../ja/README.md) | [Корейски](../ko/README.md) | [Литовски](../lt/README.md) | [Малайски](../ms/README.md) | [Маратхи](../mr/README.md) | [Непалски](../ne/README.md) | [Норвежки](../no/README.md) | [Персийски (фарси)](../fa/README.md) | [Полски](../pl/README.md) | [Португалски (Бразилия)](../br/README.md) | [Португалски (Португалия)](../pt/README.md) | [Панджаби (Гурмуки)](../pa/README.md) | [Румънски](../ro/README.md) | [Руски](../ru/README.md) | [Сръбски (кирилица)](../sr/README.md) | [Словашки](../sk/README.md) | [Словенски](../sl/README.md) | [Испански](../es/README.md) | [Суахили](../sw/README.md) | [Шведски](../sv/README.md) | [Тагалог (Филипински)](../tl/README.md) | [Тамилски](../ta/README.md) | [Тайландски](../th/README.md) | [Турски](../tr/README.md) | [Украински](../uk/README.md) | [Урду](../ur/README.md) | [Виетнамски](../vi/README.md) + **Ако желаете да добавите допълнителни преводи, списъкът с поддържани езици е [тук](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** #### Присъединете се към нашата общност [![Azure AI Discord](https://dcbadge.limes.pink/api/server/kzRShWzttr)](https://aka.ms/ds4beginners/discord) -Имаме текуща серия за обучение с AI в Discord, научете повече и се присъединете към нас на [Learn with AI Series](https://aka.ms/learnwithai/discord) от 18 до 30 септември 2025 г. Ще получите съвети и трикове за използване на GitHub Copilot за наука за данни. +Имаме текуща серия за обучение с AI в Discord. Научете повече и се присъединете към нас в [Learn with AI Series](https://aka.ms/learnwithai/discord) от 18 до 30 септември 2025 г. Ще получите съвети и трикове за използване на GitHub Copilot за наука за данни. ![Learn with AI series](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.bg.jpg) # Студент ли сте? -Започнете с следните ресурси: +Започнете с тези ресурси: -- [Студентска страница](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) На тази страница ще намерите ресурси за начинаещи, студентски пакети и дори начини да получите безплатен ваучер за сертификат. Това е страница, която трябва да запазите и проверявате от време на време, тъй като съдържанието се обновява поне веднъж месечно. +- [Студентска страница](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) На тази страница ще намерите ресурси за начинаещи, студентски пакети и дори начини да получите безплатен ваучер за сертификат. Това е страница, която си струва да запазите и да проверявате редовно, тъй като съдържанието се обновява поне веднъж месечно. - [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Присъединете се към глобална общност от студентски посланици, това може да бъде вашият път към Microsoft. # Започнете @@ -54,12 +72,12 @@ Azure Cloud Advocates в Microsoft с удоволствие предлагат ## 👨‍🎓 За студенти > **Напълно начинаещи**: Нови сте в науката за данни? Започнете с нашите [примери за начинаещи](examples/README.md)! Тези прости, добре коментирани примери ще ви помогнат да разберете основите, преди да се потопите в пълната учебна програма. -> **[Студенти](https://aka.ms/student-page)**: за да използвате тази учебна програма самостоятелно, клонирайте целия репозиторий и изпълнете упражненията самостоятелно, започвайки с тест преди лекцията. След това прочетете лекцията и изпълнете останалите дейности. Опитайте се да създадете проектите, като разбирате уроците, вместо да копирате кода на решението; въпреки това, този код е наличен в папките /solutions във всеки урок, ориентиран към проекти. Друга идея би била да създадете учебна група с приятели и да преминете през съдържанието заедно. За допълнително обучение препоръчваме [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). +> **[Студенти](https://aka.ms/student-page)**: за да използвате тази учебна програма самостоятелно, клонирайте целия репозиторий и изпълнете упражненията самостоятелно, започвайки с тест преди лекцията. След това прочетете лекцията и изпълнете останалите дейности. Опитайте се да създадете проектите, като разбирате уроците, вместо да копирате кода на решенията; въпреки това, този код е наличен в папките /solutions във всеки урок, ориентиран към проект. Друга идея е да сформирате учебна група с приятели и да преминете през съдържанието заедно. За допълнително обучение препоръчваме [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). **Бърз старт:** 1. Проверете [Ръководството за инсталация](INSTALLATION.md), за да настроите вашата среда 2. Прегледайте [Ръководството за използване](USAGE.md), за да научите как да работите с учебната програма -3. Започнете с Урок 1 и преминете последователно +3. Започнете с Урок 1 и преминавайте последователно 4. Присъединете се към нашата [Discord общност](https://aka.ms/ds4beginners/discord) за подкрепа ## 👩‍🏫 За учители @@ -75,26 +93,26 @@ Azure Cloud Advocates в Microsoft с удоволствие предлагат > 🎥 Кликнете върху изображението по-горе за видео за проекта и хората, които го създадоха! ## Педагогика +Ние избрахме два педагогически принципа при създаването на тази учебна програма: да бъде базирана на проекти и да включва чести тестове. До края на тази серия студентите ще са научили основните принципи на науката за данни, включително етични концепции, подготовка на данни, различни начини за работа с данни, визуализация на данни, анализ на данни, реални приложения на науката за данни и други. -Избрахме два педагогически принципа при създаването на тази учебна програма: да гарантираме, че тя е базирана на проекти и че включва чести тестове. До края на тази серия студентите ще са научили основни принципи на науката за данни, включително етични концепции, подготовка на данни, различни начини за работа с данни, визуализация на данни, анализ на данни, реални случаи на използване на науката за данни и други. -Освен това, тест с нисък залог преди часа насочва вниманието на ученика към изучаването на дадена тема, докато втори тест след часа гарантира по-добро запаметяване. Тази учебна програма е създадена да бъде гъвкава и забавна и може да се използва изцяло или частично. Проектите започват с малки задачи и стават все по-сложни до края на 10-седмичния цикъл. +Освен това, тест с нисък риск преди урока насочва вниманието на студента към изучаването на дадена тема, докато втори тест след урока гарантира по-добро запаметяване. Тази учебна програма е проектирана да бъде гъвкава и забавна и може да се премине изцяло или частично. Проектите започват с малки задачи и стават все по-сложни до края на 10-седмичния цикъл. > Намерете нашите [Правила за поведение](CODE_OF_CONDUCT.md), [Насоки за принос](CONTRIBUTING.md), [Насоки за превод](TRANSLATIONS.md). Очакваме вашата конструктивна обратна връзка! -## Всяка лекция включва: +## Всеки урок включва: -- По избор скица -- По избор допълнително видео -- Тест за загрявка преди лекцията -- Писмена лекция -- За лекции, базирани на проекти, ръководства стъпка по стъпка за изграждане на проекта +- По желание скица +- По желание допълнително видео +- Тест за загрявка преди урока +- Писмен урок +- За уроци, базирани на проекти, ръководства стъпка по стъпка за изграждане на проекта - Проверка на знанията - Предизвикателство - Допълнително четиво - Задача -- [Тест след лекцията](https://ff-quizzes.netlify.app/en/) +- [Тест след урока](https://ff-quizzes.netlify.app/en/) -> **Бележка относно тестовете**: Всички тестове се намират в папката Quiz-App, общо 40 теста с по три въпроса всеки. Те са свързани с лекциите, но приложението за тестове може да се стартира локално или да се разположи в Azure; следвайте инструкциите в папката `quiz-app`. Постепенно се локализират. +> **Бележка относно тестовете**: Всички тестове се намират в папката Quiz-App, общо 40 теста с по три въпроса всеки. Те са свързани с уроците, но приложението за тестове може да се стартира локално или да се разположи в Azure; следвайте инструкциите в папката `quiz-app`. Постепенно се локализират. ## 🎓 Примери за начинаещи @@ -102,42 +120,42 @@ Azure Cloud Advocates в Microsoft с удоволствие предлагат - 🌟 **Hello World** - Вашата първа програма за наука за данни - 📂 **Зареждане на данни** - Научете как да четете и изследвате набори от данни -- 📊 **Проста анализа** - Изчисляване на статистики и откриване на модели +- 📊 **Прост анализ** - Изчисляване на статистики и откриване на модели - 📈 **Основна визуализация** - Създаване на диаграми и графики -- 🔬 **Проект от реалния свят** - Пълен работен процес от начало до край +- 🔬 **Реален проект** - Пълен работен процес от начало до край Всеки пример включва подробни коментари, обясняващи всяка стъпка, което го прави идеален за абсолютни начинаещи! 👉 **[Започнете с примерите](examples/README.md)** 👈 -## Лекции +## Уроци |![ Скица от @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.bg.png)| |:---:| | Наука за данни за начинаещи: Пътна карта - _Скица от [@nitya](https://twitter.com/nitya)_ | -| Номер на лекцията | Тема | Групиране на лекции | Цели на обучението | Свързана лекция | Автор | +| Номер на урока | Тема | Групиране на урока | Цели на обучението | Свързан урок | Автор | | :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: | -| 01 | Определяне на науката за данни | [Въведение](1-Introduction/README.md) | Научете основните концепции зад науката за данни и как тя е свързана с изкуствения интелект, машинното обучение и големите данни. | [лекция](1-Introduction/01-defining-data-science/README.md) [видео](https://youtu.be/beZ7Mb_oz9I) | [Дмитрий](http://soshnikov.com) | -| 02 | Етика в науката за данни | [Въведение](1-Introduction/README.md) | Концепции за етика в данните, предизвикателства и рамки. | [лекция](1-Introduction/02-ethics/README.md) | [Нитя](https://twitter.com/nitya) | -| 03 | Определяне на данни | [Въведение](1-Introduction/README.md) | Как се класифицират данните и техните общи източници. | [лекция](1-Introduction/03-defining-data/README.md) | [Жасмин](https://www.twitter.com/paladique) | -| 04 | Въведение в статистиката и вероятностите | [Въведение](1-Introduction/README.md) | Математически техники за вероятности и статистика за разбиране на данни. | [лекция](1-Introduction/04-stats-and-probability/README.md) [видео](https://youtu.be/Z5Zy85g4Yjw) | [Дмитрий](http://soshnikov.com) | -| 05 | Работа с релационни данни | [Работа с данни](2-Working-With-Data/README.md) | Въведение в релационните данни и основите на изследването и анализа на релационни данни със Structured Query Language, известен като SQL (произнася се „си-квел“). | [лекция](2-Working-With-Data/05-relational-databases/README.md) | [Кристофър](https://www.twitter.com/geektrainer) | | | -| 06 | Работа с NoSQL данни | [Работа с данни](2-Working-With-Data/README.md) | Въведение в нерелационните данни, техните различни типове и основите на изследването и анализа на документни бази данни. | [лекция](2-Working-With-Data/06-non-relational/README.md) | [Жасмин](https://twitter.com/paladique)| -| 07 | Работа с Python | [Работа с данни](2-Working-With-Data/README.md) | Основи на използването на Python за изследване на данни с библиотеки като Pandas. Препоръчва се основно разбиране на програмирането с Python. | [лекция](2-Working-With-Data/07-python/README.md) [видео](https://youtu.be/dZjWOGbsN4Y) | [Дмитрий](http://soshnikov.com) | -| 08 | Подготовка на данни | [Работа с данни](2-Working-With-Data/README.md) | Теми за техники за почистване и трансформиране на данни за справяне с предизвикателства като липсващи, неточни или непълни данни. | [лекция](2-Working-With-Data/08-data-preparation/README.md) | [Жасмин](https://www.twitter.com/paladique) | -| 09 | Визуализиране на количества | [Визуализация на данни](3-Data-Visualization/README.md) | Научете как да използвате Matplotlib за визуализиране на данни за птици 🦆 | [лекция](3-Data-Visualization/09-visualization-quantities/README.md) | [Джен](https://twitter.com/jenlooper) | -| 10 | Визуализиране на разпределения на данни | [Визуализация на данни](3-Data-Visualization/README.md) | Визуализиране на наблюдения и тенденции в рамките на интервал. | [лекция](3-Data-Visualization/10-visualization-distributions/README.md) | [Джен](https://twitter.com/jenlooper) | -| 11 | Визуализиране на пропорции | [Визуализация на данни](3-Data-Visualization/README.md) | Визуализиране на дискретни и групирани проценти. | [лекция](3-Data-Visualization/11-visualization-proportions/README.md) | [Джен](https://twitter.com/jenlooper) | -| 12 | Визуализиране на връзки | [Визуализация на данни](3-Data-Visualization/README.md) | Визуализиране на връзки и корелации между набори от данни и техните променливи. | [лекция](3-Data-Visualization/12-visualization-relationships/README.md) | [Джен](https://twitter.com/jenlooper) | -| 13 | Смислени визуализации | [Визуализация на данни](3-Data-Visualization/README.md) | Техники и насоки за създаване на визуализации, които са ценни за ефективно решаване на проблеми и извличане на прозрения. | [лекция](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Джен](https://twitter.com/jenlooper) | -| 14 | Въведение в жизнения цикъл на науката за данни | [Жизнен цикъл](4-Data-Science-Lifecycle/README.md) | Въведение в жизнения цикъл на науката за данни и първата му стъпка - придобиване и извличане на данни. | [лекция](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Жасмин](https://twitter.com/paladique) | -| 15 | Анализиране | [Жизнен цикъл](4-Data-Science-Lifecycle/README.md) | Тази фаза от жизнения цикъл на науката за данни се фокусира върху техники за анализ на данни. | [лекция](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Жасмин](https://twitter.com/paladique) | | | -| 16 | Комуникация | [Жизнен цикъл](4-Data-Science-Lifecycle/README.md) | Тази фаза от жизнения цикъл на науката за данни се фокусира върху представянето на прозренията от данните по начин, който улеснява разбирането от страна на вземащите решения. | [лекция](4-Data-Science-Lifecycle/16-communication/README.md) | [Джейлън](https://twitter.com/JalenMcG) | | | -| 17 | Наука за данни в облака | [Данни в облака](5-Data-Science-In-Cloud/README.md) | Тази серия от лекции представя науката за данни в облака и нейните предимства. | [лекция](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Тифани](https://twitter.com/TiffanySouterre) и [Мод](https://twitter.com/maudstweets) | -| 18 | Наука за данни в облака | [Данни в облака](5-Data-Science-In-Cloud/README.md) | Обучение на модели с помощта на инструменти с нисък код. |[лекция](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Тифани](https://twitter.com/TiffanySouterre) и [Мод](https://twitter.com/maudstweets) | -| 19 | Наука за данни в облака | [Данни в облака](5-Data-Science-In-Cloud/README.md) | Разполагане на модели с Azure Machine Learning Studio. | [лекция](5-Data-Science-In-Cloud/19-Azure/README.md)| [Тифани](https://twitter.com/TiffanySouterre) и [Мод](https://twitter.com/maudstweets) | -| 20 | Наука за данни в реалния свят | [В реалния свят](6-Data-Science-In-Wild/README.md) | Проекти, базирани на науката за данни, в реалния свят. | [лекция](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Нитя](https://twitter.com/nitya) | +| 01 | Определяне на науката за данни | [Въведение](1-Introduction/README.md) | Научете основните концепции зад науката за данни и как тя е свързана с изкуствения интелект, машинното обучение и големите данни. | [урок](1-Introduction/01-defining-data-science/README.md) [видео](https://youtu.be/beZ7Mb_oz9I) | [Дмитрий](http://soshnikov.com) | +| 02 | Етика в науката за данни | [Въведение](1-Introduction/README.md) | Концепции за етика на данните, предизвикателства и рамки. | [урок](1-Introduction/02-ethics/README.md) | [Нитя](https://twitter.com/nitya) | +| 03 | Определяне на данни | [Въведение](1-Introduction/README.md) | Как се класифицират данните и техните общи източници. | [урок](1-Introduction/03-defining-data/README.md) | [Жасмин](https://www.twitter.com/paladique) | +| 04 | Въведение в статистиката и вероятността | [Въведение](1-Introduction/README.md) | Математическите техники на вероятността и статистиката за разбиране на данните. | [урок](1-Introduction/04-stats-and-probability/README.md) [видео](https://youtu.be/Z5Zy85g4Yjw) | [Дмитрий](http://soshnikov.com) | +| 05 | Работа с релационни данни | [Работа с данни](2-Working-With-Data/README.md) | Въведение в релационните данни и основите на изследването и анализа на релационни данни с езика за структурирани заявки, известен като SQL (произнася се „си-квел“). | [урок](2-Working-With-Data/05-relational-databases/README.md) | [Кристофър](https://www.twitter.com/geektrainer) | | | +| 06 | Работа с NoSQL данни | [Работа с данни](2-Working-With-Data/README.md) | Въведение в нерелационните данни, техните различни типове и основите на изследването и анализа на бази данни с документи. | [урок](2-Working-With-Data/06-non-relational/README.md) | [Жасмин](https://twitter.com/paladique)| +| 07 | Работа с Python | [Работа с данни](2-Working-With-Data/README.md) | Основи на използването на Python за изследване на данни с библиотеки като Pandas. Препоръчва се основно разбиране на програмирането с Python. | [урок](2-Working-With-Data/07-python/README.md) [видео](https://youtu.be/dZjWOGbsN4Y) | [Дмитрий](http://soshnikov.com) | +| 08 | Подготовка на данни | [Работа с данни](2-Working-With-Data/README.md) | Теми за техники за почистване и трансформиране на данни за справяне с предизвикателства като липсващи, неточни или непълни данни. | [урок](2-Working-With-Data/08-data-preparation/README.md) | [Жасмин](https://www.twitter.com/paladique) | +| 09 | Визуализиране на количества | [Визуализация на данни](3-Data-Visualization/README.md) | Научете как да използвате Matplotlib за визуализиране на данни за птици 🦆 | [урок](3-Data-Visualization/09-visualization-quantities/README.md) | [Джен](https://twitter.com/jenlooper) | +| 10 | Визуализиране на разпределения на данни | [Визуализация на данни](3-Data-Visualization/README.md) | Визуализиране на наблюдения и тенденции в рамките на интервал. | [урок](3-Data-Visualization/10-visualization-distributions/README.md) | [Джен](https://twitter.com/jenlooper) | +| 11 | Визуализиране на пропорции | [Визуализация на данни](3-Data-Visualization/README.md) | Визуализиране на дискретни и групирани проценти. | [урок](3-Data-Visualization/11-visualization-proportions/README.md) | [Джен](https://twitter.com/jenlooper) | +| 12 | Визуализиране на връзки | [Визуализация на данни](3-Data-Visualization/README.md) | Визуализиране на връзки и корелации между набори от данни и техните променливи. | [урок](3-Data-Visualization/12-visualization-relationships/README.md) | [Джен](https://twitter.com/jenlooper) | +| 13 | Смислени визуализации | [Визуализация на данни](3-Data-Visualization/README.md) | Техники и насоки за създаване на визуализации, които са ценни за ефективно решаване на проблеми и извличане на прозрения. | [урок](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Джен](https://twitter.com/jenlooper) | +| 14 | Въведение в жизнения цикъл на науката за данни | [Жизнен цикъл](4-Data-Science-Lifecycle/README.md) | Въведение в жизнения цикъл на науката за данни и първата му стъпка - придобиване и извличане на данни. | [урок](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Жасмин](https://twitter.com/paladique) | +| 15 | Анализиране | [Жизнен цикъл](4-Data-Science-Lifecycle/README.md) | Тази фаза от жизнения цикъл на науката за данни се фокусира върху техники за анализ на данни. | [урок](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Жасмин](https://twitter.com/paladique) | | | +| 16 | Комуникация | [Жизнен цикъл](4-Data-Science-Lifecycle/README.md) | Тази фаза от жизнения цикъл на науката за данни се фокусира върху представянето на прозренията от данните по начин, който улеснява разбирането от страна на вземащите решения. | [урок](4-Data-Science-Lifecycle/16-communication/README.md) | [Джейлън](https://twitter.com/JalenMcG) | | | +| 17 | Наука за данни в облака | [Данни в облака](5-Data-Science-In-Cloud/README.md) | Тази серия от уроци представя науката за данни в облака и нейните предимства. | [урок](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Тифани](https://twitter.com/TiffanySouterre) и [Мод](https://twitter.com/maudstweets) | +| 18 | Наука за данни в облака | [Данни в облака](5-Data-Science-In-Cloud/README.md) | Обучение на модели с помощта на инструменти с нисък код. |[урок](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Тифани](https://twitter.com/TiffanySouterre) и [Мод](https://twitter.com/maudstweets) | +| 19 | Наука за данни в облака | [Данни в облака](5-Data-Science-In-Cloud/README.md) | Разполагане на модели с Azure Machine Learning Studio. | [урок](5-Data-Science-In-Cloud/19-Azure/README.md)| [Тифани](https://twitter.com/TiffanySouterre) и [Мод](https://twitter.com/maudstweets) | +| 20 | Наука за данни в реалния свят | [В реалния свят](6-Data-Science-In-Wild/README.md) | Проекти, базирани на науката за данни, в реалния свят. | [урок](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Нитя](https://twitter.com/nitya) | ## GitHub Codespaces @@ -151,21 +169,21 @@ Azure Cloud Advocates в Microsoft с удоволствие предлагат 1. Ако за първи път използвате контейнер за разработка, уверете се, че вашата система отговаря на предварителните изисквания (например, инсталиран Docker) в [документацията за започване](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started). -За да използвате това хранилище, можете или да го отворите в изолиран Docker обем: +За да използвате това хранилище, можете да го отворите в изолиран Docker обем: -**Бележка**: В основата си това ще използва командата Remote-Containers: **Clone Repository in Container Volume...** за клониране на изходния код в Docker обем вместо в локалната файлова система. [Обемите](https://docs.docker.com/storage/volumes/) са предпочитаният механизъм за запазване на данни в контейнера. +**Бележка**: В основата си това ще използва командата Remote-Containers: **Clone Repository in Container Volume...**, за да клонира изходния код в Docker обем вместо в локалната файлова система. [Обемите](https://docs.docker.com/storage/volumes/) са предпочитаният механизъм за запазване на данни в контейнера. Или да отворите локално клонирана или изтеглена версия на хранилището: - Клонирайте това хранилище на вашата локална файлова система. - Натиснете F1 и изберете командата **Remote-Containers: Open Folder in Container...**. -- Изберете клонираното копие на тази папка, изчакайте контейнерът да стартира и опитайте нещата. +- Изберете клонираното копие на тази папка, изчакайте контейнерът да стартира и опитайте. ## Офлайн достъп Можете да стартирате тази документация офлайн, използвайки [Docsify](https://docsify.js.org/#/). Форкнете това хранилище, [инсталирайте Docsify](https://docsify.js.org/#/quickstart) на вашия локален компютър, след това в основната папка на това хранилище въведете `docsify serve`. Уебсайтът ще бъде достъпен на порт 3000 на вашия localhost: `localhost:3000`. -> Бележка, тетрадките няма да бъдат визуализирани чрез Docsify, така че когато трябва да стартирате тетрадка, направете това отделно в VS Code, използвайки Python kernel. +> Бележка: тетрадките няма да бъдат визуализирани чрез Docsify, така че когато трябва да стартирате тетрадка, направете го отделно в VS Code, използвайки Python kernel. ## Други учебни програми @@ -182,28 +200,28 @@ Azure Cloud Advocates в Microsoft с удоволствие предлагат - [Bash за начинаещи](https://github.com/microsoft/bash-for-beginners) - [ML за начинаещи](https://aka.ms/ml-beginners) - [Киберсигурност за начинаещи](https://github.com/microsoft/Security-101) -- [Уеб разработка за начинаещи](https://aka.ms/webdev-beginners) -- [IoT за начинаещи](https://aka.ms/iot-beginners) -- [Машинно обучение за начинаещи](https://aka.ms/ml-beginners) -- [XR разработка за начинаещи](https://aka.ms/xr-dev-for-beginners) -- [Овладяване на GitHub Copilot за AI програмиране в двойка](https://aka.ms/GitHubCopilotAI) -- [Разработка на XR за начинаещи](https://github.com/microsoft/xr-development-for-beginners) -- [Овладяване на GitHub Copilot за C#/.NET разработчици](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) -- [Избери своето приключение с Copilot](https://github.com/microsoft/CopilotAdventures) +- [Уеб разработка за начинаещи](https://aka.ms/webdev-beginners) +- [IoT за начинаещи](https://aka.ms/iot-beginners) +- [Машинно обучение за начинаещи](https://aka.ms/ml-beginners) +- [Разработка на XR за начинаещи](https://aka.ms/xr-dev-for-beginners) +- [Майсторство на GitHub Copilot за AI програмиране в екип](https://aka.ms/GitHubCopilotAI) +- [Разработка на XR за начинаещи](https://github.com/microsoft/xr-development-for-beginners) +- [Майсторство на GitHub Copilot за разработчици на C#/.NET](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) +- [Избери своето собствено приключение с Copilot](https://github.com/microsoft/CopilotAdventures) ## Получаване на помощ **Срещате проблеми?** Проверете нашето [Ръководство за отстраняване на проблеми](TROUBLESHOOTING.md) за решения на често срещани проблеми. -Ако се затрудните или имате въпроси относно създаването на AI приложения, присъединете се към: +Ако се затруднявате или имате въпроси относно създаването на AI приложения, присъединете се към: [![Azure AI Foundry Discord](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) -Ако имате обратна връзка за продукта или срещнете грешки по време на разработката, посетете: +Ако имате обратна връзка за продукт или грешки при разработката, посетете: [![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- **Отказ от отговорност**: -Този документ е преведен с помощта на AI услуга за превод [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматизираните преводи може да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Ние не носим отговорност за недоразумения или погрешни интерпретации, произтичащи от използването на този превод. \ No newline at end of file +Този документ е преведен с помощта на AI услуга за превод [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматизираните преводи може да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Ние не носим отговорност за каквито и да било недоразумения или погрешни интерпретации, произтичащи от използването на този превод. \ No newline at end of file diff --git a/translations/bn/README.md b/translations/bn/README.md index bdb8cb57..3ba3c6ae 100644 --- a/translations/bn/README.md +++ b/translations/bn/README.md @@ -1,52 +1,52 @@ -# ডেটা সায়েন্সের জন্য শিক্ষার্থীদের - একটি পাঠ্যক্রম +# ডেটা সায়েন্সের জন্য শিক্ষার্থীদের জন্য - একটি পাঠ্যক্রম [![GitHub Codespaces-এ খুলুন](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) [![GitHub লাইসেন্স](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE) [![GitHub অবদানকারী](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/) [![GitHub সমস্যা](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/) -[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/) -[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) +[![GitHub পুল-রিকোয়েস্ট](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/) +[![PRs স্বাগত](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) -[![GitHub watchers](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 forks](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 stars](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/) +[![GitHub পর্যবেক্ষক](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 ফর্ক](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 তারকা](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/) [![](https://dcbadge.vercel.app/api/server/ByRwuEEgH4)](https://discord.gg/zxKYvhSnVp?WT.mc_id=academic-000002-leestott) -[![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![Azure AI Foundry ডেভেলপার ফোরাম](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) -মাইক্রোসফটের Azure Cloud Advocates একটি ১০-সপ্তাহ, ২০-লেসনের পাঠ্যক্রম উপস্থাপন করতে পেরে আনন্দিত, যা সম্পূর্ণ ডেটা সায়েন্স নিয়ে। প্রতিটি পাঠে প্রাক-পাঠ এবং পর-পাঠ কুইজ, লিখিত নির্দেশনা, সমাধান এবং একটি অ্যাসাইনমেন্ট অন্তর্ভুক্ত রয়েছে। আমাদের প্রকল্প-ভিত্তিক শিক্ষাদান পদ্ধতি আপনাকে শেখার সময় তৈরি করতে সাহায্য করে, যা নতুন দক্ষতা অর্জনের একটি প্রমাণিত উপায়। +মাইক্রোসফটের Azure Cloud Advocates একটি ১০-সপ্তাহের, ২০-লেসনের পাঠ্যক্রম নিয়ে এসেছে যা সম্পূর্ণ ডেটা সায়েন্স নিয়ে। প্রতিটি পাঠে রয়েছে প্রাক-পাঠ এবং পর-পাঠ কুইজ, পাঠ সম্পন্ন করার জন্য লিখিত নির্দেশনা, একটি সমাধান এবং একটি অ্যাসাইনমেন্ট। আমাদের প্রকল্প-ভিত্তিক শিক্ষাদান পদ্ধতি আপনাকে শেখার সময় তৈরি করতে সাহায্য করে, যা নতুন দক্ষতা অর্জনের একটি প্রমাণিত উপায়। -**আমাদের লেখকদের প্রতি আন্তরিক ধন্যবাদ:** [জ্যাসমিন গ্রিনওয়ে](https://www.twitter.com/paladique), [দিমিত্রি সশনিকভ](http://soshnikov.com), [নিত্য নারাসিমহান](https://twitter.com/nitya), [জালেন ম্যাকগি](https://twitter.com/JalenMcG), [জেন লুপার](https://twitter.com/jenlooper), [মড লেভি](https://twitter.com/maudstweets), [টিফানি সাউটার](https://twitter.com/TiffanySouterre), [ক্রিস্টোফার হ্যারিসন](https://www.twitter.com/geektrainer)। +**আমাদের লেখকদের প্রতি আন্তরিক ধন্যবাদ:** [জেসমিন গ্রিনওয়ে](https://www.twitter.com/paladique), [দিমিত্রি সশনিকভ](http://soshnikov.com), [নিত্যা নারাসিমহান](https://twitter.com/nitya), [জালেন ম্যাকগি](https://twitter.com/JalenMcG), [জেন লুপার](https://twitter.com/jenlooper), [মড লেভি](https://twitter.com/maudstweets), [টিফানি সুটের](https://twitter.com/TiffanySouterre), [ক্রিস্টোফার হ্যারিসন](https://www.twitter.com/geektrainer)। -**🙏 বিশেষ ধন্যবাদ 🙏 আমাদের [মাইক্রোসফট স্টুডেন্ট অ্যাম্বাসেডর](https://studentambassadors.microsoft.com/) লেখক, পর্যালোচক এবং বিষয়বস্তু অবদানকারীদের প্রতি,** বিশেষত আরিয়ান অরোরা, [আদিত্য গার্গ](https://github.com/AdityaGarg00), [আলন্দ্রা সানচেজ](https://www.linkedin.com/in/alondra-sanchez-molina/), [অঙ্কিতা সিং](https://www.linkedin.com/in/ankitasingh007), [অনুপম মিশ্র](https://www.linkedin.com/in/anupam--mishra/), [অর্পিতা দাস](https://www.linkedin.com/in/arpitadas01/), চহাইলবিহারি দুবে, [ডিব্রি এনসোফর](https://www.linkedin.com/in/dibrinsofor), [দিশিতা ভাসিন](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [মাজদ সাফি](https://www.linkedin.com/in/majd-s/), [ম্যাক্স ব্লুম](https://www.linkedin.com/in/max-blum-6036a1186/), [মিগুয়েল কোরিয়া](https://www.linkedin.com/in/miguelmque/), [মোহাম্মা ইফতেখার (ইফটু) ইবনে জালাল](https://twitter.com/iftu119), [নাওরিন তাবাসসুম](https://www.linkedin.com/in/nawrin-tabassum), [রেমন্ড ওয়াংসা পুত্র](https://www.linkedin.com/in/raymond-wp/), [রোহিত যাদব](https://www.linkedin.com/in/rty2423), সমৃদ্ধি শর্মা, [সানিয়া সিনহা](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [শীনা নারুলা](https://www.linkedin.com/in/sheena-narua-n/), [তৌকির আহমদ](https://www.linkedin.com/in/tauqeerahmad5201/), যোগেন্দ্রসিংহ পাওয়ার, [বিদুষি গুপ্তা](https://www.linkedin.com/in/vidushi-gupta07/), [জাসলিন সোনধি](https://www.linkedin.com/in/jasleen-sondhi/)। +**🙏 বিশেষ ধন্যবাদ 🙏 আমাদের [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) লেখক, পর্যালোচক এবং কন্টেন্ট অবদানকারীদের,** বিশেষ করে আরিয়ান অরোরা, [আদিত্য গার্গ](https://github.com/AdityaGarg00), [আলন্দ্রা সানচেজ](https://www.linkedin.com/in/alondra-sanchez-molina/), [অঙ্কিতা সিং](https://www.linkedin.com/in/ankitasingh007), [অনুপম মিশ্র](https://www.linkedin.com/in/anupam--mishra/), [অর্পিতা দাস](https://www.linkedin.com/in/arpitadas01/), ছাইলবিহারী দুবে, [ডিব্রি এনসোফর](https://www.linkedin.com/in/dibrinsofor), [দিশিতা ভাসিন](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [মাজদ সাফি](https://www.linkedin.com/in/majd-s/), [ম্যাক্স ব্লুম](https://www.linkedin.com/in/max-blum-6036a1186/), [মিগুয়েল কোরিয়া](https://www.linkedin.com/in/miguelmque/), [মোহাম্মা ইফতেখার (ইফটু) ইবনে জালাল](https://twitter.com/iftu119), [নাওরিন তাবাসসুম](https://www.linkedin.com/in/nawrin-tabassum), [রেমন্ড ওয়াংসা পুত্র](https://www.linkedin.com/in/raymond-wp/), [রোহিত যাদব](https://www.linkedin.com/in/rty2423), সমৃদ্ধি শর্মা, [সানিয়া সিনহা](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [শীনা নারুলা](https://www.linkedin.com/in/sheena-narua-n/), [তৌকির আহমদ](https://www.linkedin.com/in/tauqeerahmad5201/), যোগেন্দ্রসিং পাওয়ার, [বিদুষী গুপ্তা](https://www.linkedin.com/in/vidushi-gupta07/), [জাসলিন সোধি](https://www.linkedin.com/in/jasleen-sondhi/)। -|![স্কেচনোট @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.bn.png)| +|![@sketchthedocs দ্বারা স্কেচনোট https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.bn.png)| |:---:| -| ডেটা সায়েন্স শিক্ষার্থীদের জন্য - _স্কেচনোট [@nitya](https://twitter.com/nitya)_ | +| শিক্ষার্থীদের জন্য ডেটা সায়েন্স - _[@nitya](https://twitter.com/nitya) দ্বারা স্কেচনোট_ | -### 🌐 বহু-ভাষার সমর্থন +### 🌐 বহু-ভাষা সমর্থন -#### GitHub Action এর মাধ্যমে সমর্থিত (স্বয়ংক্রিয় এবং সর্বদা আপডেট) +#### GitHub Action এর মাধ্যমে সমর্থিত (স্বয়ংক্রিয় এবং সর্বদা আপডেটেড) -[ফরাসি](../fr/README.md) | [স্প্যানিশ](../es/README.md) | [জার্মান](../de/README.md) | [রাশিয়ান](../ru/README.md) | [আরবি](../ar/README.md) | [ফার্সি](../fa/README.md) | [উর্দু](../ur/README.md) | [চীনা (সরলীকৃত)](../zh/README.md) | [চীনা (প্রথাগত, ম্যাকাও)](../mo/README.md) | [চীনা (প্রথাগত, হংকং)](../hk/README.md) | [চীনা (প্রথাগত, তাইওয়ান)](../tw/README.md) | [জাপানি](../ja/README.md) | [কোরিয়ান](../ko/README.md) | [হিন্দি](../hi/README.md) | [বাংলা](./README.md) | [মারাঠি](../mr/README.md) | [নেপালি](../ne/README.md) | [পাঞ্জাবি (গুরমুখী)](../pa/README.md) | [পর্তুগিজ (পর্তুগাল)](../pt/README.md) | [পর্তুগিজ (ব্রাজিল)](../br/README.md) | [ইতালিয়ান](../it/README.md) | [পোলিশ](../pl/README.md) | [তুর্কি](../tr/README.md) | [গ্রিক](../el/README.md) | [থাই](../th/README.md) | [সুইডিশ](../sv/README.md) | [ড্যানিশ](../da/README.md) | [নরওয়েজিয়ান](../no/README.md) | [ফিনিশ](../fi/README.md) | [ডাচ](../nl/README.md) | [হিব্রু](../he/README.md) | [ভিয়েতনামি](../vi/README.md) | [ইন্দোনেশিয়ান](../id/README.md) | [মালয়](../ms/README.md) | [টাগালগ (ফিলিপিনো)](../tl/README.md) | [সোয়াহিলি](../sw/README.md) | [হাঙ্গেরিয়ান](../hu/README.md) | [চেক](../cs/README.md) | [স্লোভাক](../sk/README.md) | [রোমানিয়ান](../ro/README.md) | [বুলগেরিয়ান](../bg/README.md) | [সার্বিয়ান (সিরিলিক)](../sr/README.md) | [ক্রোয়েশিয়ান](../hr/README.md) | [স্লোভেনিয়ান](../sl/README.md) | [ইউক্রেনিয়ান](../uk/README.md) | [বর্মি (মায়ানমার)](../my/README.md) +[আরবি](../ar/README.md) | [বাংলা](./README.md) | [বুলগেরিয়ান](../bg/README.md) | [বার্মিজ (মিয়ানমার)](../my/README.md) | [চীনা (সরলীকৃত)](../zh/README.md) | [চীনা (প্রথাগত, হংকং)](../hk/README.md) | [চীনা (প্রথাগত, ম্যাকাও)](../mo/README.md) | [চীনা (প্রথাগত, তাইওয়ান)](../tw/README.md) | [ক্রোয়েশিয়ান](../hr/README.md) | [চেক](../cs/README.md) | [ড্যানিশ](../da/README.md) | [ডাচ](../nl/README.md) | [এস্তোনিয়ান](../et/README.md) | [ফিনিশ](../fi/README.md) | [ফরাসি](../fr/README.md) | [জার্মান](../de/README.md) | [গ্রিক](../el/README.md) | [হিব্রু](../he/README.md) | [হিন্দি](../hi/README.md) | [হাঙ্গেরিয়ান](../hu/README.md) | [ইন্দোনেশিয়ান](../id/README.md) | [ইতালিয়ান](../it/README.md) | [জাপানি](../ja/README.md) | [কোরিয়ান](../ko/README.md) | [লিথুয়ানিয়ান](../lt/README.md) | [মালয়](../ms/README.md) | [মারাঠি](../mr/README.md) | [নেপালি](../ne/README.md) | [নরওয়েজিয়ান](../no/README.md) | [ফার্সি (পার্সিয়ান)](../fa/README.md) | [পোলিশ](../pl/README.md) | [পর্তুগিজ (ব্রাজিল)](../br/README.md) | [পর্তুগিজ (পর্তুগাল)](../pt/README.md) | [পাঞ্জাবি (গুরুমুখী)](../pa/README.md) | [রোমানিয়ান](../ro/README.md) | [রাশিয়ান](../ru/README.md) | [সার্বিয়ান (সিরিলিক)](../sr/README.md) | [স্লোভাক](../sk/README.md) | [স্লোভেনিয়ান](../sl/README.md) | [স্প্যানিশ](../es/README.md) | [সোয়াহিলি](../sw/README.md) | [সুইডিশ](../sv/README.md) | [তাগালগ (ফিলিপিনো)](../tl/README.md) | [তামিল](../ta/README.md) | [থাই](../th/README.md) | [তুর্কি](../tr/README.md) | [ইউক্রেনীয়](../uk/README.md) | [উর্দু](../ur/README.md) | [ভিয়েতনামিজ](../vi/README.md) **যদি আপনি অতিরিক্ত অনুবাদ চান, সমর্থিত ভাষার তালিকা [এখানে](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** -#### আমাদের কমিউনিটিতে যোগ দিন +#### আমাদের সম্প্রদায়ে যোগ দিন [![Azure AI Discord](https://dcbadge.limes.pink/api/server/kzRShWzttr)](https://aka.ms/ds4beginners/discord) -আমাদের Discord-এ AI শেখার সিরিজ চলছে, আরও জানুন এবং আমাদের সাথে যোগ দিন [Learn with AI Series](https://aka.ms/learnwithai/discord) ১৮ - ৩০ সেপ্টেম্বর, ২০২৫। আপনি GitHub Copilot ব্যবহার করে ডেটা সায়েন্সের টিপস এবং কৌশল শিখতে পারবেন। +আমাদের Discord-এ AI শেখার একটি সিরিজ চলছে। আরও জানুন এবং আমাদের সাথে যোগ দিন [Learn with AI Series](https://aka.ms/learnwithai/discord) ১৮ - ৩০ সেপ্টেম্বর, ২০২৫। এখানে আপনি GitHub Copilot ব্যবহার করে ডেটা সায়েন্সের টিপস এবং কৌশল শিখতে পারবেন। ![AI শেখার সিরিজ](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.bn.jpg) @@ -54,32 +54,32 @@ CO_OP_TRANSLATOR_METADATA: নিম্নলিখিত রিসোর্স দিয়ে শুরু করুন: -- [স্টুডেন্ট হাব পেজ](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) এই পেজে আপনি শিক্ষার্থীদের জন্য রিসোর্স, স্টুডেন্ট প্যাক এবং এমনকি বিনামূল্যে সার্টিফিকেট ভাউচার পাওয়ার উপায় খুঁজে পাবেন। এটি একটি পেজ যা আপনি বুকমার্ক করতে এবং সময়ে সময়ে চেক করতে চাইবেন কারণ আমরা অন্তত মাসিকভাবে বিষয়বস্তু পরিবর্তন করি। -- [মাইক্রোসফট লার্ন স্টুডেন্ট অ্যাম্বাসেডর](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) একটি বৈশ্বিক শিক্ষার্থী অ্যাম্বাসেডর কমিউনিটিতে যোগ দিন, এটি মাইক্রোসফটে প্রবেশের একটি উপায় হতে পারে। +- [স্টুডেন্ট হাব পৃষ্ঠা](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) এই পৃষ্ঠায় আপনি পাবেন শিক্ষার্থীদের জন্য রিসোর্স, স্টুডেন্ট প্যাক এবং এমনকি বিনামূল্যে সার্টিফিকেট ভাউচার পাওয়ার উপায়। এটি একটি পৃষ্ঠা যা আপনি বুকমার্ক করতে এবং সময়ে সময়ে চেক করতে চাইবেন কারণ আমরা অন্তত মাসিকভাবে বিষয়বস্তু পরিবর্তন করি। +- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) একটি বৈশ্বিক শিক্ষার্থী অ্যাম্বাসেডর সম্প্রদায়ে যোগ দিন, এটি মাইক্রোসফটে আপনার প্রবেশের পথ হতে পারে। -# শুরু করা +# শুরু করা যাক ## 📚 ডকুমেন্টেশন -- **[ইনস্টলেশন গাইড](INSTALLATION.md)** - নতুনদের জন্য ধাপে ধাপে সেটআপ নির্দেশনা +- **[ইনস্টলেশন গাইড](INSTALLATION.md)** - নতুনদের জন্য ধাপে ধাপে সেটআপ নির্দেশিকা - **[ব্যবহার গাইড](USAGE.md)** - উদাহরণ এবং সাধারণ কার্যপ্রণালী - **[সমস্যা সমাধান](TROUBLESHOOTING.md)** - সাধারণ সমস্যার সমাধান - **[অবদান গাইড](CONTRIBUTING.md)** - এই প্রকল্পে অবদান রাখার উপায় -- **[শিক্ষকদের জন্য](for-teachers.md)** - শিক্ষাদানের নির্দেশনা এবং শ্রেণীকক্ষের রিসোর্স +- **[শিক্ষকদের জন্য](for-teachers.md)** - শিক্ষাদানের নির্দেশিকা এবং শ্রেণীকক্ষের রিসোর্স ## 👨‍🎓 শিক্ষার্থীদের জন্য -> **সম্পূর্ণ নতুনরা**: ডেটা সায়েন্সে নতুন? আমাদের [নতুনদের জন্য উদাহরণ](examples/README.md) দিয়ে শুরু করুন! এই সহজ, ভালোভাবে মন্তব্যযুক্ত উদাহরণগুলো আপনাকে মৌলিক বিষয়গুলো বুঝতে সাহায্য করবে পূর্ণ পাঠ্যক্রমে প্রবেশ করার আগে। -> **[শিক্ষার্থীরা](https://aka.ms/student-page)**: এই পাঠ্যক্রমটি নিজেরাই ব্যবহার করতে চাইলে, পুরো রিপোজিটরি ফর্ক করুন এবং নিজেরাই অনুশীলন সম্পন্ন করুন, প্রাক-লেকচার কুইজ দিয়ে শুরু করুন। তারপর লেকচার পড়ুন এবং বাকি কার্যক্রম সম্পন্ন করুন। পাঠগুলো বুঝে প্রকল্প তৈরি করার চেষ্টা করুন, সমাধান কোড কপি না করে; তবে, সেই কোডটি প্রতিটি প্রকল্প-ভিত্তিক পাঠের /solutions ফোল্ডারে উপলব্ধ। আরেকটি ধারণা হতে পারে বন্ধুদের সাথে একটি স্টাডি গ্রুপ তৈরি করা এবং একসাথে বিষয়বস্তু নিয়ে কাজ করা। আরও অধ্যয়নের জন্য, আমরা [মাইক্রোসফট লার্ন](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum) সুপারিশ করি। +> **সম্পূর্ণ নতুনরা**: ডেটা সায়েন্সে নতুন? আমাদের [শিক্ষার্থী-বান্ধব উদাহরণ](examples/README.md) দিয়ে শুরু করুন! এই সহজ, ভালোভাবে মন্তব্য করা উদাহরণগুলো আপনাকে মৌলিক বিষয়গুলো বুঝতে সাহায্য করবে পূর্ণ পাঠ্যক্রমে প্রবেশ করার আগে। +> **[শিক্ষার্থীরা](https://aka.ms/student-page)**: এই পাঠ্যক্রমটি নিজেরাই ব্যবহার করতে চাইলে, পুরো রিপোজিটরি ফর্ক করুন এবং নিজেরাই অনুশীলন সম্পন্ন করুন, একটি প্রাক-পাঠ কুইজ দিয়ে শুরু করুন। তারপর লেকচার পড়ুন এবং বাকি কার্যক্রম সম্পন্ন করুন। পাঠগুলো বুঝে প্রকল্প তৈরি করার চেষ্টা করুন, সমাধান কোড কপি না করে; তবে, সেই কোড /solutions ফোল্ডারে পাওয়া যাবে প্রতিটি প্রকল্প-ভিত্তিক পাঠে। আরেকটি ধারণা হতে পারে বন্ধুদের সাথে একটি স্টাডি গ্রুপ তৈরি করা এবং একসাথে বিষয়বস্তু নিয়ে কাজ করা। আরও অধ্যয়নের জন্য, আমরা [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum) সুপারিশ করি। **দ্রুত শুরু:** 1. আপনার পরিবেশ সেটআপ করতে [ইনস্টলেশন গাইড](INSTALLATION.md) চেক করুন -2. পাঠ্যক্রমের সাথে কাজ করার পদ্ধতি শিখতে [ব্যবহার গাইড](USAGE.md) পর্যালোচনা করুন -3. প্রথম পাঠ দিয়ে শুরু করুন এবং ক্রমান্বয়ে কাজ করুন -4. সহায়তার জন্য আমাদের [Discord কমিউনিটি](https://aka.ms/ds4beginners/discord)-তে যোগ দিন +2. পাঠ্যক্রমের সাথে কাজ করার জন্য [ব্যবহার গাইড](USAGE.md) পর্যালোচনা করুন +3. প্রথম পাঠ দিয়ে শুরু করুন এবং ক্রমান্বয়ে এগিয়ে যান +4. সহায়তার জন্য আমাদের [Discord সম্প্রদায়ে](https://aka.ms/ds4beginners/discord) যোগ দিন ## 👩‍🏫 শিক্ষকদের জন্য -> **শিক্ষকরা**: আমরা এই পাঠ্যক্রমটি কীভাবে ব্যবহার করবেন তার উপর [কিছু পরামর্শ](for-teachers.md) অন্তর্ভুক্ত করেছি। আমাদের [আলোচনা ফোরামে](https://github.com/microsoft/Data-Science-For-Beginners/discussions) আপনার মতামত জানাতে চাই! +> **শিক্ষকরা**: আমরা এই পাঠ্যক্রমটি কীভাবে ব্যবহার করবেন সে সম্পর্কে [কিছু পরামর্শ](for-teachers.md) অন্তর্ভুক্ত করেছি। আমাদের [আলোচনা ফোরামে](https://github.com/microsoft/Data-Science-For-Beginners/discussions) আপনার মতামত জানাতে চাই! ## টিমের সাথে পরিচিত হন @@ -87,29 +87,29 @@ CO_OP_TRANSLATOR_METADATA: **Gif তৈরি করেছেন** [মোহিত জয়সল](https://www.linkedin.com/in/mohitjaisal) -> 🎥 উপরের ছবিতে ক্লিক করুন প্রকল্প এবং এটি তৈরি করা ব্যক্তিদের সম্পর্কে একটি ভিডিও দেখতে! +> 🎥 উপরের ছবিতে ক্লিক করুন প্রকল্প এবং এটি তৈরি করা ব্যক্তিদের সম্পর্কে একটি ভিডিও দেখার জন্য! -## শিক্ষাদানের পদ্ধতি +## শিক্ষাদান পদ্ধতি +আমরা এই পাঠক্রম তৈরি করার সময় দুটি শিক্ষামূলক নীতিকে বেছে নিয়েছি: এটি প্রকল্প-ভিত্তিক হওয়া নিশ্চিত করা এবং ঘন ঘন কুইজ অন্তর্ভুক্ত করা। এই সিরিজের শেষে, শিক্ষার্থীরা ডেটা সায়েন্সের মৌলিক নীতিগুলি শিখবে, যার মধ্যে রয়েছে নৈতিক ধারণা, ডেটা প্রস্তুতি, ডেটার সাথে কাজ করার বিভিন্ন উপায়, ডেটা ভিজ্যুয়ালাইজেশন, ডেটা বিশ্লেষণ, বাস্তব জীবনের ব্যবহারিক উদাহরণ এবং আরও অনেক কিছু। -আমরা এই পাঠ্যক্রম তৈরি করার সময় দুটি শিক্ষাদানের নীতি বেছে নিয়েছি: এটি প্রকল্প-ভিত্তিক নিশ্চিত করা এবং এতে ঘন ঘন কুইজ অন্তর্ভুক্ত করা। এই সিরিজের শেষে, শিক্ষার্থীরা ডেটা সায়েন্সের মৌলিক নীতিগুলি শিখবে, যার মধ্যে রয়েছে নৈতিক ধারণা, ডেটা প্রস্তুতি, ডেটার সাথে কাজ করার বিভিন্ন উপায়, ডেটা ভিজ্যুয়ালাইজেশন, ডেটা বিশ্লেষণ, ডেটা সায়েন্সের বাস্তব জীবনের ব্যবহার এবং আরও অনেক কিছু। -অতিরিক্তভাবে, ক্লাসের আগে একটি কম ঝুঁকির কুইজ শিক্ষার্থীর মনোযোগকে একটি বিষয় শেখার দিকে নিয়ে যায়, আর ক্লাসের পরে একটি দ্বিতীয় কুইজ আরও ভালোভাবে বিষয়টি মনে রাখার নিশ্চয়তা দেয়। এই পাঠক্রমটি নমনীয় এবং মজাদারভাবে ডিজাইন করা হয়েছে এবং এটি সম্পূর্ণ বা আংশিকভাবে নেওয়া যেতে পারে। প্রকল্পগুলো ছোট থেকে শুরু হয় এবং ১০ সপ্তাহের চক্রের শেষে ক্রমশ জটিল হয়ে ওঠে। +এছাড়াও, ক্লাসের আগে একটি কম ঝুঁকিপূর্ণ কুইজ শিক্ষার্থীর মনোযোগ বিষয় শেখার দিকে নিয়ে যায়, এবং ক্লাসের পরে একটি দ্বিতীয় কুইজ আরও ভালোভাবে মনে রাখার জন্য সহায়ক হয়। এই পাঠক্রমটি নমনীয় এবং মজাদারভাবে ডিজাইন করা হয়েছে এবং এটি সম্পূর্ণ বা আংশিকভাবে নেওয়া যেতে পারে। প্রকল্পগুলি ছোট থেকে শুরু করে এবং ১০ সপ্তাহের চক্রের শেষে ক্রমশ জটিল হয়ে ওঠে। -> আমাদের [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translation](TRANSLATIONS.md) নির্দেশিকা দেখুন। আমরা আপনার গঠনমূলক মতামতকে স্বাগত জানাই! +> আমাদের [আচরণবিধি](CODE_OF_CONDUCT.md), [অবদান](CONTRIBUTING.md), [অনুবাদ](TRANSLATIONS.md) নির্দেশিকা দেখুন। আমরা আপনার গঠনমূলক মতামতকে স্বাগত জানাই! ## প্রতিটি পাঠ অন্তর্ভুক্ত করে: - ঐচ্ছিক স্কেচনোট - ঐচ্ছিক সম্পূরক ভিডিও -- প্রাক-পাঠ উষ্ণতা কুইজ +- পাঠের আগে প্রস্তুতিমূলক কুইজ - লিখিত পাঠ - প্রকল্প-ভিত্তিক পাঠের জন্য, প্রকল্প তৈরি করার ধাপে ধাপে নির্দেশিকা - জ্ঞান যাচাই - একটি চ্যালেঞ্জ - সম্পূরক পাঠ্য - অ্যাসাইনমেন্ট -- [পোস্ট-পাঠ কুইজ](https://ff-quizzes.netlify.app/en/) +- [পাঠ-পরবর্তী কুইজ](https://ff-quizzes.netlify.app/en/) -> **কুইজ সম্পর্কে একটি নোট**: সমস্ত কুইজ `Quiz-App` ফোল্ডারে অন্তর্ভুক্ত রয়েছে, যেখানে মোট ৪০টি কুইজ রয়েছে, প্রতিটিতে তিনটি প্রশ্ন। এগুলো পাঠের মধ্যে থেকে সংযুক্ত করা হয়েছে, তবে কুইজ অ্যাপটি স্থানীয়ভাবে চালানো বা Azure-এ ডিপ্লয় করা যেতে পারে; `quiz-app` ফোল্ডারের নির্দেশনা অনুসরণ করুন। এগুলো ধীরে ধীরে স্থানীয় ভাষায় অনুবাদ করা হচ্ছে। +> **কুইজ সম্পর্কে একটি নোট**: সমস্ত কুইজ Quiz-App ফোল্ডারে অন্তর্ভুক্ত, মোট ৪০টি কুইজ, প্রতিটিতে তিনটি প্রশ্ন। এগুলি পাঠের মধ্যে থেকে লিঙ্ক করা হয়েছে, তবে কুইজ অ্যাপটি স্থানীয়ভাবে চালানো বা Azure-এ ডিপ্লয় করা যেতে পারে; `quiz-app` ফোল্ডারে নির্দেশনা অনুসরণ করুন। এগুলি ধীরে ধীরে স্থানীয় ভাষায় অনুবাদ করা হচ্ছে। ## 🎓 নবীন-বান্ধব উদাহরণ @@ -119,7 +119,7 @@ CO_OP_TRANSLATOR_METADATA: - 📂 **ডেটা লোড করা** - ডেটাসেট পড়া এবং অন্বেষণ করা শিখুন - 📊 **সহজ বিশ্লেষণ** - পরিসংখ্যান গণনা এবং প্যাটার্ন খুঁজে বের করা - 📈 **মৌলিক ভিজ্যুয়ালাইজেশন** - চার্ট এবং গ্রাফ তৈরি করা -- 🔬 **বাস্তব প্রকল্প** - শুরু থেকে শেষ পর্যন্ত সম্পূর্ণ কর্মপ্রবাহ +- 🔬 **বাস্তব জীবনের প্রকল্প** - শুরু থেকে শেষ পর্যন্ত সম্পূর্ণ কর্মপ্রবাহ প্রতিটি উদাহরণে প্রতিটি ধাপের বিস্তারিত মন্তব্য রয়েছে, যা একেবারে নবীনদের জন্য উপযুক্ত! @@ -131,60 +131,60 @@ CO_OP_TRANSLATOR_METADATA: |:---:| | ডেটা সায়েন্স ফর বিগিনার্স: রোডম্যাপ - _স্কেচনোট [@nitya](https://twitter.com/nitya) দ্বারা_ | -| পাঠ নম্বর | বিষয় | পাঠের গ্রুপিং | শেখার উদ্দেশ্য | সংযুক্ত পাঠ | লেখক | +| পাঠ নম্বর | বিষয় | পাঠের গ্রুপিং | শেখার উদ্দেশ্য | লিঙ্ককৃত পাঠ | লেখক | | :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: | | 01 | ডেটা সায়েন্স সংজ্ঞায়িত করা | [ভূমিকা](1-Introduction/README.md) | ডেটা সায়েন্সের মৌলিক ধারণা এবং এটি কৃত্রিম বুদ্ধিমত্তা, মেশিন লার্নিং এবং বিগ ডেটার সাথে কীভাবে সম্পর্কিত তা শিখুন। | [পাঠ](1-Introduction/01-defining-data-science/README.md) [ভিডিও](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | -| 02 | ডেটা সায়েন্স নৈতিকতা | [ভূমিকা](1-Introduction/README.md) | ডেটা নৈতিকতার ধারণা, চ্যালেঞ্জ এবং কাঠামো। | [পাঠ](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | +| 02 | ডেটা সায়েন্সের নৈতিকতা | [ভূমিকা](1-Introduction/README.md) | ডেটা নৈতিকতার ধারণা, চ্যালেঞ্জ এবং কাঠামো। | [পাঠ](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | | 03 | ডেটা সংজ্ঞায়িত করা | [ভূমিকা](1-Introduction/README.md) | ডেটা কীভাবে শ্রেণীবদ্ধ করা হয় এবং এর সাধারণ উৎস। | [পাঠ](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) | | 04 | পরিসংখ্যান ও সম্ভাবনার পরিচিতি | [ভূমিকা](1-Introduction/README.md) | ডেটা বোঝার জন্য সম্ভাবনা এবং পরিসংখ্যানের গাণিতিক কৌশল। | [পাঠ](1-Introduction/04-stats-and-probability/README.md) [ভিডিও](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | -| 05 | সম্পর্কিত ডেটার সাথে কাজ করা | [ডেটার সাথে কাজ করা](2-Working-With-Data/README.md) | সম্পর্কিত ডেটার পরিচিতি এবং SQL নামে পরিচিত স্ট্রাকচার্ড কোয়েরি ল্যাঙ্গুয়েজ ব্যবহার করে সম্পর্কিত ডেটা অন্বেষণ এবং বিশ্লেষণের মৌলিক বিষয়। | [পাঠ](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | -| 06 | NoSQL ডেটার সাথে কাজ করা | [ডেটার সাথে কাজ করা](2-Working-With-Data/README.md) | অ-সম্পর্কিত ডেটার পরিচিতি, এর বিভিন্ন প্রকার এবং ডকুমেন্ট ডেটাবেস অন্বেষণ এবং বিশ্লেষণের মৌলিক বিষয়। | [পাঠ](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| -| 07 | পাইথনের সাথে কাজ করা | [ডেটার সাথে কাজ করা](2-Working-With-Data/README.md) | Pandas-এর মতো লাইব্রেরি ব্যবহার করে ডেটা অন্বেষণের জন্য পাইথন ব্যবহার করার মৌলিক বিষয়। পাইথন প্রোগ্রামিংয়ের প্রাথমিক ধারণা সুপারিশ করা হয়। | [পাঠ](2-Working-With-Data/07-python/README.md) [ভিডিও](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | -| 08 | ডেটা প্রস্তুতি | [ডেটার সাথে কাজ করা](2-Working-With-Data/README.md) | ডেটা পরিষ্কার এবং রূপান্তর করার কৌশল নিয়ে আলোচনা, যা অনুপস্থিত, ভুল বা অসম্পূর্ণ ডেটার চ্যালেঞ্জ মোকাবেলা করতে সাহায্য করে। | [পাঠ](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | +| 05 | সম্পর্কযুক্ত ডেটার সাথে কাজ করা | [ডেটার সাথে কাজ করা](2-Working-With-Data/README.md) | সম্পর্কযুক্ত ডেটার পরিচিতি এবং SQL (যা "see-quell" নামে পরিচিত) ব্যবহার করে সম্পর্কযুক্ত ডেটা অন্বেষণ এবং বিশ্লেষণের মৌলিক বিষয়। | [পাঠ](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | +| 06 | NoSQL ডেটার সাথে কাজ করা | [ডেটার সাথে কাজ করা](2-Working-With-Data/README.md) | অ-সম্পর্কযুক্ত ডেটার পরিচিতি, এর বিভিন্ন প্রকার এবং ডকুমেন্ট ডেটাবেস অন্বেষণ এবং বিশ্লেষণের মৌলিক বিষয়। | [পাঠ](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| +| 07 | পাইথনের সাথে কাজ করা | [ডেটার সাথে কাজ করা](2-Working-With-Data/README.md) | Pandas-এর মতো লাইব্রেরি ব্যবহার করে ডেটা অন্বেষণের জন্য পাইথন ব্যবহার করার মৌলিক বিষয়। পাইথন প্রোগ্রামিংয়ের মৌলিক ধারণা সুপারিশ করা হয়। | [পাঠ](2-Working-With-Data/07-python/README.md) [ভিডিও](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | +| 08 | ডেটা প্রস্তুতি | [ডেটার সাথে কাজ করা](2-Working-With-Data/README.md) | অনুপস্থিত, ভুল বা অসম্পূর্ণ ডেটার চ্যালেঞ্জ মোকাবেলার জন্য ডেটা পরিষ্কার এবং রূপান্তর করার কৌশল। | [পাঠ](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | | 09 | পরিমাণের ভিজ্যুয়ালাইজেশন | [ডেটা ভিজ্যুয়ালাইজেশন](3-Data-Visualization/README.md) | Matplotlib ব্যবহার করে পাখির ডেটা 🦆 ভিজ্যুয়ালাইজ করতে শিখুন। | [পাঠ](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) | -| 10 | ডেটার বিতরণ ভিজ্যুয়ালাইজেশন | [ডেটা ভিজ্যুয়ালাইজেশন](3-Data-Visualization/README.md) | একটি অন্তরালের মধ্যে পর্যবেক্ষণ এবং প্রবণতা ভিজ্যুয়ালাইজ করা। | [পাঠ](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) | -| 11 | অনুপাতের ভিজ্যুয়ালাইজেশন | [ডেটা ভিজ্যুয়ালাইজেশন](3-Data-Visualization/README.md) | পৃথক এবং গোষ্ঠীভুক্ত শতাংশ ভিজ্যুয়ালাইজ করা। | [পাঠ](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) | -| 12 | সম্পর্কের ভিজ্যুয়ালাইজেশন | [ডেটা ভিজ্যুয়ালাইজেশন](3-Data-Visualization/README.md) | ডেটার সেট এবং এর ভেরিয়েবলগুলোর মধ্যে সংযোগ এবং সম্পর্ক ভিজ্যুয়ালাইজ করা। | [পাঠ](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) | -| 13 | অর্থপূর্ণ ভিজ্যুয়ালাইজেশন | [ডেটা ভিজ্যুয়ালাইজেশন](3-Data-Visualization/README.md) | আপনার ভিজ্যুয়ালাইজেশনকে কার্যকর সমস্যা সমাধান এবং অন্তর্দৃষ্টির জন্য মূল্যবান করার কৌশল এবং নির্দেশিকা। | [পাঠ](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) | +| 10 | ডেটার বিতরণ ভিজ্যুয়ালাইজ করা | [ডেটা ভিজ্যুয়ালাইজেশন](3-Data-Visualization/README.md) | একটি অন্তরালের মধ্যে পর্যবেক্ষণ এবং প্রবণতা ভিজ্যুয়ালাইজ করা। | [পাঠ](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) | +| 11 | অনুপাত ভিজ্যুয়ালাইজ করা | [ডেটা ভিজ্যুয়ালাইজেশন](3-Data-Visualization/README.md) | পৃথক এবং গোষ্ঠীভুক্ত শতাংশ ভিজ্যুয়ালাইজ করা। | [পাঠ](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) | +| 12 | সম্পর্ক ভিজ্যুয়ালাইজ করা | [ডেটা ভিজ্যুয়ালাইজেশন](3-Data-Visualization/README.md) | ডেটার সেট এবং এর ভেরিয়েবলগুলির মধ্যে সংযোগ এবং সম্পর্ক ভিজ্যুয়ালাইজ করা। | [পাঠ](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) | +| 13 | অর্থপূর্ণ ভিজ্যুয়ালাইজেশন | [ডেটা ভিজ্যুয়ালাইজেশন](3-Data-Visualization/README.md) | কার্যকর সমস্যা সমাধান এবং অন্তর্দৃষ্টির জন্য আপনার ভিজ্যুয়ালাইজেশনকে মূল্যবান করার কৌশল এবং নির্দেশিকা। | [পাঠ](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) | | 14 | ডেটা সায়েন্স লাইফসাইকেলের পরিচিতি | [লাইফসাইকেল](4-Data-Science-Lifecycle/README.md) | ডেটা সায়েন্স লাইফসাইকেলের পরিচিতি এবং ডেটা সংগ্রহ এবং নিষ্কাশনের প্রথম ধাপ। | [পাঠ](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | -| 15 | বিশ্লেষণ | [লাইফসাইকেল](4-Data-Science-Lifecycle/README.md) | ডেটা সায়েন্স লাইফসাইকেলের এই ধাপটি ডেটা বিশ্লেষণের কৌশলগুলোর উপর ফোকাস করে। | [পাঠ](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | | -| 16 | যোগাযোগ | [লাইফসাইকেল](4-Data-Science-Lifecycle/README.md) | ডেটা থেকে অন্তর্দৃষ্টি উপস্থাপন করার উপর ফোকাস করে, যা সিদ্ধান্ত গ্রহণকারীদের জন্য বোঝা সহজ করে। | [পাঠ](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | -| 17 | ক্লাউডে ডেটা সায়েন্স | [ক্লাউড ডেটা](5-Data-Science-In-Cloud/README.md) | ক্লাউডে ডেটা সায়েন্স এবং এর সুবিধাগুলোর পরিচিতি। | [পাঠ](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) এবং [Maud](https://twitter.com/maudstweets) | +| 15 | বিশ্লেষণ | [লাইফসাইকেল](4-Data-Science-Lifecycle/README.md) | ডেটা বিশ্লেষণের কৌশলগুলির উপর ফোকাস করে লাইফসাইকেলের এই ধাপ। | [পাঠ](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | | +| 16 | যোগাযোগ | [লাইফসাইকেল](4-Data-Science-Lifecycle/README.md) | ডেটা থেকে অন্তর্দৃষ্টি উপস্থাপন করার উপর ফোকাস করে যা সিদ্ধান্ত গ্রহণকারীদের জন্য বোঝা সহজ করে। | [পাঠ](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | +| 17 | ক্লাউডে ডেটা সায়েন্স | [ক্লাউড ডেটা](5-Data-Science-In-Cloud/README.md) | ক্লাউডে ডেটা সায়েন্স এবং এর সুবিধাগুলির পরিচিতি। | [পাঠ](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) এবং [Maud](https://twitter.com/maudstweets) | | 18 | ক্লাউডে ডেটা সায়েন্স | [ক্লাউড ডেটা](5-Data-Science-In-Cloud/README.md) | লো কোড টুল ব্যবহার করে মডেল প্রশিক্ষণ। |[পাঠ](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) এবং [Maud](https://twitter.com/maudstweets) | | 19 | ক্লাউডে ডেটা সায়েন্স | [ক্লাউড ডেটা](5-Data-Science-In-Cloud/README.md) | Azure Machine Learning Studio ব্যবহার করে মডেল ডিপ্লয় করা। | [পাঠ](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) এবং [Maud](https://twitter.com/maudstweets) | -| 20 | বাস্তব জীবনে ডেটা সায়েন্স | [বাস্তব জীবনে](6-Data-Science-In-Wild/README.md) | বাস্তব জীবনে ডেটা সায়েন্স চালিত প্রকল্প। | [পাঠ](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) | +| 20 | বাস্তব জীবনে ডেটা সায়েন্স | [বাস্তব জীবনে](6-Data-Science-In-Wild/README.md) | বাস্তব জীবনের প্রকল্পে ডেটা সায়েন্স চালিত উদাহরণ। | [পাঠ](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) | ## GitHub Codespaces -এই নমুনাটি Codespace-এ খুলতে নিম্নলিখিত ধাপগুলো অনুসরণ করুন: +এই নমুনাটি Codespace-এ খুলতে নিম্নলিখিত পদক্ষেপগুলি অনুসরণ করুন: 1. Code ড্রপ-ডাউন মেনুতে ক্লিক করুন এবং Open with Codespaces অপশনটি নির্বাচন করুন। 2. প্যানেলের নিচে + New codespace নির্বাচন করুন। আরও তথ্যের জন্য, [GitHub ডকুমেন্টেশন](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace) দেখুন। ## VSCode Remote - Containers -আপনার স্থানীয় মেশিন এবং VSCode ব্যবহার করে VS Code Remote - Containers এক্সটেনশন ব্যবহার করে এই রিপোজিটরিটি একটি কন্টেইনারে খুলতে নিম্নলিখিত ধাপগুলো অনুসরণ করুন: +আপনার স্থানীয় মেশিন এবং VSCode ব্যবহার করে এই রিপোজিটরিটি একটি কন্টেইনারে খুলতে নিম্নলিখিত পদক্ষেপগুলি অনুসরণ করুন: -1. যদি এটি আপনার প্রথমবার হয় একটি ডেভেলপমেন্ট কন্টেইনার ব্যবহার করা, তাহলে নিশ্চিত করুন যে আপনার সিস্টেম প্রয়োজনীয়তা পূরণ করে (যেমন, Docker ইনস্টল করা আছে) [শুরু করার ডকুমেন্টেশন](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started) এ। +1. যদি এটি আপনার প্রথমবার ডেভেলপমেন্ট কন্টেইনার ব্যবহার হয়, তাহলে নিশ্চিত করুন যে আপনার সিস্টেম প্রয়োজনীয়তা পূরণ করে (যেমন Docker ইনস্টল করা আছে) [শুরু করার ডকুমেন্টেশন](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started) এ। -এই রিপোজিটরিটি ব্যবহার করতে, আপনি হয় একটি আইসোলেটেড Docker ভলিউমে রিপোজিটরিটি খুলতে পারেন: +এই রিপোজিটরিটি ব্যবহার করতে, আপনি হয় রিপোজিটরিটি একটি বিচ্ছিন্ন Docker ভলিউমে খুলতে পারেন: -**নোট**: ভিতরে, এটি Remote-Containers: **Clone Repository in Container Volume...** কমান্ড ব্যবহার করবে যা সোর্স কোডকে একটি Docker ভলিউমে ক্লোন করবে, স্থানীয় ফাইল সিস্টেমের পরিবর্তে। [Volumes](https://docs.docker.com/storage/volumes/) কন্টেইনার ডেটা সংরক্ষণের জন্য পছন্দনীয় পদ্ধতি। +**নোট**: এর ভিতরে, এটি Remote-Containers: **Clone Repository in Container Volume...** কমান্ড ব্যবহার করবে যা স্থানীয় ফাইল সিস্টেমের পরিবর্তে একটি Docker ভলিউমে সোর্স কোড ক্লোন করবে। [Volumes](https://docs.docker.com/storage/volumes/) কন্টেইনার ডেটা সংরক্ষণের জন্য পছন্দনীয় পদ্ধতি। অথবা স্থানীয়ভাবে ক্লোন করা বা ডাউনলোড করা রিপোজিটরিটির একটি সংস্করণ খুলুন: - এই রিপোজিটরিটি আপনার স্থানীয় ফাইল সিস্টেমে ক্লোন করুন। - F1 চাপুন এবং **Remote-Containers: Open Folder in Container...** কমান্ডটি নির্বাচন করুন। -- এই ফোল্ডারের ক্লোন করা কপি নির্বাচন করুন, কন্টেইনারটি শুরু হওয়ার জন্য অপেক্ষা করুন এবং জিনিসগুলো পরীক্ষা করুন। +- এই ফোল্ডারের ক্লোন করা কপি নির্বাচন করুন, কন্টেইনারটি শুরু হওয়ার জন্য অপেক্ষা করুন এবং জিনিসগুলি চেষ্টা করুন। ## অফলাইন অ্যাক্সেস -আপনি [Docsify](https://docsify.js.org/#/) ব্যবহার করে এই ডকুমেন্টেশনটি অফলাইনে চালাতে পারেন। এই রিপোজিটরিটি ফর্ক করুন, [Docsify ইনস্টল করুন](https://docsify.js.org/#/quickstart) আপনার স্থানীয় মেশিনে, তারপর এই রিপোজিটরির মূল ফোল্ডারে `docsify serve` টাইপ করুন। ওয়েবসাইটটি আপনার লোকালহোস্টে পোর্ট ৩০০০-এ পরিবেশন করা হবে: `localhost:3000`। +আপনি [Docsify](https://docsify.js.org/#/) ব্যবহার করে এই ডকুমেন্টেশনটি অফলাইনে চালাতে পারেন। এই রিপোজিটরিটি ফর্ক করুন, [Docsify ইনস্টল করুন](https://docsify.js.org/#/quickstart) আপনার স্থানীয় মেশিনে, তারপর এই রিপোজিটরির মূল ফোল্ডারে `docsify serve` টাইপ করুন। ওয়েবসাইটটি আপনার localhost-এ পোর্ট 3000-এ পরিবেশন করা হবে: `localhost:3000`। -> নোট, নোটবুকগুলো Docsify এর মাধ্যমে রেন্ডার করা হবে না, তাই যখন আপনাকে একটি নোটবুক চালাতে হবে, তখন সেটি আলাদাভাবে VS Code-এ একটি Python কের্নেল চালিয়ে করুন। +> নোট, নোটবুকগুলি Docsify-এর মাধ্যমে রেন্ডার করা হবে না, তাই যখন আপনাকে একটি নোটবুক চালাতে হবে, তখন এটি আলাদাভাবে Python কার্নেল চালিয়ে VS Code-এ করুন। ## অন্যান্য পাঠক্রম -আমাদের দল অন্যান্য পাঠক্রমও তৈরি করে! দেখুন: +আমাদের দল অন্যান্য পাঠক্রম তৈরি করে! দেখুন: - [Edge AI for Beginners](https://aka.ms/edgeai-for-beginners) - [AI Agents for Beginners](https://aka.ms/ai-agents-beginners) @@ -197,20 +197,20 @@ CO_OP_TRANSLATOR_METADATA: - [Bash for Beginners](https://github.com/microsoft/bash-for-beginners) - [ML for Beginners](https://aka.ms/ml-beginners) - [Cybersecurity for Beginners](https://github.com/microsoft/Security-101) -- [Web Dev for Beginners](https://aka.ms/webdev-beginners) -- [IoT for Beginners](https://aka.ms/iot-beginners) -- [Machine Learning for Beginners](https://aka.ms/ml-beginners) -- [XR Development for Beginners](https://aka.ms/xr-dev-for-beginners) -- [GitHub Copilot ব্যবহার করে AI পেয়ারড প্রোগ্রামিংয়ে দক্ষতা অর্জন](https://aka.ms/GitHubCopilotAI) -- [শুরু করার জন্য XR ডেভেলপমেন্ট](https://github.com/microsoft/xr-development-for-beginners) -- [C#/.NET ডেভেলপারদের জন্য GitHub Copilot ব্যবহার করে দক্ষতা অর্জন](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) -- [আপনার নিজস্ব Copilot অ্যাডভেঞ্চার নির্বাচন করুন](https://github.com/microsoft/CopilotAdventures) +- [ওয়েব ডেভেলপমেন্ট শেখার জন্য](https://aka.ms/webdev-beginners) +- [আইওটি শেখার জন্য](https://aka.ms/iot-beginners) +- [মেশিন লার্নিং শেখার জন্য](https://aka.ms/ml-beginners) +- [এক্সআর ডেভেলপমেন্ট শেখার জন্য](https://aka.ms/xr-dev-for-beginners) +- [গিটহাব কপাইলট ব্যবহার করে এআই পেয়ারড প্রোগ্রামিং আয়ত্ত করা](https://aka.ms/GitHubCopilotAI) +- [এক্সআর ডেভেলপমেন্ট শেখার জন্য](https://github.com/microsoft/xr-development-for-beginners) +- [গিটহাব কপাইলট আয়ত্ত করা সি#/.NET ডেভেলপারদের জন্য](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) +- [আপনার নিজস্ব কপাইলট অ্যাডভেঞ্চার নির্বাচন করুন](https://github.com/microsoft/CopilotAdventures) ## সাহায্য পাওয়া **সমস্যার সম্মুখীন হচ্ছেন?** সাধারণ সমস্যার সমাধানের জন্য আমাদের [সমস্যা সমাধানের গাইড](TROUBLESHOOTING.md) দেখুন। -যদি আপনি আটকে যান বা AI অ্যাপ তৈরি সম্পর্কে কোনো প্রশ্ন থাকে, যোগ দিন: +যদি আপনি আটকে যান বা এআই অ্যাপ তৈরি করার বিষয়ে কোনো প্রশ্ন থাকে, যোগ দিন: [![Azure AI Foundry Discord](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) @@ -221,4 +221,4 @@ CO_OP_TRANSLATOR_METADATA: --- **অস্বীকৃতি**: -এই নথিটি AI অনুবাদ পরিষেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনুবাদ করা হয়েছে। আমরা যথাসাধ্য সঠিকতার জন্য চেষ্টা করি, তবে অনুগ্রহ করে মনে রাখবেন যে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা অসঙ্গতি থাকতে পারে। মূল ভাষায় থাকা নথিটিকে প্রামাণিক উৎস হিসেবে বিবেচনা করা উচিত। গুরুত্বপূর্ণ তথ্যের জন্য, পেশাদার মানব অনুবাদ সুপারিশ করা হয়। এই অনুবাদ ব্যবহারের ফলে কোনো ভুল বোঝাবুঝি বা ভুল ব্যাখ্যা হলে আমরা দায়বদ্ধ থাকব না। \ No newline at end of file +এই নথিটি AI অনুবাদ পরিষেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনুবাদ করা হয়েছে। আমরা যথাসাধ্য সঠিক অনুবাদের চেষ্টা করি, তবে দয়া করে মনে রাখবেন যে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা অসঙ্গতি থাকতে পারে। নথিটির মূল ভাষায় থাকা সংস্করণটিকে প্রামাণিক উৎস হিসেবে বিবেচনা করা উচিত। গুরুত্বপূর্ণ তথ্যের জন্য, পেশাদার মানব অনুবাদ সুপারিশ করা হয়। এই অনুবাদ ব্যবহারের ফলে সৃষ্ট কোনো ভুল বোঝাবুঝি বা ভুল ব্যাখ্যার জন্য আমরা দায়ী নই। \ No newline at end of file diff --git a/translations/br/README.md b/translations/br/README.md index 2537c60e..cbe24ed1 100644 --- a/translations/br/README.md +++ b/translations/br/README.md @@ -1,8 +1,8 @@ # Data Science pro začátečníky - Kurikulum -[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) +[![Otevřít v GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) -[![GitHub license](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE) -[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/) -[![GitHub issues](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/) +[![GitHub licence](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE) +[![GitHub přispěvatelé](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/) +[![GitHub problémy](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/) [![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) -[![GitHub watchers](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 forks](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 stars](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/) +[![GitHub sledující](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 forky](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 hvězdy](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/) [![](https://dcbadge.vercel.app/api/server/ByRwuEEgH4)](https://discord.gg/zxKYvhSnVp?WT.mc_id=academic-000002-leestott) [![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) -Azure Cloud Advocates ve společnosti Microsoft s potěšením nabízejí 10týdenní kurikulum s 20 lekcemi zaměřenými na Data Science. Každá lekce obsahuje kvízy před a po lekci, písemné pokyny k dokončení lekce, řešení a úkol. Náš přístup založený na projektech vám umožní učit se při tvorbě, což je osvědčený způsob, jak si nové dovednosti lépe osvojit. +Azure Cloud Advocates ve společnosti Microsoft s potěšením nabízejí 10týdenní kurikulum s 20 lekcemi zaměřenými na datovou vědu. Každá lekce obsahuje kvízy před a po lekci, písemné pokyny k dokončení lekce, řešení a úkol. Náš přístup založený na projektech vám umožní učit se při tvorbě, což je osvědčený způsob, jak si nové dovednosti lépe osvojit. **Velké díky našim autorům:** [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). **🙏 Speciální poděkování 🙏 našim [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) autorům, recenzentům a přispěvatelům obsahu,** zejména 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/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.cs.png)| +|![Sketchnote od @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.cs.png)| |:---:| | Data Science pro začátečníky - _Sketchnote od [@nitya](https://twitter.com/nitya)_ | ### 🌐 Podpora více jazyků -#### Podporováno prostřednictvím GitHub Action (automaticky a vždy aktuální) +#### Podporováno prostřednictvím GitHub Action (Automatizované & vždy aktuální) -[Francouzština](../fr/README.md) | [Španělština](../es/README.md) | [Němčina](../de/README.md) | [Ruština](../ru/README.md) | [Arabština](../ar/README.md) | [Perština (Farsi)](../fa/README.md) | [Urdu](../ur/README.md) | [Čínština (zjednodušená)](../zh/README.md) | [Čínština (tradiční, Macao)](../mo/README.md) | [Čínština (tradiční, Hongkong)](../hk/README.md) | [Čínština (tradiční, Tchaj-wan)](../tw/README.md) | [Japonština](../ja/README.md) | [Korejština](../ko/README.md) | [Hindština](../hi/README.md) | [Bengálština](../bn/README.md) | [Maráthština](../mr/README.md) | [Nepálština](../ne/README.md) | [Paňdžábština (Gurmukhi)](../pa/README.md) | [Portugalština (Portugalsko)](../pt/README.md) | [Portugalština (Brazílie)](../br/README.md) | [Italština](../it/README.md) | [Polština](../pl/README.md) | [Turečtina](../tr/README.md) | [Řečtina](../el/README.md) | [Thajština](../th/README.md) | [Švédština](../sv/README.md) | [Dánština](../da/README.md) | [Norština](../no/README.md) | [Finština](../fi/README.md) | [Nizozemština](../nl/README.md) | [Hebrejština](../he/README.md) | [Vietnamština](../vi/README.md) | [Indonéština](../id/README.md) | [Malajština](../ms/README.md) | [Tagalog (Filipíny)](../tl/README.md) | [Svahilština](../sw/README.md) | [Maďarština](../hu/README.md) | [Čeština](./README.md) | [Slovenština](../sk/README.md) | [Rumunština](../ro/README.md) | [Bulharština](../bg/README.md) | [Srbština (cyrilice)](../sr/README.md) | [Chorvatština](../hr/README.md) | [Slovinština](../sl/README.md) | [Ukrajinština](../uk/README.md) | [Barmština (Myanmar)](../my/README.md) + +[Arabština](../ar/README.md) | [Bengálština](../bn/README.md) | [Bulharština](../bg/README.md) | [Barmština (Myanmar)](../my/README.md) | [Čínština (zjednodušená)](../zh/README.md) | [Čínština (tradiční, Hongkong)](../hk/README.md) | [Čínština (tradiční, Macao)](../mo/README.md) | [Čínština (tradiční, Tchaj-wan)](../tw/README.md) | [Chorvatština](../hr/README.md) | [Čeština](./README.md) | [Dánština](../da/README.md) | [Nizozemština](../nl/README.md) | [Estonština](../et/README.md) | [Finština](../fi/README.md) | [Francouzština](../fr/README.md) | [Němčina](../de/README.md) | [Řečtina](../el/README.md) | [Hebrejština](../he/README.md) | [Hindština](../hi/README.md) | [Maďarština](../hu/README.md) | [Indonéština](../id/README.md) | [Italština](../it/README.md) | [Japonština](../ja/README.md) | [Korejština](../ko/README.md) | [Litevština](../lt/README.md) | [Malajština](../ms/README.md) | [Maráthština](../mr/README.md) | [Nepálština](../ne/README.md) | [Norština](../no/README.md) | [Perština (Farsi)](../fa/README.md) | [Polština](../pl/README.md) | [Portugalština (Brazílie)](../br/README.md) | [Portugalština (Portugalsko)](../pt/README.md) | [Panjábština (Gurmukhi)](../pa/README.md) | [Rumunština](../ro/README.md) | [Ruština](../ru/README.md) | [Srbština (cyrilice)](../sr/README.md) | [Slovenština](../sk/README.md) | [Slovinština](../sl/README.md) | [Španělština](../es/README.md) | [Svahilština](../sw/README.md) | [Švédština](../sv/README.md) | [Tagalog (Filipínština)](../tl/README.md) | [Tamilština](../ta/README.md) | [Thajština](../th/README.md) | [Turečtina](../tr/README.md) | [Ukrajinština](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamština](../vi/README.md) + **Pokud si přejete přidat další překlady, seznam podporovaných jazyků najdete [zde](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** #### Připojte se k naší komunitě [![Azure AI Discord](https://dcbadge.limes.pink/api/server/kzRShWzttr)](https://aka.ms/ds4beginners/discord) -Máme probíhající sérii Learn with AI na Discordu, dozvíte se více a připojte se k nám na [Learn with AI Series](https://aka.ms/learnwithai/discord) od 18. do 30. září 2025. Získáte tipy a triky, jak používat GitHub Copilot pro Data Science. +Máme probíhající sérii Learn with AI na Discordu, dozvíte se více a připojte se k nám na [Learn with AI Series](https://aka.ms/learnwithai/discord) od 18. do 30. září 2025. Získáte tipy a triky, jak používat GitHub Copilot pro datovou vědu. ![Learn with AI series](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.cs.jpg) @@ -69,8 +71,8 @@ Začněte s následujícími zdroji: - **[Pro učitele](for-teachers.md)** - Pokyny pro výuku a zdroje pro třídy ## 👨‍🎓 Pro studenty -> **Úplní začátečníci**: Noví v oblasti data science? Začněte s našimi [příklady pro začátečníky](examples/README.md)! Tyto jednoduché, dobře komentované příklady vám pomohou pochopit základy, než se pustíte do celého kurikula. -> **[Studenti](https://aka.ms/student-page)**: chcete-li toto kurikulum použít samostatně, vytvořte si vlastní kopii celého repozitáře a dokončete cvičení sami, počínaje kvízem před lekcí. Poté si přečtěte lekci a dokončete zbytek aktivit. Snažte se vytvářet projekty pochopením lekcí, místo abyste kopírovali řešení kódu; tento kód je však dostupný ve složkách /solutions v každé lekci zaměřené na projekt. Dalším nápadem by bylo vytvořit studijní skupinu s přáteli a projít obsah společně. Pro další studium doporučujeme [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). +> **Úplní začátečníci**: Noví v datové vědě? Začněte s našimi [příklady pro začátečníky](examples/README.md)! Tyto jednoduché, dobře komentované příklady vám pomohou pochopit základy před tím, než se pustíte do celého kurikula. +> **[Studenti](https://aka.ms/student-page)**: chcete-li toto kurikulum použít samostatně, vytvořte si vlastní kopii celého repozitáře a dokončete cvičení sami, začněte kvízem před lekcí. Poté si přečtěte lekci a dokončete zbytek aktivit. Snažte se vytvářet projekty pochopením lekcí, místo abyste kopírovali řešení kódu; tento kód je však dostupný ve složkách /solutions v každé lekci zaměřené na projekt. Dalším nápadem by bylo vytvořit studijní skupinu s přáteli a projít obsah společně. Pro další studium doporučujeme [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). **Rychlý start:** 1. Podívejte se na [Průvodce instalací](INSTALLATION.md) pro nastavení prostředí @@ -80,7 +82,7 @@ Začněte s následujícími zdroji: ## 👩‍🏫 Pro učitele -> **Učitelé**: [zahrnuli jsme několik návrhů](for-teachers.md), jak toto kurikulum využít. Budeme rádi za vaši zpětnou vazbu [v našem diskusním fóru](https://github.com/microsoft/Data-Science-For-Beginners/discussions)! +> **Učitelé**: [zahrnuli jsme několik návrhů](for-teachers.md), jak používat toto kurikulum. Budeme rádi za vaši zpětnou vazbu [v našem diskusním fóru](https://github.com/microsoft/Data-Science-For-Beginners/discussions)! ## Seznamte se s týmem @@ -91,11 +93,11 @@ Začněte s následujícími zdroji: > 🎥 Klikněte na obrázek výše pro video o projektu a lidech, kteří ho vytvořili! ## Pedagogika +Při vytváření tohoto kurikula jsme se rozhodli pro dvě pedagogické zásady: zajistit, aby bylo založené na projektech, a zahrnout časté kvízy. Na konci této série se studenti naučí základní principy datové vědy, včetně etických konceptů, přípravy dat, různých způsobů práce s daty, vizualizace dat, analýzy dat, reálných příkladů využití datové vědy a další. -Při tvorbě tohoto kurikula jsme se rozhodli pro dva pedagogické principy: zajistit, aby bylo založeno na projektech, a zahrnout časté kvízy. Na konci této série se studenti naučí základní principy data science, včetně etických konceptů, přípravy dat, různých způsobů práce s daty, vizualizace dat, analýzy dat, reálných případů využití data science a dalších. -Navíc nízkostresový kvíz před hodinou nastaví záměr studenta na učení daného tématu, zatímco druhý kvíz po hodině zajistí lepší zapamatování. Tento učební plán byl navržen tak, aby byl flexibilní a zábavný, a lze jej absolvovat celý nebo jen jeho část. Projekty začínají jednoduše a postupně se stávají složitějšími na konci 10týdenního cyklu. +Navíc nízkostresový kvíz před hodinou nastaví záměr studenta na učení daného tématu, zatímco druhý kvíz po hodině zajistí lepší zapamatování. Toto kurikulum bylo navrženo tak, aby bylo flexibilní a zábavné, a lze jej absolvovat celé nebo jen jeho část. Projekty začínají jednoduše a postupně se stávají složitějšími během 10týdenního cyklu. -> Najděte naše [Pravidla chování](CODE_OF_CONDUCT.md), [Pokyny pro přispívání](CONTRIBUTING.md), [Pokyny pro překlad](TRANSLATIONS.md). Uvítáme vaši konstruktivní zpětnou vazbu! +> Najděte naše [Pravidla chování](CODE_OF_CONDUCT.md), [Pokyny pro přispívání](CONTRIBUTING.md), [Pokyny pro překlad](TRANSLATIONS.md). Uvítáme vaše konstruktivní zpětné vazby! ## Každá lekce obsahuje: @@ -104,10 +106,10 @@ Navíc nízkostresový kvíz před hodinou nastaví záměr studenta na učení - Zahřívací kvíz před lekcí - Písemnou lekci - U lekcí založených na projektech podrobné návody, jak projekt vytvořit -- Kontroly znalostí +- Kontrolní otázky - Výzvu - Doplňkové čtení -- Úkol +- Zadání - [Kvíz po lekci](https://ff-quizzes.netlify.app/en/) > **Poznámka ke kvízům**: Všechny kvízy jsou obsaženy ve složce Quiz-App, celkem 40 kvízů po třech otázkách. Jsou propojeny přímo z lekcí, ale aplikaci kvízů lze spustit lokálně nebo nasadit na Azure; postupujte podle pokynů ve složce `quiz-app`. Postupně jsou lokalizovány. @@ -117,7 +119,7 @@ Navíc nízkostresový kvíz před hodinou nastaví záměr studenta na učení **Noví v datové vědě?** Vytvořili jsme speciální [adresář příkladů](examples/README.md) s jednoduchým, dobře okomentovaným kódem, který vám pomůže začít: - 🌟 **Hello World** - Váš první program v datové vědě -- 📂 **Načítání dat** - Naučte se číst a zkoumat datové sady +- 📂 **Načítání dat** - Naučte se číst a prozkoumávat datové sady - 📊 **Jednoduchá analýza** - Vypočítejte statistiky a najděte vzory - 📈 **Základní vizualizace** - Vytvářejte grafy a diagramy - 🔬 **Projekt z reálného světa** - Kompletní pracovní postup od začátku do konce @@ -128,19 +130,19 @@ Každý příklad obsahuje podrobné komentáře vysvětlující každý krok, c ## Lekce -|![ Sketchnote od @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.cs.png)| +|![ Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.cs.png)| |:---:| | Data Science For Beginners: Roadmap - _Sketchnote od [@nitya](https://twitter.com/nitya)_ | -| Číslo lekce | Téma | Skupina lekcí | Cíle učení | Propojená lekce | Autor | +| Číslo lekce | Téma | Skupina lekcí | Cíle učení | Odkaz na lekci | Autor | | :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: | | 01 | Definování datové vědy | [Úvod](1-Introduction/README.md) | Naučte se základní koncepty datové vědy a jak souvisí s umělou inteligencí, strojovým učením a velkými daty. | [lekce](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | | 02 | Etika datové vědy | [Úvod](1-Introduction/README.md) | Koncepty etiky dat, výzvy a rámce. | [lekce](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | | 03 | Definování dat | [Úvod](1-Introduction/README.md) | Jak jsou data klasifikována a jejich běžné zdroje. | [lekce](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) | | 04 | Úvod do statistiky a pravděpodobnosti | [Úvod](1-Introduction/README.md) | Matematické techniky pravděpodobnosti a statistiky pro pochopení dat. | [lekce](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | -| 05 | Práce s relačními daty | [Práce s daty](2-Working-With-Data/README.md) | Úvod do relačních dat a základy zkoumání a analýzy relačních dat pomocí Structured Query Language, známého také jako SQL (vyslovováno „sí-kvel“). | [lekce](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | +| 05 | Práce s relačními daty | [Práce s daty](2-Working-With-Data/README.md) | Úvod do relačních dat a základy zkoumání a analýzy relačních dat pomocí Structured Query Language, známého jako SQL (vyslovováno „si-kvel“). | [lekce](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | | 06 | Práce s NoSQL daty | [Práce s daty](2-Working-With-Data/README.md) | Úvod do nerelačních dat, jejich různých typů a základy zkoumání a analýzy dokumentových databází. | [lekce](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| -| 07 | Práce s Pythonem | [Práce s daty](2-Working-With-Data/README.md) | Základy používání Pythonu pro zkoumání dat s knihovnami jako Pandas. Doporučuje se základní znalost programování v Pythonu. | [lekce](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | +| 07 | Práce s Pythonem | [Práce s daty](2-Working-With-Data/README.md) | Základy používání Pythonu pro zkoumání dat s knihovnami, jako je Pandas. Doporučuje se základní znalost programování v Pythonu. | [lekce](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | | 08 | Příprava dat | [Práce s daty](2-Working-With-Data/README.md) | Témata o technikách čištění a transformace dat pro řešení problémů s chybějícími, nepřesnými nebo neúplnými daty. | [lekce](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | | 09 | Vizualizace množství | [Vizualizace dat](3-Data-Visualization/README.md) | Naučte se používat Matplotlib k vizualizaci dat o ptácích 🦆 | [lekce](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) | | 10 | Vizualizace rozložení dat | [Vizualizace dat](3-Data-Visualization/README.md) | Vizualizace pozorování a trendů v rámci intervalu. | [lekce](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) | @@ -151,7 +153,7 @@ Každý příklad obsahuje podrobné komentáře vysvětlující každý krok, c | 15 | Analýza | [Životní cyklus](4-Data-Science-Lifecycle/README.md) | Tato fáze životního cyklu datové vědy se zaměřuje na techniky analýzy dat. | [lekce](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | | | 16 | Komunikace | [Životní cyklus](4-Data-Science-Lifecycle/README.md) | Tato fáze životního cyklu datové vědy se zaměřuje na prezentaci poznatků z dat způsobem, který usnadňuje jejich pochopení pro rozhodovací orgány. | [lekce](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | | 17 | Datová věda v cloudu | [Cloudová data](5-Data-Science-In-Cloud/README.md) | Tato série lekcí představuje datovou vědu v cloudu a její výhody. | [lekce](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) a [Maud](https://twitter.com/maudstweets) | -| 18 | Datová věda v cloudu | [Cloudová data](5-Data-Science-In-Cloud/README.md) | Trénování modelů pomocí nástrojů Low Code. |[lekce](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) a [Maud](https://twitter.com/maudstweets) | +| 18 | Datová věda v cloudu | [Cloudová data](5-Data-Science-In-Cloud/README.md) | Trénování modelů pomocí Low Code nástrojů. |[lekce](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) a [Maud](https://twitter.com/maudstweets) | | 19 | Datová věda v cloudu | [Cloudová data](5-Data-Science-In-Cloud/README.md) | Nasazení modelů pomocí Azure Machine Learning Studio. | [lekce](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) a [Maud](https://twitter.com/maudstweets) | | 20 | Datová věda v reálném světě | [V reálném světě](6-Data-Science-In-Wild/README.md) | Projekty řízené datovou vědou v reálném světě. | [lekce](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) | @@ -181,11 +183,11 @@ Nebo otevřete lokálně klonovanou nebo staženou verzi repozitáře: Tuto dokumentaci můžete spustit offline pomocí [Docsify](https://docsify.js.org/#/). Forkněte tento repozitář, [nainstalujte Docsify](https://docsify.js.org/#/quickstart) na svůj lokální počítač, poté v kořenové složce tohoto repozitáře zadejte `docsify serve`. Webová stránka bude spuštěna na portu 3000 na vašem localhostu: `localhost:3000`. -> Poznámka, notebooky nebudou renderovány přes Docsify, takže když potřebujete spustit notebook, udělejte to samostatně ve VS Code s běžícím Python jádrem. +> Poznámka, notebooky nebudou renderovány přes Docsify, takže pokud potřebujete spustit notebook, udělejte to samostatně ve VS Code s běžícím Python jádrem. -## Další učební plány +## Další kurikula -Náš tým vytváří další učební plány! Podívejte se na: +Náš tým vytváří další kurikula! Podívejte se na: - [Edge AI pro začátečníky](https://aka.ms/edgeai-for-beginners) - [AI agenti pro začátečníky](https://aka.ms/ai-agents-beginners) @@ -198,28 +200,28 @@ Náš tým vytváří další učební plány! Podívejte se na: - [Bash pro začátečníky](https://github.com/microsoft/bash-for-beginners) - [ML pro začátečníky](https://aka.ms/ml-beginners) - [Kybernetická bezpečnost pro začátečníky](https://github.com/microsoft/Security-101) -- [Webový vývoj pro začátečníky](https://aka.ms/webdev-beginners) -- [IoT pro začátečníky](https://aka.ms/iot-beginners) -- [Strojové učení pro začátečníky](https://aka.ms/ml-beginners) -- [XR vývoj pro začátečníky](https://aka.ms/xr-dev-for-beginners) -- [Ovládnutí GitHub Copilot pro párové programování s AI](https://aka.ms/GitHubCopilotAI) -- [Vývoj XR pro začátečníky](https://github.com/microsoft/xr-development-for-beginners) -- [Ovládnutí GitHub Copilot pro vývojáře C#/.NET](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) -- [Vyberte si vlastní dobrodružství s Copilotem](https://github.com/microsoft/CopilotAdventures) +- [Web Dev pro začátečníky](https://aka.ms/webdev-beginners) +- [IoT pro začátečníky](https://aka.ms/iot-beginners) +- [Strojové učení pro začátečníky](https://aka.ms/ml-beginners) +- [Vývoj XR pro začátečníky](https://aka.ms/xr-dev-for-beginners) +- [Ovládnutí GitHub Copilot pro párové programování s AI](https://aka.ms/GitHubCopilotAI) +- [Vývoj XR pro začátečníky](https://github.com/microsoft/xr-development-for-beginners) +- [Ovládnutí GitHub Copilot pro vývojáře C#/.NET](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) +- [Vyberte si vlastní dobrodružství s Copilotem](https://github.com/microsoft/CopilotAdventures) -## Získání pomoci +## Získání pomoci -**Narazili jste na problémy?** Podívejte se na náš [Průvodce řešením problémů](TROUBLESHOOTING.md) pro řešení běžných potíží. +**Narazili jste na problémy?** Podívejte se na náš [Průvodce řešením problémů](TROUBLESHOOTING.md) pro řešení běžných potíží. -Pokud se zaseknete nebo máte otázky ohledně tvorby AI aplikací, připojte se: +Pokud se zaseknete nebo máte otázky ohledně tvorby AI aplikací, připojte se: -[![Azure AI Foundry Discord](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) +[![Azure AI Foundry Discord](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) -Pokud máte zpětnou vazbu k produktu nebo narazíte na chyby při tvorbě, navštivte: +Pokud máte zpětnou vazbu k produktu nebo narazíte na chyby při tvorbě, navštivte: -[![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- **Prohlášení**: -Tento dokument byl přeložen pomocí služby AI pro překlad [Co-op Translator](https://github.com/Azure/co-op-translator). I když se snažíme o přesnost, mějte prosím na paměti, že automatizované překlady mohou obsahovat chyby nebo nepřesnosti. Původní dokument v jeho původním jazyce by měl být považován za autoritativní zdroj. Pro důležité informace doporučujeme profesionální lidský překlad. Neodpovídáme za žádná nedorozumění nebo nesprávné interpretace vyplývající z použití tohoto překladu. \ No newline at end of file +Tento dokument byl přeložen pomocí služby AI pro překlady [Co-op Translator](https://github.com/Azure/co-op-translator). I když se snažíme o přesnost, mějte prosím na paměti, že automatizované překlady mohou obsahovat chyby nebo nepřesnosti. Původní dokument v jeho původním jazyce by měl být považován za autoritativní zdroj. Pro důležité informace doporučujeme profesionální lidský překlad. Neodpovídáme za žádná nedorozumění nebo nesprávné interpretace vyplývající z použití tohoto překladu. \ No newline at end of file diff --git a/translations/da/README.md b/translations/da/README.md index cafda0d9..3497b9f6 100644 --- a/translations/da/README.md +++ b/translations/da/README.md @@ -1,53 +1,53 @@ -# Data Science for Begyndere - Et Curriculum +# Data Science for Begyndere - Et Læseplan -[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) +[![Åbn i GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) -[![GitHub license](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE) -[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/) -[![GitHub issues](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/) +[![GitHub licens](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE) +[![GitHub bidragydere](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/) +[![GitHub problemer](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/) [![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/) -[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) +[![PRs Velkommen](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) -[![GitHub watchers](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 følgere](https://img.shields.io/github/watchers/microsoft/Data-Science-For-Beginners.svg?style=social&label=Følg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/watchers/) [![GitHub forks](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 stars](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/) +[![GitHub stjerner](https://img.shields.io/github/stars/microsoft/Data-Science-For-Beginners.svg?style=social&label=Stjerne)](https://GitHub.com/microsoft/Data-Science-For-Beginners/stargazers/) [![](https://dcbadge.vercel.app/api/server/ByRwuEEgH4)](https://discord.gg/zxKYvhSnVp?WT.mc_id=academic-000002-leestott) [![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_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 et 10-ugers, 20-lektioners curriculum om Data Science. Hver lektion inkluderer quizzer før og efter lektionen, skriftlige instruktioner til at gennemføre lektionen, en løsning og en opgave. Vores projektbaserede tilgang giver dig mulighed for at lære, mens du bygger, en dokumenteret metode til at få nye færdigheder til at hænge ved. +Azure Cloud Advocates hos Microsoft er glade for at kunne tilbyde en 10-ugers, 20-lektioners læseplan 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 dokumenteret metode til at få nye færdigheder til at hænge fast. -**Stor 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). +**En stor 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). -**🙏 Særlig 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), +**🙏 En særlig 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), [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/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.da.png)| +|![Sketchnote af @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.da.png)| |:---:| | Data Science For Beginners - _Sketchnote af [@nitya](https://twitter.com/nitya)_ | -### 🌐 Multisproget Support +### 🌐 Flersproget support #### Understøttet via GitHub Action (Automatisk & Altid Opdateret) -[Fransk](../fr/README.md) | [Spansk](../es/README.md) | [Tysk](../de/README.md) | [Russisk](../ru/README.md) | [Arabisk](../ar/README.md) | [Persisk (Farsi)](../fa/README.md) | [Urdu](../ur/README.md) | [Kinesisk (Forenklet)](../zh/README.md) | [Kinesisk (Traditionelt, Macau)](../mo/README.md) | [Kinesisk (Traditionelt, Hong Kong)](../hk/README.md) | [Kinesisk (Traditionelt, Taiwan)](../tw/README.md) | [Japansk](../ja/README.md) | [Koreansk](../ko/README.md) | [Hindi](../hi/README.md) | [Bengali](../bn/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Portugisisk (Portugal)](../pt/README.md) | [Portugisisk (Brasilien)](../br/README.md) | [Italiensk](../it/README.md) | [Polsk](../pl/README.md) | [Tyrkisk](../tr/README.md) | [Græsk](../el/README.md) | [Thai](../th/README.md) | [Svensk](../sv/README.md) | [Dansk](./README.md) | [Norsk](../no/README.md) | [Finsk](../fi/README.md) | [Hollandsk](../nl/README.md) | [Hebraisk](../he/README.md) | [Vietnamesisk](../vi/README.md) | [Indonesisk](../id/README.md) | [Malay](../ms/README.md) | [Tagalog (Filippinsk)](../tl/README.md) | [Swahili](../sw/README.md) | [Ungarsk](../hu/README.md) | [Tjekkisk](../cs/README.md) | [Slovakisk](../sk/README.md) | [Rumænsk](../ro/README.md) | [Bulgarsk](../bg/README.md) | [Serbisk (Kyrillisk)](../sr/README.md) | [Kroatisk](../hr/README.md) | [Slovensk](../sl/README.md) | [Ukrainsk](../uk/README.md) | [Burmesisk (Myanmar)](../my/README.md) +[Arabisk](../ar/README.md) | [Bengalsk](../bn/README.md) | [Bulgarsk](../bg/README.md) | [Burmesisk (Myanmar)](../my/README.md) | [Kinesisk (Forenklet)](../zh/README.md) | [Kinesisk (Traditionel, Hong Kong)](../hk/README.md) | [Kinesisk (Traditionel, Macau)](../mo/README.md) | [Kinesisk (Traditionel, Taiwan)](../tw/README.md) | [Kroatisk](../hr/README.md) | [Tjekkisk](../cs/README.md) | [Dansk](./README.md) | [Hollandsk](../nl/README.md) | [Estisk](../et/README.md) | [Finsk](../fi/README.md) | [Fransk](../fr/README.md) | [Tysk](../de/README.md) | [Græsk](../el/README.md) | [Hebraisk](../he/README.md) | [Hindi](../hi/README.md) | [Ungarsk](../hu/README.md) | [Indonesisk](../id/README.md) | [Italiensk](../it/README.md) | [Japansk](../ja/README.md) | [Koreansk](../ko/README.md) | [Litauisk](../lt/README.md) | [Malayisk](../ms/README.md) | [Marathi](../mr/README.md) | [Nepalesisk](../ne/README.md) | [Norsk](../no/README.md) | [Persisk (Farsi)](../fa/README.md) | [Polsk](../pl/README.md) | [Portugisisk (Brasilien)](../br/README.md) | [Portugisisk (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Rumænsk](../ro/README.md) | [Russisk](../ru/README.md) | [Serbisk (Kyrillisk)](../sr/README.md) | [Slovakisk](../sk/README.md) | [Slovensk](../sl/README.md) | [Spansk](../es/README.md) | [Swahili](../sw/README.md) | [Svensk](../sv/README.md) | [Tagalog (Filippinsk)](../tl/README.md) | [Tamil](../ta/README.md) | [Thai](../th/README.md) | [Tyrkisk](../tr/README.md) | [Ukrainsk](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamesisk](../vi/README.md) -**Hvis du ønsker yderligere oversættelser, er understøttede sprog listet [her](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Hvis du ønsker at få yderligere oversættelser, er de understøttede sprog opført [her](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** -#### Bliv en del af vores fællesskab +#### Deltag i vores fællesskab [![Azure AI Discord](https://dcbadge.limes.pink/api/server/kzRShWzttr)](https://aka.ms/ds4beginners/discord) -Vi har en Discord-serie om læring med AI i gang. Lær mere og deltag i [Learn with AI Series](https://aka.ms/learnwithai/discord) fra 18. - 30. september 2025. Du vil få tips og tricks til at bruge GitHub Copilot til Data Science. +Vi har en igangværende Discord-læringsserie med AI, lær mere og deltag i [Learn with AI Series](https://aka.ms/learnwithai/discord) fra 18. - 30. september 2025. Du vil få tips og tricks til at bruge GitHub Copilot til Data Science. ![Learn with AI series](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.da.jpg) @@ -55,32 +55,32 @@ Vi har en Discord-serie om læring med AI i gang. Lær mere og deltag i [Learn w 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 ressourcer for begyndere, studentpakker og endda måder at få en gratis certifikatvoucher. Dette er en side, du bør 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 en del af et globalt fællesskab af studentambassadører; dette kunne være din vej ind i Microsoft. +- [Student Hub side](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) På denne side finder du ressourcer for begyndere, studenterpakker og endda måder at få en gratis certifikatvoucher. Dette er en side, du bør 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) Deltag i et globalt fællesskab af studentambassadører, dette kunne være din vej ind i Microsoft. -# Kom i gang +# Kom godt i gang ## 📚 Dokumentation -- **[Installationsguide](INSTALLATION.md)** - Trin-for-trin opsætningsinstruktioner for begyndere -- **[Brugervejledning](USAGE.md)** - Eksempler og almindelige arbejdsgange +- **[Installationsvejledning](INSTALLATION.md)** - Trin-for-trin opsætningsinstruktioner for begyndere +- **[Brugsvejledning](USAGE.md)** - Eksempler og almindelige arbejdsgange - **[Fejlfinding](TROUBLESHOOTING.md)** - Løsninger på almindelige problemer - **[Bidragsvejledning](CONTRIBUTING.md)** - Sådan bidrager du til dette projekt -- **[For lærere](for-teachers.md)** - Vejledning til undervisning og klasselokaleressourcer +- **[For Lærere](for-teachers.md)** - Vejledning og ressourcer til undervisning ## 👨‍🎓 For Studerende -> **Helt nye**: Ny inden for 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 dykker ned i det fulde curriculum. -> **[Studerende](https://aka.ms/student-page)**: For at bruge dette curriculum på egen hånd, fork hele repoen og gennemfør øvelserne selv, startende med en quiz før lektionen. Læs derefter lektionen og fuldfør resten af aktiviteterne. Prøv at skabe projekterne ved at forstå lektionerne i stedet for at kopiere løsningskoden; dog er denne kode tilgængelig i /solutions-mapperne i hver projektorienteret lektion. En anden idé kunne være at danne en studiegruppe med venner og gennemgå indholdet sammen. For yderligere studier anbefaler vi [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). +> **Helt nye begyndere**: Ny inden for data science? Start med vores [begynder-venlige eksempler](examples/README.md)! Disse enkle, velkommenterede eksempler vil hjælpe dig med at forstå det grundlæggende, før du dykker ned i hele læseplanen. +> **[Studerende](https://aka.ms/student-page)**: For at bruge denne læseplan på egen hånd, fork hele repoen og gennemfør øvelserne selv, startende med en quiz før lektionen. Læs derefter lektionen og gennemfør resten af aktiviteterne. Prøv at skabe projekterne ved at forstå lektionerne i stedet for at kopiere løsningskoden; dog er denne kode tilgængelig i /solutions-mapperne i hver projektorienteret lektion. En anden idé kunne være at danne en studiegruppe med venner og gennemgå indholdet sammen. For yderligere studier 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 opsætte dit miljø -2. Gennemgå [Brugervejledningen](USAGE.md) for at lære, hvordan du arbejder med curriculumet +**Hurtig start:** +1. Tjek [Installationsvejledningen](INSTALLATION.md) for at opsætte dit miljø +2. Gennemgå [Brugsvejledningen](USAGE.md) for at lære, hvordan du arbejder med læseplanen 3. Start med Lektion 1 og arbejd dig igennem i rækkefølge 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 man bruger dette curriculum. Vi vil meget gerne høre din feedback [i vores diskussionsforum](https://github.com/microsoft/Data-Science-For-Beginners/discussions)! +> **Lærere**: Vi har [inkluderet nogle forslag](for-teachers.md) til, hvordan man kan bruge denne læseplan. Vi vil meget gerne høre din feedback [i vores diskussionsforum](https://github.com/microsoft/Data-Science-For-Beginners/discussions)! ## Mød Teamet @@ -88,12 +88,12 @@ Kom i gang med følgende ressourcer: **Gif af** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal) -> 🎥 Klik på billedet ovenfor for en video om projektet og de folk, der skabte det! +> 🎥 Klik på billedet ovenfor for at se en video om projektet og de folk, der skabte det! ## Pædagogik +Vi har valgt to pædagogiske principper, mens vi udviklede dette pensum: at sikre, at det er projektbaseret, og at det inkluderer hyppige quizzer. Ved slutningen af denne serie vil eleverne have lært grundlæggende principper for datavidenskab, herunder etiske begreber, dataklargøring, forskellige måder at arbejde med data på, datavisualisering, dataanalyse, virkelige anvendelser af datavidenskab og mere. -Vi har valgt to pædagogiske principper, mens vi byggede dette curriculum: at sikre, at det er projektbaseret, og at det inkluderer hyppige quizzer. Ved slutningen af denne serie vil studerende have lært grundlæggende principper for data science, herunder etiske begreber, dataklargøring, forskellige måder at arbejde med data på, datavisualisering, dataanalyse, virkelige anvendelser af data science og meget mere. -Derudover kan en lav-risiko quiz før en lektion hjælpe med at fokusere elevens intention mod at lære et emne, mens en anden quiz efter lektionen sikrer yderligere fastholdelse. Dette pensum er designet til at være fleksibelt og sjovt og kan gennemføres 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 lektion fokus på emnet for eleven, mens en anden quiz efter lektionen 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. > Find vores [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translation](TRANSLATIONS.md) retningslinjer. Vi modtager gerne din konstruktive feedback! @@ -110,19 +110,19 @@ Derudover kan en lav-risiko quiz før en lektion hjælpe med at fokusere elevens - Opgave - [Quiz efter lektionen](https://ff-quizzes.netlify.app/en/) -> **En note om quizzer**: Alle quizzer findes i Quiz-App-mappen, med i alt 40 quizzer, hver med tre spørgsmål. De er linket fra lektionerne, men quiz-appen kan køres lokalt eller implementeres på Azure; følg instruktionerne i `quiz-app`-mappen. De bliver gradvist lokaliseret. +> **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 implementeres på Azure; følg instruktionerne i `quiz-app`-mappen. De bliver gradvist lokaliseret. ## 🎓 Begyndervenlige eksempler -**Ny inden for Data Science?** Vi har oprettet en speciel [eksempelmappe](examples/README.md) med enkel, velkommenteret kode for at hjælpe dig i gang: +**Ny inden for datavidenskab?** Vi har oprettet en speciel [eksempelmappestruktur](examples/README.md) med enkel, velkommenteret kode for at hjælpe dig i gang: -- 🌟 **Hello World** - Dit første data science-program +- 🌟 **Hello World** - Dit første datavidenskabsprogram - 📂 **Indlæsning af data** - Lær at læse og udforske datasæt - 📊 **Enkel analyse** - Beregn statistikker og find mønstre - 📈 **Grundlæggende visualisering** - Lav diagrammer og grafer -- 🔬 **Projekt fra den virkelige verden** - Komplet arbejdsgang fra start til slut +- 🔬 **Virkeligt projekt** - Komplet arbejdsgang fra start til slut -Hvert eksempel indeholder detaljerede kommentarer, der forklarer hvert trin, hvilket gør det perfekt for absolutte begyndere! +Hvert eksempel indeholder detaljerede kommentarer, der forklarer hvert trin, hvilket gør det perfekt til absolutte begyndere! 👉 **[Start med eksemplerne](examples/README.md)** 👈 @@ -130,42 +130,42 @@ Hvert eksempel indeholder detaljerede kommentarer, der forklarer hvert trin, hvi |![ Sketchnote af @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.da.png)| |:---:| -| Data Science For Beginners: Roadmap - _Sketchnote af [@nitya](https://twitter.com/nitya)_ | +| Datavidenskab for begyndere: Roadmap - _Sketchnote af [@nitya](https://twitter.com/nitya)_ | | Lektion Nummer | Emne | Lektion Gruppe | Læringsmål | Linket Lektion | Forfatter | | :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: | -| 01 | Definere Data Science | [Introduktion](1-Introduction/README.md) | Lær de grundlæggende begreber bag data science og hvordan det relaterer 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) | Begreber, udfordringer og rammer inden for dataetik. | [lektion](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | -| 03 | Definere 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) | -| 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. 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 transformation 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 | [Data Visualisering](3-Data-Visualization/README.md) | Lær hvordan man bruger Matplotlib til at visualisere fugledata 🦆 | [lektion](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) | -| 10 | Visualisering af Datafordelinger | [Data Visualisering](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 Proportioner | [Data Visualisering](3-Data-Visualization/README.md) | Visualisering af diskrete og grupperede procentdele. | [lektion](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) | -| 12 | Visualisering af Relationer | [Data Visualisering](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 | [Data Visualisering](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 Data Science-livscyklussen | [Livscyklus](4-Data-Science-Lifecycle/README.md) | Introduktion til data science-livscyklussen og dens første trin med at indsamle 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, der gør det lettere for beslutningstagere at forstå. | [lektion](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | -| 17 | Data Science i Skyen | [Sky Data](5-Data-Science-In-Cloud/README.md) | Denne serie af 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 | [Sky Data](5-Data-Science-In-Cloud/README.md) | Træning af modeller ved hjælp 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 | Data Science i Skyen | [Sky 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 | Data Science i Den Virkelige Verden | [I Den Virkelige Verden](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) | +| 01 | Definere datavidenskab | [Introduktion](1-Introduction/README.md) | Lær de grundlæggende begreber bag datavidenskab og hvordan det relaterer 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 | Datavidenskabsetik | [Introduktion](1-Introduction/README.md) | Begreber, udfordringer og rammer inden for dataetik. | [lektion](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | +| 03 | Definere 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 og sandsynlighed | [Introduktion](1-Introduction/README.md) | Matematiske teknikker inden for sandsynlighed og statistik for at forstå 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. 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 transformation 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 man bruger Matplotlib til at visualisere fugledata 🦆 | [lektion](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) | +| 10 | Visualisering af datafordelinger | [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 proportioner | [Datavisualisering](3-Data-Visualization/README.md) | Visualisering af diskrete og grupperede procentdele. | [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 datavidenskabens livscyklus | [Livscyklus](4-Data-Science-Lifecycle/README.md) | Introduktion til datavidenskabens livscyklus og dens første trin med at indsamle 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 datavidenskabens livscyklus 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 datavidenskabens livscyklus 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 | [Skydata](5-Data-Science-In-Cloud/README.md) | Denne serie af 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 | [Skydata](5-Data-Science-In-Cloud/README.md) | Træning af modeller ved hjælp 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 | [Skydata](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 | [I praksis](6-Data-Science-In-Wild/README.md) | Datavidenskabsdrevne 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-drop-down-menuen og vælg Open with Codespaces. +1. Klik på Code-rullemenuen og vælg Open with Codespaces-indstillingen. 2. Vælg + New 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 hjælp af din lokale maskine og VSCode med VS Code Remote - Containers-udvidelsen: -1. Hvis det er første gang, du bruger en udviklingscontainer, skal du sikre dig, at dit system opfylder forudsætningerne (f.eks. have Docker installeret) i [kom godt i gang-dokumentationen](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started). +1. Hvis det er første gang, du bruger en udviklingscontainer, skal du sikre dig, at dit system opfylder forudsætningerne (f.eks. have Docker installeret) i [dokumentationen for at komme i gang](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started). For at bruge dette repository kan du enten åbne det i et isoleret Docker-volumen: @@ -191,35 +191,35 @@ Vores team producerer andre pensum! Tjek: - [AI Agents for Beginners](https://aka.ms/ai-agents-beginners) - [Generative AI for Beginners](https://aka.ms/genai-beginners) - [Generative AI for Beginners .NET](https://github.com/microsoft/Generative-AI-for-beginners-dotnet) -- [Generative AI med JavaScript](https://github.com/microsoft/generative-ai-with-javascript) -- [Generative AI med Java](https://aka.ms/genaijava) +- [Generative AI with JavaScript](https://github.com/microsoft/generative-ai-with-javascript) +- [Generative AI with Java](https://aka.ms/genaijava) - [AI for Beginners](https://aka.ms/ai-beginners) - [Data Science for Beginners](https://aka.ms/datascience-beginners) - [Bash for Beginners](https://github.com/microsoft/bash-for-beginners) - [ML for Beginners](https://aka.ms/ml-beginners) - [Cybersecurity for Beginners](https://github.com/microsoft/Security-101) -- [Web Dev for Beginners](https://aka.ms/webdev-beginners) -- [IoT for Beginners](https://aka.ms/iot-beginners) -- [Machine Learning for Beginners](https://aka.ms/ml-beginners) -- [XR Development for Beginners](https://aka.ms/xr-dev-for-beginners) -- [Mestre GitHub Copilot til AI-parprogrammering](https://aka.ms/GitHubCopilotAI) -- [XR-udvikling for begyndere](https://github.com/microsoft/xr-development-for-beginners) -- [Mestre GitHub Copilot til C#/.NET-udviklere](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) -- [Vælg dit eget Copilot-eventyr](https://github.com/microsoft/CopilotAdventures) +- [Webudvikling for begyndere](https://aka.ms/webdev-beginners) +- [IoT for begyndere](https://aka.ms/iot-beginners) +- [Maskinlæring for begyndere](https://aka.ms/ml-beginners) +- [XR-udvikling for begyndere](https://aka.ms/xr-dev-for-beginners) +- [Mestre GitHub Copilot til AI-parprogrammering](https://aka.ms/GitHubCopilotAI) +- [XR-udvikling for begyndere](https://github.com/microsoft/xr-development-for-beginners) +- [Mestre GitHub Copilot for C#/.NET-udviklere](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) +- [Vælg dit eget Copilot-eventyr](https://github.com/microsoft/CopilotAdventures) -## Få hjælp +## Få hjælp -**Støder du på problemer?** Tjek vores [Fejlfindingsguide](TROUBLESHOOTING.md) for løsninger på almindelige problemer. +**Støder du på problemer?** Tjek vores [Fejlfindingsguide](TROUBLESHOOTING.md) for løsninger på almindelige problemer. -Hvis du sidder fast eller har spørgsmål om at bygge AI-apps, kan du deltage i: +Hvis du sidder fast eller har spørgsmål om at bygge AI-apps, kan du deltage i: -[![Azure AI Foundry Discord](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) +[![Azure AI Foundry Discord](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) -Hvis du har produktfeedback eller oplever fejl under udviklingen, besøg: +Hvis du har produktfeedback eller oplever fejl under udviklingen, besøg: -[![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- **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, skal det bemærkes, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det originale dokument på dets oprindelige sprog bør betragtes som den autoritative kilde. For kritisk information anbefales professionel menneskelig oversættelse. Vi påtager os ikke ansvar for misforståelser eller fejltolkninger, der måtte opstå som følge af brugen af denne oversættelse. \ No newline at end of file +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 at opnå nøjagtighed, skal det bemærkes, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det originale dokument på dets oprindelige sprog bør betragtes som den autoritative kilde. For kritisk information anbefales professionel menneskelig oversættelse. Vi påtager os ikke ansvar for misforståelser eller fejltolkninger, der måtte opstå som følge af brugen af denne oversættelse. \ No newline at end of file diff --git a/translations/de/README.md b/translations/de/README.md index 68f46f30..6381fcfc 100644 --- a/translations/de/README.md +++ b/translations/de/README.md @@ -1,15 +1,15 @@ -# Datenwissenschaft für Anfänger - Ein Lehrplan +# Data Science für Anfänger - Ein Lehrplan -Azure Cloud Advocates bei Microsoft freuen sich, einen 10-wöchigen, 20-Lektionen umfassenden Lehrplan rund um Datenwissenschaft anzubieten. Jede Lektion enthält Vor- und Nachtests, schriftliche Anweisungen zur Durchführung der Lektion, eine Lösung und eine Aufgabe. Unsere projektbasierte Pädagogik ermöglicht es Ihnen, durch praktisches Arbeiten zu lernen – eine bewährte Methode, um neue Fähigkeiten nachhaltig zu erlernen. +Azure Cloud Advocates bei Microsoft freuen sich, einen 10-wöchigen, 20-Lektionen umfassenden Lehrplan rund um Data Science anzubieten. Jede Lektion enthält Vor- und Nachtests, schriftliche Anweisungen zur Durchführung der Lektion, eine Lösung und eine Aufgabe. Unsere projektbasierte Pädagogik ermöglicht es Ihnen, durch praktisches Arbeiten zu lernen – eine bewährte Methode, um neue Fähigkeiten nachhaltig zu erlernen. **Herzlichen Dank an unsere Autoren:** [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). @@ -18,20 +18,20 @@ Azure Cloud Advocates bei Microsoft freuen sich, einen 10-wöchigen, 20-Lektione |![Sketchnote von @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.de.png)| |:---:| -| Datenwissenschaft für Anfänger - _Sketchnote von [@nitya](https://twitter.com/nitya)_ | +| Data Science für Anfänger - _Sketchnote von [@nitya](https://twitter.com/nitya)_ | ### 🌐 Mehrsprachige Unterstützung #### Unterstützt durch GitHub Action (Automatisiert & Immer aktuell) -[Französisch](../fr/README.md) | [Spanisch](../es/README.md) | [Deutsch](./README.md) | [Russisch](../ru/README.md) | [Arabisch](../ar/README.md) | [Persisch (Farsi)](../fa/README.md) | [Urdu](../ur/README.md) | [Chinesisch (Vereinfacht)](../zh/README.md) | [Chinesisch (Traditionell, Macau)](../mo/README.md) | [Chinesisch (Traditionell, Hongkong)](../hk/README.md) | [Chinesisch (Traditionell, Taiwan)](../tw/README.md) | [Japanisch](../ja/README.md) | [Koreanisch](../ko/README.md) | [Hindi](../hi/README.md) | [Bengalisch](../bn/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Portugiesisch (Portugal)](../pt/README.md) | [Portugiesisch (Brasilien)](../br/README.md) | [Italienisch](../it/README.md) | [Polnisch](../pl/README.md) | [Türkisch](../tr/README.md) | [Griechisch](../el/README.md) | [Thailändisch](../th/README.md) | [Schwedisch](../sv/README.md) | [Dänisch](../da/README.md) | [Norwegisch](../no/README.md) | [Finnisch](../fi/README.md) | [Niederländisch](../nl/README.md) | [Hebräisch](../he/README.md) | [Vietnamesisch](../vi/README.md) | [Indonesisch](../id/README.md) | [Malaiisch](../ms/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Swahili](../sw/README.md) | [Ungarisch](../hu/README.md) | [Tschechisch](../cs/README.md) | [Slowakisch](../sk/README.md) | [Rumänisch](../ro/README.md) | [Bulgarisch](../bg/README.md) | [Serbisch (Kyrillisch)](../sr/README.md) | [Kroatisch](../hr/README.md) | [Slowenisch](../sl/README.md) | [Ukrainisch](../uk/README.md) | [Birmanisch (Myanmar)](../my/README.md) +[Arabisch](../ar/README.md) | [Bengalisch](../bn/README.md) | [Bulgarisch](../bg/README.md) | [Birmanisch (Myanmar)](../my/README.md) | [Chinesisch (Vereinfacht)](../zh/README.md) | [Chinesisch (Traditionell, Hongkong)](../hk/README.md) | [Chinesisch (Traditionell, Macau)](../mo/README.md) | [Chinesisch (Traditionell, Taiwan)](../tw/README.md) | [Kroatisch](../hr/README.md) | [Tschechisch](../cs/README.md) | [Dänisch](../da/README.md) | [Niederländisch](../nl/README.md) | [Estnisch](../et/README.md) | [Finnisch](../fi/README.md) | [Französisch](../fr/README.md) | [Deutsch](./README.md) | [Griechisch](../el/README.md) | [Hebräisch](../he/README.md) | [Hindi](../hi/README.md) | [Ungarisch](../hu/README.md) | [Indonesisch](../id/README.md) | [Italienisch](../it/README.md) | [Japanisch](../ja/README.md) | [Koreanisch](../ko/README.md) | [Litauisch](../lt/README.md) | [Malaiisch](../ms/README.md) | [Marathi](../mr/README.md) | [Nepalesisch](../ne/README.md) | [Norwegisch](../no/README.md) | [Persisch (Farsi)](../fa/README.md) | [Polnisch](../pl/README.md) | [Portugiesisch (Brasilien)](../br/README.md) | [Portugiesisch (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Rumänisch](../ro/README.md) | [Russisch](../ru/README.md) | [Serbisch (Kyrillisch)](../sr/README.md) | [Slowakisch](../sk/README.md) | [Slowenisch](../sl/README.md) | [Spanisch](../es/README.md) | [Swahili](../sw/README.md) | [Schwedisch](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Thai](../th/README.md) | [Türkisch](../tr/README.md) | [Ukrainisch](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamesisch](../vi/README.md) -**Falls Sie zusätzliche Übersetzungen wünschen, finden Sie die unterstützten Sprachen [hier](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Wenn Sie zusätzliche Übersetzungen wünschen, finden Sie die unterstützten Sprachen [hier](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** #### Treten Sie unserer Community bei [![Azure AI Discord](https://dcbadge.limes.pink/api/server/kzRShWzttr)](https://aka.ms/ds4beginners/discord) -Wir haben eine laufende Discord-Serie zum Lernen mit KI. Erfahren Sie mehr und treten Sie uns bei [Learn with AI Series](https://aka.ms/learnwithai/discord) vom 18. bis 30. September 2025 bei. Sie erhalten Tipps und Tricks zur Nutzung von GitHub Copilot für Datenwissenschaft. +Wir haben eine laufende Discord-Serie "Lernen mit KI", erfahren Sie mehr und treten Sie uns bei [Learn with AI Series](https://aka.ms/learnwithai/discord) vom 18. bis 30. September 2025 bei. Sie erhalten Tipps und Tricks zur Nutzung von GitHub Copilot für Data Science. ![Learn with AI series](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.de.jpg) @@ -39,7 +39,7 @@ Wir haben eine laufende Discord-Serie zum Lernen mit KI. Erfahren Sie mehr und t Beginnen Sie mit den folgenden Ressourcen: -- [Student Hub Seite](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) Auf dieser Seite finden Sie Ressourcen für Anfänger, Studentenpakete und sogar Möglichkeiten, einen kostenlosen Zertifikatsgutschein zu erhalten. Diese Seite sollten Sie sich unbedingt als Lesezeichen speichern und regelmäßig besuchen, da wir den Inhalt mindestens monatlich aktualisieren. +- [Student Hub Seite](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) Auf dieser Seite finden Sie Ressourcen für Anfänger, Student Packs und sogar Möglichkeiten, einen kostenlosen Zertifikatsgutschein zu erhalten. Diese Seite sollten Sie sich unbedingt merken und regelmäßig besuchen, da wir den Inhalt mindestens monatlich aktualisieren. - [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Treten Sie einer globalen Gemeinschaft von Studentenbotschaftern bei – dies könnte Ihr Einstieg bei Microsoft sein. # Erste Schritte @@ -53,7 +53,7 @@ Beginnen Sie mit den folgenden Ressourcen: - **[Für Lehrer](for-teachers.md)** - Unterrichtsanleitungen und Ressourcen für den Klassenraum ## 👨‍🎓 Für Studenten -> **Komplette Anfänger**: Neu in der Datenwissenschaft? Beginnen Sie mit unseren [anfängerfreundlichen Beispielen](examples/README.md)! Diese einfachen, gut kommentierten Beispiele helfen Ihnen, die Grundlagen zu verstehen, bevor Sie in den vollständigen Lehrplan eintauchen. +> **Komplette Anfänger**: Neu in der Welt der Data Science? Beginnen Sie mit unseren [anfängerfreundlichen Beispielen](examples/README.md)! Diese einfachen, gut kommentierten Beispiele helfen Ihnen, die Grundlagen zu verstehen, bevor Sie in den vollständigen Lehrplan eintauchen. > **[Studenten](https://aka.ms/student-page)**: Um diesen Lehrplan eigenständig zu nutzen, forken Sie das gesamte Repository und bearbeiten Sie die Übungen eigenständig, beginnend mit einem Quiz vor der Vorlesung. Lesen Sie dann die Vorlesung und führen Sie die restlichen Aktivitäten durch. Versuchen Sie, die Projekte zu erstellen, indem Sie die Lektionen verstehen, anstatt den Lösungscode zu kopieren; dieser Code ist jedoch in den /solutions-Ordnern jeder projektorientierten Lektion verfügbar. Eine weitere Idee wäre, eine Lerngruppe mit Freunden zu bilden und den Inhalt gemeinsam durchzugehen. Für weiterführende Studien empfehlen wir [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). **Schnellstart:** @@ -75,17 +75,17 @@ Beginnen Sie mit den folgenden Ressourcen: > 🎥 Klicken Sie auf das Bild oben, um ein Video über das Projekt und die Personen, die es erstellt haben, anzusehen! ## Pädagogik +Wir haben zwei pädagogische Grundsätze bei der Erstellung dieses Lehrplans gewählt: sicherzustellen, dass er projektbasiert ist und häufige Quizze enthält. Am Ende dieser Serie werden die Studierenden die grundlegenden Prinzipien der Datenwissenschaft erlernt haben, einschließlich ethischer Konzepte, Datenvorbereitung, verschiedener Arbeitsweisen mit Daten, Datenvisualisierung, Datenanalyse, realer Anwendungsfälle der Datenwissenschaft und mehr. -Wir haben zwei pädagogische Grundsätze gewählt, während wir diesen Lehrplan erstellt haben: sicherzustellen, dass er projektbasiert ist und dass er häufige Quizfragen enthält. Am Ende dieser Serie werden die Studenten grundlegende Prinzipien der Datenwissenschaft gelernt haben, einschließlich ethischer Konzepte, Datenvorbereitung, verschiedener Arbeitsweisen mit Daten, Datenvisualisierung, Datenanalyse, realer Anwendungsfälle der Datenwissenschaft und mehr. -Zusätzlich setzt ein Quiz mit geringem Schwierigkeitsgrad vor einer Unterrichtsstunde die Absicht des Lernenden auf das Thema, während ein zweites Quiz nach der Stunde die weitere Speicherung des Wissens sicherstellt. Dieses Curriculum wurde flexibel und unterhaltsam gestaltet und kann vollständig oder teilweise absolviert werden. Die Projekte beginnen klein und werden bis zum Ende des 10-Wochen-Zyklus zunehmend komplexer. +Darüber hinaus setzt ein niedrigschwelliges Quiz vor einer Unterrichtsstunde die Absicht der Studierenden, ein Thema zu lernen, während ein zweites Quiz nach der Stunde die weitere Beibehaltung des Wissens sicherstellt. Dieser Lehrplan wurde so gestaltet, dass er flexibel und unterhaltsam ist und entweder vollständig oder teilweise absolviert werden kann. Die Projekte beginnen klein und werden bis zum Ende des 10-Wochen-Zyklus zunehmend komplexer. -> Finden Sie unsere [Verhaltensregeln](CODE_OF_CONDUCT.md), [Mitwirkungsrichtlinien](CONTRIBUTING.md), [Übersetzungsrichtlinien](TRANSLATIONS.md). Wir freuen uns über Ihr konstruktives Feedback! +> Finden Sie unsere [Verhaltensregeln](CODE_OF_CONDUCT.md), [Beitragsrichtlinien](CONTRIBUTING.md), [Übersetzungsrichtlinien](TRANSLATIONS.md). Wir freuen uns über Ihr konstruktives Feedback! ## Jede Lektion enthält: -- Optionale Sketchnote +- Optionales Sketchnote - Optionales ergänzendes Video -- Aufwärmquiz vor der Lektion +- Warm-up-Quiz vor der Lektion - Schriftliche Lektion - Für projektbasierte Lektionen: Schritt-für-Schritt-Anleitungen zum Erstellen des Projekts - Wissensüberprüfungen @@ -94,7 +94,7 @@ Zusätzlich setzt ein Quiz mit geringem Schwierigkeitsgrad vor einer Unterrichts - Aufgabe - [Quiz nach der Lektion](https://ff-quizzes.netlify.app/en/) -> **Hinweis zu den Quiz**: Alle Quiz befinden sich im Ordner Quiz-App, insgesamt 40 Quiz mit jeweils drei Fragen. Sie sind in den Lektionen verlinkt, aber die Quiz-App kann lokal ausgeführt oder auf Azure bereitgestellt werden; folgen Sie den Anweisungen im `quiz-app`-Ordner. Sie werden nach und nach lokalisiert. +> **Eine Anmerkung zu den Quizzen**: Alle Quizze befinden sich im Quiz-App-Ordner, insgesamt 40 Quizze mit jeweils drei Fragen. Sie sind in den Lektionen verlinkt, aber die Quiz-App kann lokal ausgeführt oder auf Azure bereitgestellt werden; folgen Sie den Anweisungen im `quiz-app`-Ordner. Sie werden schrittweise lokalisiert. ## 🎓 Anfängerfreundliche Beispiele @@ -104,7 +104,7 @@ Zusätzlich setzt ein Quiz mit geringem Schwierigkeitsgrad vor einer Unterrichts - 📂 **Daten laden** - Lernen Sie, Datensätze zu lesen und zu erkunden - 📊 **Einfache Analyse** - Berechnen Sie Statistiken und finden Sie Muster - 📈 **Grundlegende Visualisierung** - Erstellen Sie Diagramme und Grafiken -- 🔬 **Projekt aus der realen Welt** - Vollständiger Workflow von Anfang bis Ende +- 🔬 **Reales Projekt** - Vollständiger Workflow von Anfang bis Ende Jedes Beispiel enthält detaillierte Kommentare, die jeden Schritt erklären, und ist perfekt für absolute Anfänger geeignet! @@ -119,19 +119,19 @@ Jedes Beispiel enthält detaillierte Kommentare, die jeden Schritt erklären, un | Lektion Nummer | Thema | Lektion Gruppierung | Lernziele | Verlinkte Lektion | Autor | | :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: | | 01 | Definition der Datenwissenschaft | [Einführung](1-Introduction/README.md) | Lernen Sie die grundlegenden Konzepte der Datenwissenschaft und wie sie mit künstlicher Intelligenz, maschinellem Lernen und Big Data zusammenhängt. | [Lektion](1-Introduction/01-defining-data-science/README.md) [Video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | -| 02 | Ethik in der Datenwissenschaft | [Einführung](1-Introduction/README.md) | Konzepte, Herausforderungen und Rahmenbedingungen der Datenethik. | [Lektion](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | +| 02 | Ethik in der Datenwissenschaft | [Einführung](1-Introduction/README.md) | Konzepte, Herausforderungen und Rahmenwerke der Datenethik. | [Lektion](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | | 03 | Definition von Daten | [Einführung](1-Introduction/README.md) | Wie Daten klassifiziert werden und ihre häufigsten Quellen. | [Lektion](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) | | 04 | Einführung in Statistik und Wahrscheinlichkeit | [Einführung](1-Introduction/README.md) | Die mathematischen Techniken der Wahrscheinlichkeit und Statistik, um Daten zu verstehen. | [Lektion](1-Introduction/04-stats-and-probability/README.md) [Video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | -| 05 | Arbeiten mit relationalen Daten | [Arbeiten mit Daten](2-Working-With-Data/README.md) | Einführung in relationale Daten und die Grundlagen der Exploration und Analyse relationaler Daten mit der Structured Query Language, auch bekannt als SQL (ausgesprochen „see-quell“). | [Lektion](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | -| 06 | Arbeiten mit NoSQL-Daten | [Arbeiten mit Daten](2-Working-With-Data/README.md) | Einführung in nicht-relationale Daten, ihre verschiedenen Typen und die Grundlagen der Exploration und Analyse von Dokumentdatenbanken. | [Lektion](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| -| 07 | Arbeiten mit Python | [Arbeiten mit Daten](2-Working-With-Data/README.md) | Grundlagen der Verwendung von Python zur Datenexploration mit Bibliotheken wie Pandas. Grundlegendes Verständnis der Python-Programmierung wird empfohlen. | [Lektion](2-Working-With-Data/07-python/README.md) [Video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | +| 05 | Arbeiten mit relationalen Daten | [Arbeiten mit Daten](2-Working-With-Data/README.md) | Einführung in relationale Daten und die Grundlagen der Erkundung und Analyse relationaler Daten mit der Structured Query Language, auch bekannt als SQL (ausgesprochen „see-quell“). | [Lektion](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | +| 06 | Arbeiten mit NoSQL-Daten | [Arbeiten mit Daten](2-Working-With-Data/README.md) | Einführung in nicht-relationale Daten, ihre verschiedenen Typen und die Grundlagen der Erkundung und Analyse von Dokumentdatenbanken. | [Lektion](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| +| 07 | Arbeiten mit Python | [Arbeiten mit Daten](2-Working-With-Data/README.md) | Grundlagen der Nutzung von Python zur Datenexploration mit Bibliotheken wie Pandas. Grundlegendes Verständnis der Python-Programmierung wird empfohlen. | [Lektion](2-Working-With-Data/07-python/README.md) [Video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | | 08 | Datenvorbereitung | [Arbeiten mit Daten](2-Working-With-Data/README.md) | Themen zu Datentechniken zur Bereinigung und Transformation von Daten, um Herausforderungen wie fehlende, ungenaue oder unvollständige Daten zu bewältigen. | [Lektion](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | | 09 | Visualisierung von Mengen | [Datenvisualisierung](3-Data-Visualization/README.md) | Lernen Sie, wie Sie Matplotlib verwenden, um Vogeldaten 🦆 zu visualisieren. | [Lektion](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) | | 10 | Visualisierung von Datenverteilungen | [Datenvisualisierung](3-Data-Visualization/README.md) | Beobachtungen und Trends innerhalb eines Intervalls visualisieren. | [Lektion](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) | | 11 | Visualisierung von Proportionen | [Datenvisualisierung](3-Data-Visualization/README.md) | Diskrete und gruppierte Prozentsätze visualisieren. | [Lektion](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) | | 12 | Visualisierung von Beziehungen | [Datenvisualisierung](3-Data-Visualization/README.md) | Verbindungen und Korrelationen zwischen Datensätzen und ihren Variablen visualisieren. | [Lektion](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) | | 13 | Sinnvolle Visualisierungen | [Datenvisualisierung](3-Data-Visualization/README.md) | Techniken und Leitlinien, um Ihre Visualisierungen wertvoll für effektive Problemlösungen und Erkenntnisse zu machen. | [Lektion](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) | -| 14 | Einführung in den Lebenszyklus der Datenwissenschaft | [Lebenszyklus](4-Data-Science-Lifecycle/README.md) | Einführung in den Lebenszyklus der Datenwissenschaft und seinen ersten Schritt der Datenakquise und -extraktion. | [Lektion](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | +| 14 | Einführung in den Lebenszyklus der Datenwissenschaft | [Lebenszyklus](4-Data-Science-Lifecycle/README.md) | Einführung in den Lebenszyklus der Datenwissenschaft und seinen ersten Schritt der Datenbeschaffung und -extraktion. | [Lektion](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | | 15 | Analyse | [Lebenszyklus](4-Data-Science-Lifecycle/README.md) | Diese Phase des Lebenszyklus der Datenwissenschaft konzentriert sich auf Techniken zur Datenanalyse. | [Lektion](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | | | 16 | Kommunikation | [Lebenszyklus](4-Data-Science-Lifecycle/README.md) | Diese Phase des Lebenszyklus der Datenwissenschaft konzentriert sich darauf, die Erkenntnisse aus den Daten so zu präsentieren, dass sie für Entscheidungsträger leichter verständlich sind. | [Lektion](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | | 17 | Datenwissenschaft in der Cloud | [Cloud-Daten](5-Data-Science-In-Cloud/README.md) | Diese Serie von Lektionen führt in die Datenwissenschaft in der Cloud und ihre Vorteile ein. | [Lektion](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) und [Maud](https://twitter.com/maudstweets) | @@ -142,34 +142,34 @@ Jedes Beispiel enthält detaillierte Kommentare, die jeden Schritt erklären, un ## GitHub Codespaces Folgen Sie diesen Schritten, um dieses Beispiel in einem Codespace zu öffnen: -1. Klicken Sie auf das Dropdown-Menü "Code" und wählen Sie die Option "Mit Codespaces öffnen". +1. Klicken Sie auf das Code-Dropdown-Menü und wählen Sie die Option "Mit Codespaces öffnen". 2. Wählen Sie + Neuer Codespace unten im Fenster. Weitere Informationen finden Sie in der [GitHub-Dokumentation](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace). ## VSCode Remote - Containers -Folgen Sie diesen Schritten, um dieses Repository in einem Container mit Ihrer lokalen Maschine und VSCode mithilfe der VS Code Remote - Containers-Erweiterung zu öffnen: +Folgen Sie diesen Schritten, um dieses Repository in einem Container mit Ihrer lokalen Maschine und VSCode unter Verwendung der VS Code Remote - Containers-Erweiterung zu öffnen: 1. Wenn Sie zum ersten Mal einen Entwicklungscontainer verwenden, stellen Sie bitte sicher, dass Ihr System die Voraussetzungen erfüllt (z. B. Docker installiert ist), wie in der [Einführungsdokumentation](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started) beschrieben. Um dieses Repository zu verwenden, können Sie entweder das Repository in einem isolierten Docker-Volume öffnen: -**Hinweis**: Im Hintergrund wird der Befehl Remote-Containers: **Clone Repository in Container Volume...** verwendet, um den Quellcode in einem Docker-Volume anstelle des lokalen Dateisystems zu klonen. [Volumes](https://docs.docker.com/storage/volumes/) sind das bevorzugte Verfahren zur Speicherung von Containerdaten. +**Hinweis**: Im Hintergrund wird der Befehl Remote-Containers: **Clone Repository in Container Volume...** verwendet, um den Quellcode in einem Docker-Volume anstelle des lokalen Dateisystems zu klonen. [Volumes](https://docs.docker.com/storage/volumes/) sind das bevorzugte Mechanismus zur Persistierung von Containerdaten. Oder öffnen Sie eine lokal geklonte oder heruntergeladene Version des Repositorys: - Klonen Sie dieses Repository auf Ihr lokales Dateisystem. - Drücken Sie F1 und wählen Sie den Befehl **Remote-Containers: Open Folder in Container...**. -- Wählen Sie die geklonte Kopie dieses Ordners aus, warten Sie, bis der Container gestartet ist, und probieren Sie es aus. +- Wählen Sie die geklonte Kopie dieses Ordners aus, warten Sie, bis der Container gestartet ist, und probieren Sie Dinge aus. ## Offline-Zugriff -Sie können diese Dokumentation offline mit [Docsify](https://docsify.js.org/#/) ausführen. Forken Sie dieses Repository, [installieren Sie Docsify](https://docsify.js.org/#/quickstart) auf Ihrem lokalen Rechner, und geben Sie dann im Stammordner dieses Repositorys `docsify serve` ein. Die Website wird auf Port 3000 auf Ihrem localhost bereitgestellt: `localhost:3000`. +Sie können diese Dokumentation offline mit [Docsify](https://docsify.js.org/#/) ausführen. Forken Sie dieses Repository, [installieren Sie Docsify](https://docsify.js.org/#/quickstart) auf Ihrer lokalen Maschine, und geben Sie dann im Stammordner dieses Repositorys `docsify serve` ein. Die Website wird auf Port 3000 auf Ihrem localhost bereitgestellt: `localhost:3000`. -> Hinweis: Notebooks werden nicht über Docsify gerendert. Wenn Sie ein Notebook ausführen müssen, tun Sie dies separat in VS Code mit einem Python-Kernel. +> Hinweis: Notebooks werden nicht über Docsify gerendert, daher führen Sie ein Notebook bei Bedarf separat in VS Code mit einem Python-Kernel aus. -## Weitere Curricula +## Weitere Lehrpläne -Unser Team erstellt weitere Curricula! Schauen Sie sich an: +Unser Team erstellt weitere Lehrpläne! Schauen Sie sich an: - [Edge AI für Anfänger](https://aka.ms/edgeai-for-beginners) - [AI Agents für Anfänger](https://aka.ms/ai-agents-beginners) @@ -182,26 +182,26 @@ Unser Team erstellt weitere Curricula! Schauen Sie sich an: - [Bash für Anfänger](https://github.com/microsoft/bash-for-beginners) - [ML für Anfänger](https://aka.ms/ml-beginners) - [Cybersicherheit für Anfänger](https://github.com/microsoft/Security-101) -- [Webentwicklung für Anfänger](https://aka.ms/webdev-beginners) -- [IoT für Anfänger](https://aka.ms/iot-beginners) -- [Maschinelles Lernen für Anfänger](https://aka.ms/ml-beginners) -- [XR-Entwicklung für Anfänger](https://aka.ms/xr-dev-for-beginners) -- [GitHub Copilot meistern für KI-gestütztes Pair Programming](https://aka.ms/GitHubCopilotAI) -- [XR-Entwicklung für Anfänger](https://github.com/microsoft/xr-development-for-beginners) -- [GitHub Copilot meistern für C#/.NET-Entwickler](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) -- [Wähle dein eigenes Copilot-Abenteuer](https://github.com/microsoft/CopilotAdventures) +- [Webentwicklung für Anfänger](https://aka.ms/webdev-beginners) +- [IoT für Anfänger](https://aka.ms/iot-beginners) +- [Maschinelles Lernen für Anfänger](https://aka.ms/ml-beginners) +- [XR-Entwicklung für Anfänger](https://aka.ms/xr-dev-for-beginners) +- [GitHub Copilot meistern für KI-gestütztes Pair-Programming](https://aka.ms/GitHubCopilotAI) +- [XR-Entwicklung für Anfänger](https://github.com/microsoft/xr-development-for-beginners) +- [GitHub Copilot meistern für C#/.NET-Entwickler](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) +- [Wähle dein eigenes Copilot-Abenteuer](https://github.com/microsoft/CopilotAdventures) -## Hilfe erhalten +## Hilfe erhalten -**Probleme auftreten?** Schau dir unseren [Fehlerbehebungsleitfaden](TROUBLESHOOTING.md) an, um Lösungen für häufige Probleme zu finden. +**Probleme?** Schau dir unseren [Leitfaden zur Fehlerbehebung](TROUBLESHOOTING.md) an, um Lösungen für häufige Probleme zu finden. -Falls du nicht weiterkommst oder Fragen zum Erstellen von KI-Anwendungen hast, tritt bei: +Falls du nicht weiterkommst oder Fragen zum Erstellen von KI-Anwendungen hast, tritt bei: -[![Azure AI Foundry Discord](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) +[![Azure AI Foundry Discord](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) -Falls du Produktfeedback geben möchtest oder Fehler beim Erstellen auftreten, besuche: +Falls du Produktfeedback geben möchtest oder Fehler beim Erstellen auftreten, besuche: -[![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- diff --git a/translations/el/README.md b/translations/el/README.md index ea6c0197..7a04bfc8 100644 --- a/translations/el/README.md +++ b/translations/el/README.md @@ -1,17 +1,17 @@ # Επιστήμη Δεδομένων για Αρχάριους - Ένα Πρόγραμμα Σπουδών -Azure Cloud Advocates στη Microsoft είναι στην ευχάριστη θέση να προσφέρουν ένα πρόγραμμα σπουδών 10 εβδομάδων, 20 μαθημάτων, που αφορά την Επιστήμη Δεδομένων. Κάθε μάθημα περιλαμβάνει κουίζ πριν και μετά το μάθημα, γραπτές οδηγίες για την ολοκλήρωση του μαθήματος, μια λύση και μια εργασία. Η παιδαγωγική μας προσέγγιση βασισμένη σε έργα σας επιτρέπει να μαθαίνετε ενώ δημιουργείτε, ένας αποδεδειγμένος τρόπος για να αποκτήσετε νέες δεξιότητες που "μένουν". +Azure Cloud Advocates στη Microsoft είναι στην ευχάριστη θέση να προσφέρουν ένα πρόγραμμα σπουδών 10 εβδομάδων, με 20 μαθήματα, αφιερωμένο στην Επιστήμη Δεδομένων. Κάθε μάθημα περιλαμβάνει ερωτήσεις πριν και μετά το μάθημα, γραπτές οδηγίες για την ολοκλήρωση του μαθήματος, λύσεις και εργασίες. Η παιδαγωγική μας προσέγγιση βασίζεται σε έργα, επιτρέποντάς σας να μάθετε μέσα από την πράξη, μια αποδεδειγμένη μέθοδος για να εδραιώσετε νέες δεξιότητες. -**Ευχαριστίες στους συγγραφείς μας:** [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). +**Ευχαριστούμε θερμά τους συγγραφείς μας:** [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). **🙏 Ειδικές ευχαριστίες 🙏 στους [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) συγγραφείς, κριτές και συνεισφέροντες περιεχομένου,** όπως οι 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/) @@ -20,66 +20,66 @@ Azure Cloud Advocates στη Microsoft είναι στην ευχάριστη θ |:---:| | Επιστήμη Δεδομένων για Αρχάριους - _Σκίτσο από [@nitya](https://twitter.com/nitya)_ | -### 🌐 Υποστήριξη Πολλών Γλωσσών +### 🌐 Υποστήριξη Πολλαπλών Γλωσσών #### Υποστηρίζεται μέσω GitHub Action (Αυτόματη & Πάντα Ενημερωμένη) -[Γαλλικά](../fr/README.md) | [Ισπανικά](../es/README.md) | [Γερμανικά](../de/README.md) | [Ρωσικά](../ru/README.md) | [Αραβικά](../ar/README.md) | [Περσικά (Φαρσί)](../fa/README.md) | [Ουρντού](../ur/README.md) | [Κινέζικα (Απλοποιημένα)](../zh/README.md) | [Κινέζικα (Παραδοσιακά, Μακάο)](../mo/README.md) | [Κινέζικα (Παραδοσιακά, Χονγκ Κονγκ)](../hk/README.md) | [Κινέζικα (Παραδοσιακά, Ταϊβάν)](../tw/README.md) | [Ιαπωνικά](../ja/README.md) | [Κορεατικά](../ko/README.md) | [Χίντι](../hi/README.md) | [Μπενγκάλι](../bn/README.md) | [Μαραθικά](../mr/README.md) | [Νεπαλικά](../ne/README.md) | [Παντζάμπι (Γκουρμούκι)](../pa/README.md) | [Πορτογαλικά (Πορτογαλία)](../pt/README.md) | [Πορτογαλικά (Βραζιλία)](../br/README.md) | [Ιταλικά](../it/README.md) | [Πολωνικά](../pl/README.md) | [Τουρκικά](../tr/README.md) | [Ελληνικά](./README.md) | [Ταϊλανδικά](../th/README.md) | [Σουηδικά](../sv/README.md) | [Δανικά](../da/README.md) | [Νορβηγικά](../no/README.md) | [Φινλανδικά](../fi/README.md) | [Ολλανδικά](../nl/README.md) | [Εβραϊκά](../he/README.md) | [Βιετναμέζικα](../vi/README.md) | [Ινδονησιακά](../id/README.md) | [Μαλαισιανά](../ms/README.md) | [Ταγκαλόγκ (Φιλιππινέζικα)](../tl/README.md) | [Σουαχίλι](../sw/README.md) | [Ουγγρικά](../hu/README.md) | [Τσέχικα](../cs/README.md) | [Σλοβακικά](../sk/README.md) | [Ρουμανικά](../ro/README.md) | [Βουλγαρικά](../bg/README.md) | [Σερβικά (Κυριλλικά)](../sr/README.md) | [Κροατικά](../hr/README.md) | [Σλοβενικά](../sl/README.md) | [Ουκρανικά](../uk/README.md) | [Βιρμανικά (Μιανμάρ)](../my/README.md) +[Αραβικά](../ar/README.md) | [Μπενγκάλι](../bn/README.md) | [Βουλγαρικά](../bg/README.md) | [Βιρμανικά (Μιανμάρ)](../my/README.md) | [Κινέζικα (Απλοποιημένα)](../zh/README.md) | [Κινέζικα (Παραδοσιακά, Χονγκ Κονγκ)](../hk/README.md) | [Κινέζικα (Παραδοσιακά, Μακάου)](../mo/README.md) | [Κινέζικα (Παραδοσιακά, Ταϊβάν)](../tw/README.md) | [Κροατικά](../hr/README.md) | [Τσέχικα](../cs/README.md) | [Δανικά](../da/README.md) | [Ολλανδικά](../nl/README.md) | [Εσθονικά](../et/README.md) | [Φινλανδικά](../fi/README.md) | [Γαλλικά](../fr/README.md) | [Γερμανικά](../de/README.md) | [Ελληνικά](./README.md) | [Εβραϊκά](../he/README.md) | [Χίντι](../hi/README.md) | [Ουγγρικά](../hu/README.md) | [Ινδονησιακά](../id/README.md) | [Ιταλικά](../it/README.md) | [Ιαπωνικά](../ja/README.md) | [Κορεατικά](../ko/README.md) | [Λιθουανικά](../lt/README.md) | [Μαλαισιανά](../ms/README.md) | [Μαραθικά](../mr/README.md) | [Νεπαλικά](../ne/README.md) | [Νορβηγικά](../no/README.md) | [Περσικά (Φαρσί)](../fa/README.md) | [Πολωνικά](../pl/README.md) | [Πορτογαλικά (Βραζιλία)](../br/README.md) | [Πορτογαλικά (Πορτογαλία)](../pt/README.md) | [Παντζάμπι (Γκουρμούκι)](../pa/README.md) | [Ρουμανικά](../ro/README.md) | [Ρωσικά](../ru/README.md) | [Σερβικά (Κυριλλικά)](../sr/README.md) | [Σλοβακικά](../sk/README.md) | [Σλοβενικά](../sl/README.md) | [Ισπανικά](../es/README.md) | [Σουαχίλι](../sw/README.md) | [Σουηδικά](../sv/README.md) | [Ταγκαλόγκ (Φιλιππινέζικα)](../tl/README.md) | [Ταμίλ](../ta/README.md) | [Ταϊλανδικά](../th/README.md) | [Τουρκικά](../tr/README.md) | [Ουκρανικά](../uk/README.md) | [Ουρντού](../ur/README.md) | [Βιετναμέζικα](../vi/README.md) -**Αν επιθυμείτε να υποστηριχθούν επιπλέον γλώσσες, οι διαθέσιμες γλώσσες παρατίθενται [εδώ](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Αν επιθυμείτε να υποστηριχθούν επιπλέον γλώσσες, μπορείτε να δείτε τη λίστα [εδώ](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** #### Γίνετε μέλος της κοινότητάς μας [![Azure AI Discord](https://dcbadge.limes.pink/api/server/kzRShWzttr)](https://aka.ms/ds4beginners/discord) -Έχουμε μια σειρά εκμάθησης με AI στο Discord, μάθετε περισσότερα και γίνετε μέλος μας στο [Learn with AI Series](https://aka.ms/learnwithai/discord) από 18 - 30 Σεπτεμβρίου, 2025. Θα λάβετε συμβουλές και κόλπα για τη χρήση του GitHub Copilot για την Επιστήμη Δεδομένων. +Έχουμε μια σειρά εκμάθησης με AI σε εξέλιξη στο Discord, μάθετε περισσότερα και συμμετάσχετε στο [Learn with AI Series](https://aka.ms/learnwithai/discord) από τις 18 - 30 Σεπτεμβρίου 2025. Θα λάβετε συμβουλές και κόλπα για τη χρήση του GitHub Copilot στην Επιστήμη Δεδομένων. -![Learn with AI series](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.el.jpg) +![Σειρά εκμάθησης με AI](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.el.jpg) # Είστε φοιτητής; Ξεκινήστε με τους παρακάτω πόρους: -- [Σελίδα Student Hub](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) Σε αυτή τη σελίδα, θα βρείτε πόρους για αρχάριους, πακέτα για φοιτητές και ακόμη και τρόπους για να αποκτήσετε δωρεάν πιστοποιητικό. Αυτή είναι μια σελίδα που θέλετε να προσθέσετε στους σελιδοδείκτες σας και να ελέγχετε από καιρό σε καιρό καθώς αλλάζουμε το περιεχόμενο τουλάχιστον μηνιαία. +- [Σελίδα Student Hub](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) Σε αυτή τη σελίδα, θα βρείτε πόρους για αρχάριους, πακέτα για φοιτητές και ακόμη και τρόπους για να αποκτήσετε δωρεάν πιστοποιητικό. Αυτή είναι μια σελίδα που αξίζει να προσθέσετε στους σελιδοδείκτες σας και να ελέγχετε από καιρό σε καιρό καθώς αλλάζουμε το περιεχόμενο τουλάχιστον μηνιαία. - [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Γίνετε μέλος μιας παγκόσμιας κοινότητας φοιτητών πρεσβευτών, αυτό θα μπορούσε να είναι ο δρόμος σας προς τη Microsoft. # Ξεκινώντας ## 📚 Τεκμηρίωση -- **[Οδηγός Εγκατάστασης](INSTALLATION.md)** - Βήμα προς βήμα οδηγίες για αρχάριους -- **[Οδηγός Χρήσης](USAGE.md)** - Παραδείγματα και κοινές ροές εργασίας -- **[Αντιμετώπιση Προβλημάτων](TROUBLESHOOTING.md)** - Λύσεις για κοινά προβλήματα +- **[Οδηγός Εγκατάστασης](INSTALLATION.md)** - Βήμα προς βήμα οδηγίες εγκατάστασης για αρχάριους +- **[Οδηγός Χρήσης](USAGE.md)** - Παραδείγματα και κοινές διαδικασίες +- **[Επίλυση Προβλημάτων](TROUBLESHOOTING.md)** - Λύσεις για κοινά προβλήματα - **[Οδηγός Συνεισφοράς](CONTRIBUTING.md)** - Πώς να συνεισφέρετε σε αυτό το έργο -- **[Για Εκπαιδευτικούς](for-teachers.md)** - Οδηγίες διδασκαλίας και πόροι για την τάξη +- **[Για Δασκάλους](for-teachers.md)** - Οδηγίες διδασκαλίας και πόροι για την τάξη ## 👨‍🎓 Για Φοιτητές -> **Απόλυτοι Αρχάριοι**: Νέοι στην επιστήμη δεδομένων; Ξεκινήστε με τα [παραδείγματα φιλικά προς αρχάριους](examples/README.md)! Αυτά τα απλά, καλά σχολιασμένα παραδείγματα θα σας βοηθήσουν να κατανοήσετε τα βασικά πριν προχωρήσετε στο πλήρες πρόγραμμα σπουδών. -> **[Φοιτητές](https://aka.ms/student-page)**: για να χρησιμοποιήσετε αυτό το πρόγραμμα σπουδών μόνοι σας, κάντε fork ολόκληρο το repo και ολοκληρώστε τις ασκήσεις μόνοι σας, ξεκινώντας με ένα κουίζ πριν το μάθημα. Στη συνέχεια, διαβάστε το μάθημα και ολοκληρώστε τις υπόλοιπες δραστηριότητες. Προσπαθήστε να δημιουργήσετε τα έργα κατανοώντας τα μαθήματα αντί να αντιγράφετε τον κώδικα λύσης. Ωστόσο, αυτός ο κώδικας είναι διαθέσιμος στους φακέλους /solutions σε κάθε μάθημα που βασίζεται σε έργο. Μια άλλη ιδέα θα ήταν να σχηματίσετε μια ομάδα μελέτης με φίλους και να περάσετε το περιεχόμενο μαζί. Για περαιτέρω μελέτη, προτείνουμε το [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). +> **Απόλυτοι Αρχάριοι**: Νέοι στην επιστήμη δεδομένων; Ξεκινήστε με τα [παραδείγματα για αρχάριους](examples/README.md)! Αυτά τα απλά, καλά σχολιασμένα παραδείγματα θα σας βοηθήσουν να κατανοήσετε τα βασικά πριν προχωρήσετε στο πλήρες πρόγραμμα σπουδών. +> **[Φοιτητές](https://aka.ms/student-page)**: για να χρησιμοποιήσετε αυτό το πρόγραμμα σπουδών μόνοι σας, κάντε fork ολόκληρο το αποθετήριο και ολοκληρώστε τις ασκήσεις μόνοι σας, ξεκινώντας με ένα κουίζ πριν το μάθημα. Στη συνέχεια, διαβάστε το μάθημα και ολοκληρώστε τις υπόλοιπες δραστηριότητες. Προσπαθήστε να δημιουργήσετε τα έργα κατανοώντας τα μαθήματα αντί να αντιγράφετε τον κώδικα λύσης. Ωστόσο, αυτός ο κώδικας είναι διαθέσιμος στους φακέλους /solutions σε κάθε μάθημα που βασίζεται σε έργο. Μια άλλη ιδέα θα ήταν να σχηματίσετε μια ομάδα μελέτης με φίλους και να εξετάσετε το περιεχόμενο μαζί. Για περαιτέρω μελέτη, προτείνουμε το [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). **Γρήγορη Έναρξη:** 1. Ελέγξτε τον [Οδηγό Εγκατάστασης](INSTALLATION.md) για να ρυθμίσετε το περιβάλλον σας -2. Ανατρέξτε στον [Οδηγό Χρήσης](USAGE.md) για να μάθετε πώς να εργάζεστε με το πρόγραμμα σπουδών +2. Ανατρέξτε στον [Οδηγό Χρήσης](USAGE.md) για να μάθετε πώς να δουλεύετε με το πρόγραμμα σπουδών 3. Ξεκινήστε με το Μάθημα 1 και προχωρήστε διαδοχικά 4. Γίνετε μέλος της [κοινότητας Discord](https://aka.ms/ds4beginners/discord) για υποστήριξη -## 👩‍🏫 Για Εκπαιδευτικούς +## 👩‍🏫 Για Δασκάλους -> **Εκπαιδευτικοί**: έχουμε [συμπεριλάβει κάποιες προτάσεις](for-teachers.md) για το πώς να χρησιμοποιήσετε αυτό το πρόγραμμα σπουδών. Θα θέλαμε τα σχόλιά σας [στο φόρουμ συζητήσεων μας](https://github.com/microsoft/Data-Science-For-Beginners/discussions)! +> **Δάσκαλοι**: έχουμε [συμπεριλάβει κάποιες προτάσεις](for-teachers.md) για το πώς να χρησιμοποιήσετε αυτό το πρόγραμμα σπουδών. Θα θέλαμε τα σχόλιά σας [στο φόρουμ συζητήσεων μας](https://github.com/microsoft/Data-Science-For-Beginners/discussions)! ## Γνωρίστε την Ομάδα -[![Promo video](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Promo video") +[![Βίντεο προώθησης](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Βίντεο προώθησης") **Gif από** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal) > 🎥 Κάντε κλικ στην εικόνα παραπάνω για ένα βίντεο σχετικά με το έργο και τους ανθρώπους που το δημιούργησαν! ## Παιδαγωγική +Επιλέξαμε δύο παιδαγωγικές αρχές κατά τη δημιουργία αυτού του προγράμματος σπουδών: να διασφαλίσουμε ότι βασίζεται σε έργα και ότι περιλαμβάνει συχνά κουίζ. Μέχρι το τέλος αυτής της σειράς, οι μαθητές θα έχουν μάθει βασικές αρχές της επιστήμης δεδομένων, συμπεριλαμβανομένων ηθικών εννοιών, προετοιμασίας δεδομένων, διαφορετικών τρόπων εργασίας με δεδομένα, οπτικοποίησης δεδομένων, ανάλυσης δεδομένων, πραγματικών περιπτώσεων χρήσης της επιστήμης δεδομένων και πολλά άλλα. -Έχουμε επιλέξει δύο παιδαγωγικές αρχές κατά την κατασκευή αυτού του προγράμματος σπουδών: να διασφαλίσουμε ότι είναι βασισμένο σε έργα και ότι περιλαμβάνει συχνά κουίζ. Μέχρι το τέλος αυτής της σειράς, οι φοιτητές θα έχουν μάθει βασικές αρχές της επιστήμης δεδομένων, συμπεριλαμβανομένων ηθικών εννοιών, προετοιμασίας δεδομένων, διαφορετικών τρόπων εργασίας με δεδομένα, οπτικοποίησης δεδομένων, ανάλυσης δεδομένων, πραγματικών περιπτώσεων χρήσης της επιστήμης δεδομένων και πολλά άλλα. -Επιπλέον, ένα κουίζ χαμηλής δυσκολίας πριν από το μάθημα βοηθά τον μαθητή να επικεντρωθεί στο θέμα που θα διδαχθεί, ενώ ένα δεύτερο κουίζ μετά το μάθημα ενισχύει την απομνημόνευση. Το πρόγραμμα σπουδών έχει σχεδιαστεί ώστε να είναι ευέλικτο και διασκεδαστικό και μπορεί να ολοκληρωθεί είτε ολόκληρο είτε τμηματικά. Τα έργα ξεκινούν με απλές δραστηριότητες και γίνονται σταδιακά πιο σύνθετα μέχρι το τέλος του 10 εβδομαδιαίου κύκλου. +Επιπλέον, ένα κουίζ χαμηλής πίεσης πριν από το μάθημα θέτει την πρόθεση του μαθητή να μάθει ένα θέμα, ενώ ένα δεύτερο κουίζ μετά το μάθημα διασφαλίζει περαιτέρω την απομνημόνευση. Αυτό το πρόγραμμα σπουδών σχεδιάστηκε για να είναι ευέλικτο και διασκεδαστικό και μπορεί να ολοκληρωθεί είτε ολόκληρο είτε εν μέρει. Τα έργα ξεκινούν μικρά και γίνονται όλο και πιο περίπλοκα μέχρι το τέλος του κύκλου των 10 εβδομάδων. -> Βρείτε τον [Κώδικα Δεοντολογίας](CODE_OF_CONDUCT.md), τις [Οδηγίες Συνεισφοράς](CONTRIBUTING.md), και τις [Οδηγίες Μετάφρασης](TRANSLATIONS.md). Περιμένουμε τα εποικοδομητικά σας σχόλια! +> Βρείτε τον [Κώδικα Δεοντολογίας](CODE_OF_CONDUCT.md), τις οδηγίες για [Συνεισφορά](CONTRIBUTING.md), και [Μεταφράσεις](TRANSLATIONS.md). Καλωσορίζουμε τα εποικοδομητικά σας σχόλια! ## Κάθε μάθημα περιλαμβάνει: @@ -87,14 +87,14 @@ Azure Cloud Advocates στη Microsoft είναι στην ευχάριστη θ - Προαιρετικό συμπληρωματικό βίντεο - Κουίζ προθέρμανσης πριν το μάθημα - Γραπτό μάθημα -- Για μαθήματα που βασίζονται σε έργα, οδηγίες βήμα προς βήμα για την κατασκευή του έργου +- Για μαθήματα βασισμένα σε έργα, οδηγίες βήμα προς βήμα για την κατασκευή του έργου - Έλεγχοι γνώσεων - Μια πρόκληση - Συμπληρωματική ανάγνωση - Εργασία - [Κουίζ μετά το μάθημα](https://ff-quizzes.netlify.app/en/) -> **Σημείωση για τα κουίζ**: Όλα τα κουίζ βρίσκονται στον φάκελο Quiz-App, συνολικά 40 κουίζ με τρεις ερωτήσεις το καθένα. Συνδέονται μέσα από τα μαθήματα, αλλά η εφαρμογή κουίζ μπορεί να εκτελεστεί τοπικά ή να αναπτυχθεί στο Azure. Ακολουθήστε τις οδηγίες στον φάκελο `quiz-app`. Σταδιακά μεταφράζονται σε άλλες γλώσσες. +> **Σημείωση για τα κουίζ**: Όλα τα κουίζ περιλαμβάνονται στον φάκελο Quiz-App, συνολικά 40 κουίζ με τρεις ερωτήσεις το καθένα. Συνδέονται μέσα από τα μαθήματα, αλλά η εφαρμογή κουίζ μπορεί να εκτελεστεί τοπικά ή να αναπτυχθεί στο Azure. Ακολουθήστε τις οδηγίες στον φάκελο `quiz-app`. Σταδιακά μεταφράζονται. ## 🎓 Παραδείγματα για Αρχάριους @@ -104,9 +104,9 @@ Azure Cloud Advocates στη Microsoft είναι στην ευχάριστη θ - 📂 **Φόρτωση Δεδομένων** - Μάθετε να διαβάζετε και να εξερευνάτε σύνολα δεδομένων - 📊 **Απλή Ανάλυση** - Υπολογίστε στατιστικά και βρείτε μοτίβα - 📈 **Βασική Οπτικοποίηση** - Δημιουργήστε γραφήματα και διαγράμματα -- 🔬 **Έργο Πραγματικού Κόσμου** - Ολοκληρωμένη διαδικασία από την αρχή μέχρι το τέλος +- 🔬 **Πραγματικό Έργο** - Ολοκληρωμένη ροή εργασίας από την αρχή μέχρι το τέλος -Κάθε παράδειγμα περιλαμβάνει λεπτομερή σχόλια που εξηγούν κάθε βήμα, καθιστώντας τα ιδανικά για απόλυτους αρχάριους! +Κάθε παράδειγμα περιλαμβάνει λεπτομερή σχόλια που εξηγούν κάθε βήμα, καθιστώντας το ιδανικό για απόλυτους αρχάριους! 👉 **[Ξεκινήστε με τα παραδείγματα](examples/README.md)** 👈 @@ -114,92 +114,89 @@ Azure Cloud Advocates στη Microsoft είναι στην ευχάριστη θ |![ Σκίτσο από @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.el.png)| |:---:| -| Επιστήμη Δεδομένων για Αρχάριους: Χάρτης Πορείας - _Σκίτσο από [@nitya](https://twitter.com/nitya)_ | +| Επιστήμη Δεδομένων για Αρχάριους: Οδικός Χάρτης - _Σκίτσο από [@nitya](https://twitter.com/nitya)_ | -| Αριθμός Μαθήματος | Θέμα | Ομαδοποίηση Μαθήματος | Στόχοι Μάθησης | Συνδεδεμένο Μάθημα | Συγγραφέας | +| Αριθμός Μαθήματος | Θέμα | Ομαδοποίηση Μαθημάτων | Στόχοι Μάθησης | Συνδεδεμένο Μάθημα | Συγγραφέας | | :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: | -| 01 | Ορισμός της Επιστήμης Δεδομένων | [Εισαγωγή](1-Introduction/README.md) | Μάθετε τις βασικές έννοιες της επιστήμης δεδομένων και πώς συνδέεται με την τεχνητή νοημοσύνη, τη μηχανική μάθηση και τα μεγάλα δεδομένα. | [μάθημα](1-Introduction/01-defining-data-science/README.md) [βίντεο](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | -| 02 | Ηθική στην Επιστήμη Δεδομένων | [Εισαγωγή](1-Introduction/README.md) | Έννοιες, προκλήσεις και πλαίσια ηθικής στην επιστήμη δεδομένων. | [μάθημα](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | +| 01 | Ορισμός της Επιστήμης Δεδομένων | [Εισαγωγή](1-Introduction/README.md) | Μάθετε τις βασικές έννοιες της επιστήμης δεδομένων και πώς σχετίζεται με την τεχνητή νοημοσύνη, τη μηχανική μάθηση και τα μεγάλα δεδομένα. | [μάθημα](1-Introduction/01-defining-data-science/README.md) [βίντεο](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | +| 02 | Ηθική στην Επιστήμη Δεδομένων | [Εισαγωγή](1-Introduction/README.md) | Έννοιες ηθικής δεδομένων, προκλήσεις και πλαίσια. | [μάθημα](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | | 03 | Ορισμός των Δεδομένων | [Εισαγωγή](1-Introduction/README.md) | Πώς ταξινομούνται τα δεδομένα και ποιες είναι οι κοινές πηγές τους. | [μάθημα](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) | -| 04 | Εισαγωγή στις Στατιστικές & Πιθανότητες | [Εισαγωγή](1-Introduction/README.md) | Μαθηματικές τεχνικές πιθανοτήτων και στατιστικών για την κατανόηση των δεδομένων. | [μάθημα](1-Introduction/04-stats-and-probability/README.md) [βίντεο](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | -| 05 | Εργασία με Σχεσιακά Δεδομένα | [Εργασία με Δεδομένα](2-Working-With-Data/README.md) | Εισαγωγή στα σχεσιακά δεδομένα και βασικές τεχνικές εξερεύνησης και ανάλυσης με τη Δομημένη Γλώσσα Ερωτημάτων (SQL). | [μάθημα](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | -| 06 | Εργασία με Δεδομένα NoSQL | [Εργασία με Δεδομένα](2-Working-With-Data/README.md) | Εισαγωγή στα μη σχεσιακά δεδομένα, τους διάφορους τύπους τους και τις βασικές τεχνικές εξερεύνησης και ανάλυσης βάσεων δεδομένων εγγράφων. | [μάθημα](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| -| 07 | Εργασία με Python | [Εργασία με Δεδομένα](2-Working-With-Data/README.md) | Βασικές τεχνικές χρήσης της Python για εξερεύνηση δεδομένων με βιβλιοθήκες όπως η Pandas. Συνιστάται θεμελιώδης κατανόηση της Python. | [μάθημα](2-Working-With-Data/07-python/README.md) [βίντεο](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | -| 08 | Προετοιμασία Δεδομένων | [Εργασία με Δεδομένα](2-Working-With-Data/README.md) | Θέματα τεχνικών καθαρισμού και μετασχηματισμού δεδομένων για την αντιμετώπιση προβλημάτων όπως ελλιπή ή ανακριβή δεδομένα. | [μάθημα](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | +| 04 | Εισαγωγή στη Στατιστική & Πιθανότητες | [Εισαγωγή](1-Introduction/README.md) | Οι μαθηματικές τεχνικές των πιθανοτήτων και της στατιστικής για την κατανόηση των δεδομένων. | [μάθημα](1-Introduction/04-stats-and-probability/README.md) [βίντεο](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | +| 05 | Εργασία με Σχεσιακά Δεδομένα | [Εργασία με Δεδομένα](2-Working-With-Data/README.md) | Εισαγωγή στα σχεσιακά δεδομένα και τις βασικές αρχές εξερεύνησης και ανάλυσης σχεσιακών δεδομένων με τη Γλώσσα Δομημένων Ερωτημάτων, γνωστή και ως SQL (προφέρεται "σι-κουέλ"). | [μάθημα](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | +| 06 | Εργασία με Δεδομένα NoSQL | [Εργασία με Δεδομένα](2-Working-With-Data/README.md) | Εισαγωγή στα μη σχεσιακά δεδομένα, τους διάφορους τύπους τους και τις βασικές αρχές εξερεύνησης και ανάλυσης βάσεων δεδομένων εγγράφων. | [μάθημα](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| +| 07 | Εργασία με Python | [Εργασία με Δεδομένα](2-Working-With-Data/README.md) | Βασικές αρχές χρήσης της Python για εξερεύνηση δεδομένων με βιβλιοθήκες όπως η Pandas. Συνιστάται θεμελιώδης κατανόηση του προγραμματισμού Python. | [μάθημα](2-Working-With-Data/07-python/README.md) [βίντεο](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | +| 08 | Προετοιμασία Δεδομένων | [Εργασία με Δεδομένα](2-Working-With-Data/README.md) | Θέματα σχετικά με τεχνικές καθαρισμού και μετασχηματισμού δεδομένων για την αντιμετώπιση προκλήσεων όπως ελλιπή ή ανακριβή δεδομένα. | [μάθημα](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | | 09 | Οπτικοποίηση Ποσοτήτων | [Οπτικοποίηση Δεδομένων](3-Data-Visualization/README.md) | Μάθετε πώς να χρησιμοποιείτε το Matplotlib για να οπτικοποιήσετε δεδομένα πουλιών 🦆 | [μάθημα](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) | | 10 | Οπτικοποίηση Κατανομών Δεδομένων | [Οπτικοποίηση Δεδομένων](3-Data-Visualization/README.md) | Οπτικοποίηση παρατηρήσεων και τάσεων μέσα σε ένα διάστημα. | [μάθημα](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) | | 11 | Οπτικοποίηση Αναλογιών | [Οπτικοποίηση Δεδομένων](3-Data-Visualization/README.md) | Οπτικοποίηση διακριτών και ομαδοποιημένων ποσοστών. | [μάθημα](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) | -| 12 | Οπτικοποίηση Σχέσεων | [Οπτικοποίηση Δεδομένων](3-Data-Visualization/README.md) | Οπτικοποίηση συνδέσεων και συσχετίσεων μεταξύ συνόλων δεδομένων και μεταβλητών τους. | [μάθημα](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) | -| 13 | Σημαντικές Οπτικοποιήσεις | [Οπτικοποίηση Δεδομένων](3-Data-Visualization/README.md) | Τεχνικές και καθοδήγηση για τη δημιουργία οπτικοποιήσεων που είναι χρήσιμες για αποτελεσματική επίλυση προβλημάτων και εξαγωγή πληροφοριών. | [μάθημα](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) | -| 14 | Εισαγωγή στον Κύκλο Ζωής της Επιστήμης Δεδομένων | [Κύκλος Ζωής](4-Data-Science-Lifecycle/README.md) | Εισαγωγή στον κύκλο ζωής της επιστήμης δεδομένων και το πρώτο βήμα της απόκτησης και εξαγωγής δεδομένων. | [μάθημα](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | +| 12 | Οπτικοποίηση Σχέσεων | [Οπτικοποίηση Δεδομένων](3-Data-Visualization/README.md) | Οπτικοποίηση συνδέσεων και συσχετίσεων μεταξύ συνόλων δεδομένων και των μεταβλητών τους. | [μάθημα](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) | +| 13 | Σημαντικές Οπτικοποιήσεις | [Οπτικοποίηση Δεδομένων](3-Data-Visualization/README.md) | Τεχνικές και καθοδήγηση για να κάνετε τις οπτικοποιήσεις σας πολύτιμες για αποτελεσματική επίλυση προβλημάτων και εξαγωγή συμπερασμάτων. | [μάθημα](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) | +| 14 | Εισαγωγή στον Κύκλο Ζωής της Επιστήμης Δεδομένων | [Κύκλος Ζωής](4-Data-Science-Lifecycle/README.md) | Εισαγωγή στον κύκλο ζωής της επιστήμης δεδομένων και το πρώτο του βήμα, την απόκτηση και εξαγωγή δεδομένων. | [μάθημα](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | | 15 | Ανάλυση | [Κύκλος Ζωής](4-Data-Science-Lifecycle/README.md) | Αυτή η φάση του κύκλου ζωής της επιστήμης δεδομένων επικεντρώνεται σε τεχνικές ανάλυσης δεδομένων. | [μάθημα](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | | -| 16 | Επικοινωνία | [Κύκλος Ζωής](4-Data-Science-Lifecycle/README.md) | Αυτή η φάση του κύκλου ζωής της επιστήμης δεδομένων επικεντρώνεται στην παρουσίαση των πληροφοριών από τα δεδομένα με τρόπο που να διευκολύνει τους υπεύθυνους λήψης αποφάσεων να κατανοήσουν. | [μάθημα](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | -| 17 | Επιστήμη Δεδομένων στο Cloud | [Cloud Δεδομένα](5-Data-Science-In-Cloud/README.md) | Αυτή η σειρά μαθημάτων εισάγει την επιστήμη δεδομένων στο cloud και τα οφέλη της. | [μάθημα](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) και [Maud](https://twitter.com/maudstweets) | -| 18 | Επιστήμη Δεδομένων στο Cloud | [Cloud Δεδομένα](5-Data-Science-In-Cloud/README.md) | Εκπαίδευση μοντέλων χρησιμοποιώντας εργαλεία Low Code. |[μάθημα](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) και [Maud](https://twitter.com/maudstweets) | -| 19 | Επιστήμη Δεδομένων στο Cloud | [Cloud Δεδομένα](5-Data-Science-In-Cloud/README.md) | Ανάπτυξη μοντέλων με το Azure Machine Learning Studio. | [μάθημα](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) και [Maud](https://twitter.com/maudstweets) | +| 16 | Επικοινωνία | [Κύκλος Ζωής](4-Data-Science-Lifecycle/README.md) | Αυτή η φάση του κύκλου ζωής της επιστήμης δεδομένων επικεντρώνεται στην παρουσίαση των συμπερασμάτων από τα δεδομένα με τρόπο που να είναι κατανοητός από τους υπεύθυνους λήψης αποφάσεων. | [μάθημα](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | +| 17 | Επιστήμη Δεδομένων στο Cloud | [Δεδομένα στο Cloud](5-Data-Science-In-Cloud/README.md) | Αυτή η σειρά μαθημάτων εισάγει την επιστήμη δεδομένων στο cloud και τα οφέλη της. | [μάθημα](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) και [Maud](https://twitter.com/maudstweets) | +| 18 | Επιστήμη Δεδομένων στο Cloud | [Δεδομένα στο Cloud](5-Data-Science-In-Cloud/README.md) | Εκπαίδευση μοντέλων χρησιμοποιώντας εργαλεία χαμηλού κώδικα. |[μάθημα](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) και [Maud](https://twitter.com/maudstweets) | +| 19 | Επιστήμη Δεδομένων στο Cloud | [Δεδομένα στο Cloud](5-Data-Science-In-Cloud/README.md) | Ανάπτυξη μοντέλων με το Azure Machine Learning Studio. | [μάθημα](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) και [Maud](https://twitter.com/maudstweets) | | 20 | Επιστήμη Δεδομένων στον Πραγματικό Κόσμο | [Στον Πραγματικό Κόσμο](6-Data-Science-In-Wild/README.md) | Έργα επιστήμης δεδομένων στον πραγματικό κόσμο. | [μάθημα](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) | ## GitHub Codespaces Ακολουθήστε αυτά τα βήματα για να ανοίξετε αυτό το δείγμα σε ένα Codespace: 1. Κάντε κλικ στο αναπτυσσόμενο μενού Code και επιλέξτε την επιλογή Open with Codespaces. -2. Επιλέξτε + New codespace στο κάτω μέρος του πίνακα. +2. Επιλέξτε + New codespace στο κάτω μέρος του παραθύρου. Για περισσότερες πληροφορίες, δείτε την [τεκμηρίωση του GitHub](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace). ## VSCode Remote - Containers Ακολουθήστε αυτά τα βήματα για να ανοίξετε αυτό το αποθετήριο σε ένα container χρησιμοποιώντας τον τοπικό σας υπολογιστή και το VSCode με την επέκταση VS Code Remote - Containers: -1. Εάν είναι η πρώτη φορά που χρησιμοποιείτε container ανάπτυξης, βεβαιωθείτε ότι το σύστημά σας πληροί τις προϋποθέσεις (π.χ. έχετε εγκαταστήσει το Docker) σύμφωνα με την [τεκμηρίωση για την έναρξη](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started). +1. Αν είναι η πρώτη φορά που χρησιμοποιείτε container ανάπτυξης, βεβαιωθείτε ότι το σύστημά σας πληροί τις προϋποθέσεις (π.χ. να έχετε εγκατεστημένο το Docker) σύμφωνα με την [τεκμηρίωση για την έναρξη](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started). Για να χρησιμοποιήσετε αυτό το αποθετήριο, μπορείτε είτε να το ανοίξετε σε έναν απομονωμένο όγκο Docker: -**Σημείωση**: Στο παρασκήνιο, αυτό θα χρησιμοποιήσει την εντολή Remote-Containers: **Clone Repository in Container Volume...** για να κλωνοποιήσει τον πηγαίο κώδικα σε έναν όγκο Docker αντί για το τοπικό σύστημα αρχείων. Οι [Όγκοι](https://docs.docker.com/storage/volumes/) είναι ο προτιμώμενος μηχανισμός για τη διατήρηση δεδομένων container. +**Σημείωση**: Στο παρασκήνιο, αυτό θα χρησιμοποιήσει την εντολή Remote-Containers: **Clone Repository in Container Volume...** για να κλωνοποιήσει τον πηγαίο κώδικα σε έναν όγκο Docker αντί για το τοπικό σύστημα αρχείων. Οι [Όγκοι](https://docs.docker.com/storage/volumes/) είναι ο προτιμώμενος μηχανισμός για την αποθήκευση δεδομένων container. -Ή να ανοίξετε μια τοπικά κλωνοποιημένη ή ληφθείσα έκδοση του αποθετηρίου: +Ή να ανοίξετε μια τοπικά κλωνοποιημένη ή κατεβασμένη έκδοση του αποθετηρίου: - Κλωνοποιήστε αυτό το αποθετήριο στο τοπικό σας σύστημα αρχείων. - Πατήστε F1 και επιλέξτε την εντολή **Remote-Containers: Open Folder in Container...**. -- Επιλέξτε την κλωνοποιημένη έκδοση αυτού του φακέλου, περιμένετε να ξεκινήσει το container και δοκιμάστε το. +- Επιλέξτε το κλωνοποιημένο αντίγραφο αυτού του φακέλου, περιμένετε να ξεκινήσει το container και δοκιμάστε το. ## Πρόσβαση εκτός σύνδεσης -Μπορείτε να εκτελέσετε αυτήν την τεκμηρίωση εκτός σύνδεσης χρησιμοποιώντας το [Docsify](https://docsify.js.org/#/). Κλωνοποιήστε αυτό το αποθετήριο, [εγκαταστήστε το Docsify](https://docsify.js.org/#/quickstart) στον τοπικό σας υπολογιστή και στη συνέχεια, στον ριζικό φάκελο αυτού του αποθετηρίου, πληκτρολογήστε `docsify serve`. Ο ιστότοπος θα εξυπηρετείται στην θύρα 3000 του localhost σας: `localhost:3000`. +Μπορείτε να εκτελέσετε αυτήν την τεκμηρίωση εκτός σύνδεσης χρησιμοποιώντας το [Docsify](https://docsify.js.org/#/). Κλωνοποιήστε αυτό το αποθετήριο, [εγκαταστήστε το Docsify](https://docsify.js.org/#/quickstart) στον τοπικό σας υπολογιστή και στη συνέχεια, στον ριζικό φάκελο αυτού του αποθετηρίου, πληκτρολογήστε `docsify serve`. Ο ιστότοπος θα εξυπηρετείται στη θύρα 3000 του localhost σας: `localhost:3000`. > Σημείωση, τα notebooks δεν θα εμφανίζονται μέσω του Docsify, οπότε όταν χρειαστεί να εκτελέσετε ένα notebook, κάντε το ξεχωριστά στο VS Code χρησιμοποιώντας έναν πυρήνα Python. ## Άλλα Προγράμματα Σπουδών -Η ομάδα μας δημιουργεί και άλλα προγράμματα σπουδών! Δείτε: +Η ομάδα μας παράγει και άλλα προγράμματα σπουδών! Δείτε: - [Edge AI για Αρχάριους](https://aka.ms/edgeai-for-beginners) - [AI Agents για Αρχάριους](https://aka.ms/ai-agents-beginners) - [Γενετική AI για Αρχάριους](https://aka.ms/genai-beginners) -- [Γενετική AI για Αρχάριους .NET](https://github.com/microsoft/Generative-AI-for-beginners-dotnet) -- [Γενετική AI με JavaScript](https://github.com/microsoft/generative-ai-with-javascript) -- [Γενετική AI με Java](https://aka.ms/genaijava) -- [AI για Αρχάριους](https://aka.ms/ai-beginners) -- [Επιστήμη Δεδομένων για Αρχάριους](https://aka.ms/datascience-beginners) -- [Bash για Αρχάριους](https://github.com/microsoft/bash-for-beginners) -- [ML για Αρχάριους](https://aka.ms/ml-beginners) -- [Κυβερνοασφάλεια για Αρχάριους](https -- [Κατακτώντας το GitHub Copilot για Συνεργατικό Προγραμματισμό AI](https://aka.ms/GitHubCopilotAI) -- [Ανάπτυξη XR για Αρχάριους](https://github.com/microsoft/xr-development-for-beginners) -- [Κατακτώντας το GitHub Copilot για Προγραμματιστές C#/.NET](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) -- [Επιλέξτε τη Δική σας Περιπέτεια με το Copilot](https://github.com/microsoft/CopilotAdventures) +- [Γενετική AI για Αρχάριους .NET](https://github.com/m +- [Web Dev για Αρχάριους](https://aka.ms/webdev-beginners) +- [IoT για Αρχάριους](https://aka.ms/iot-beginners) +- [Machine Learning για Αρχάριους](https://aka.ms/ml-beginners) +- [Ανάπτυξη XR για Αρχάριους](https://aka.ms/xr-dev-for-beginners) +- [Mastering GitHub Copilot για Συνεργατική Προγραμματιστική Τεχνητής Νοημοσύνης](https://aka.ms/GitHubCopilotAI) +- [Ανάπτυξη XR για Αρχάριους](https://github.com/microsoft/xr-development-for-beginners) +- [Mastering GitHub Copilot για Προγραμματιστές C#/.NET](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) +- [Διάλεξε τη Δική σου Περιπέτεια με το Copilot](https://github.com/microsoft/CopilotAdventures) -## Λήψη Βοήθειας +## Λήψη Βοήθειας -**Αντιμετωπίζετε προβλήματα;** Ελέγξτε τον [Οδηγό Επίλυσης Προβλημάτων](TROUBLESHOOTING.md) για λύσεις σε κοινά ζητήματα. +**Αντιμετωπίζετε προβλήματα;** Ελέγξτε τον [Οδηγό Επίλυσης Προβλημάτων](TROUBLESHOOTING.md) για λύσεις σε κοινά ζητήματα. -Αν κολλήσετε ή έχετε ερωτήσεις σχετικά με την ανάπτυξη εφαρμογών AI, συμμετάσχετε: +Αν κολλήσετε ή έχετε ερωτήσεις σχετικά με την ανάπτυξη εφαρμογών AI, συμμετάσχετε: -[![Azure AI Foundry Discord](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) +[![Azure AI Foundry Discord](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) -Αν έχετε σχόλια για το προϊόν ή αντιμετωπίζετε σφάλματα κατά την ανάπτυξη, επισκεφθείτε: +Αν έχετε σχόλια για το προϊόν ή αντιμετωπίζετε σφάλματα κατά την ανάπτυξη, επισκεφθείτε: -[![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- **Αποποίηση ευθύνης**: -Αυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία αυτόματης μετάφρασης [Co-op Translator](https://github.com/Azure/co-op-translator). Παρόλο που καταβάλλουμε προσπάθειες για ακρίβεια, παρακαλούμε να έχετε υπόψη ότι οι αυτόματες μεταφράσεις ενδέχεται να περιέχουν σφάλματα ή ανακρίβειες. Το πρωτότυπο έγγραφο στη μητρική του γλώσσα θα πρέπει να θεωρείται η αυθεντική πηγή. Για κρίσιμες πληροφορίες, συνιστάται επαγγελματική ανθρώπινη μετάφραση. Δεν φέρουμε ευθύνη για τυχόν παρεξηγήσεις ή εσφαλμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης. \ No newline at end of file +Αυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία αυτόματης μετάφρασης [Co-op Translator](https://github.com/Azure/co-op-translator). Παρόλο που καταβάλλουμε προσπάθειες για ακρίβεια, παρακαλούμε να έχετε υπόψη ότι οι αυτόματες μεταφράσεις ενδέχεται να περιέχουν λάθη ή ανακρίβειες. Το πρωτότυπο έγγραφο στη μητρική του γλώσσα θα πρέπει να θεωρείται η αυθεντική πηγή. Για κρίσιμες πληροφορίες, συνιστάται επαγγελματική ανθρώπινη μετάφραση. Δεν φέρουμε ευθύνη για τυχόν παρεξηγήσεις ή εσφαλμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης. \ No newline at end of file diff --git a/translations/en/README.md b/translations/en/README.md index 2e6d99ed..3f60e3b2 100644 --- a/translations/en/README.md +++ b/translations/en/README.md @@ -1,19 +1,36 @@ # Data Science for Beginners - A Curriculum -Azure Cloud Advocates at Microsoft are excited to present a 10-week, 20-lesson curriculum focused on Data Science. Each lesson includes pre-lesson and post-lesson quizzes, detailed instructions, solutions, and assignments. This project-based approach helps you learn effectively by building practical skills. +[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) + +[![GitHub license](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE) +[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/) +[![GitHub issues](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/) +[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/) +[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) + +[![GitHub watchers](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 forks](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 stars](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/) + +[![](https://dcbadge.vercel.app/api/server/ByRwuEEgH4)](https://discord.gg/zxKYvhSnVp?WT.mc_id=academic-000002-leestott) + +[![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) + +Azure Cloud Advocates at Microsoft are excited to present a 10-week, 20-lesson curriculum focused on Data Science. Each lesson includes pre-lesson and post-lesson quizzes, detailed instructions, solutions, and assignments. This project-based approach helps you learn by doing, ensuring the knowledge sticks. **Special thanks to our authors:** [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). -**🙏 A big thank you 🙏 to our [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) authors, reviewers, and contributors,** including 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/). +**🙏 A big thank you 🙏 to our [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) authors, reviewers, and contributors,** including 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/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.en.png)| |:---:| @@ -23,7 +40,7 @@ Azure Cloud Advocates at Microsoft are excited to present a 10-week, 20-lesson c #### Supported via GitHub Action (Automated & Always Up-to-Date) -[French](../fr/README.md) | [Spanish](../es/README.md) | [German](../de/README.md) | [Russian](../ru/README.md) | [Arabic](../ar/README.md) | [Persian (Farsi)](../fa/README.md) | [Urdu](../ur/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Japanese](../ja/README.md) | [Korean](../ko/README.md) | [Hindi](../hi/README.md) | [Bengali](../bn/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Portuguese (Brazil)](../br/README.md) | [Italian](../it/README.md) | [Polish](../pl/README.md) | [Turkish](../tr/README.md) | [Greek](../el/README.md) | [Thai](../th/README.md) | [Swedish](../sv/README.md) | [Danish](../da/README.md) | [Norwegian](../no/README.md) | [Finnish](../fi/README.md) | [Dutch](../nl/README.md) | [Hebrew](../he/README.md) | [Vietnamese](../vi/README.md) | [Indonesian](../id/README.md) | [Malay](../ms/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Swahili](../sw/README.md) | [Hungarian](../hu/README.md) | [Czech](../cs/README.md) | [Slovak](../sk/README.md) | [Romanian](../ro/README.md) | [Bulgarian](../bg/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Croatian](../hr/README.md) | [Slovenian](../sl/README.md) | [Ukrainian](../uk/README.md) | [Burmese (Myanmar)](../my/README.md) +[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/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) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) **If you'd like additional translations, supported languages are listed [here](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** @@ -38,7 +55,7 @@ We are hosting a Discord "Learn with AI" series from September 18–30, 2025. Jo Get started with these resources: -- [Student Hub page](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum): Find beginner resources, student packs, and even ways to get a free certification voucher. Bookmark this page and check back regularly for updates. +- [Student Hub page](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum): Find beginner resources, student packs, and even ways to get a free certification voucher. Bookmark this page and check back often, as content is updated monthly. - [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum): Join a global community of student ambassadors—your gateway to Microsoft. # Getting Started @@ -53,17 +70,17 @@ Get started with these resources: ## 👨‍🎓 For Students > **Complete Beginners**: New to data science? Start with our [beginner-friendly examples](examples/README.md)! These simple, well-commented examples will help you understand the basics before diving into the full curriculum. -> **[Students](https://aka.ms/student-page)**: To use this curriculum independently, fork the repo and complete the exercises starting with the pre-lecture quiz. Then read the lecture and complete the activities. Try to build the projects by understanding the lessons rather than copying the solution code (available in the /solutions folders). Alternatively, form a study group with friends and go through the content together. For further study, explore [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). +> **[Students](https://aka.ms/student-page)**: To use this curriculum independently, fork the entire repository and complete the exercises on your own, starting with the pre-lecture quiz. Then, read the lecture and complete the activities. Try to build the projects by understanding the lessons rather than copying the solution code (though solutions are available in the /solutions folders for each project-based lesson). Alternatively, form a study group with friends and go through the content together. For further study, we recommend [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). **Quick Start:** -1. Follow the [Installation Guide](INSTALLATION.md) to set up your environment. +1. Check the [Installation Guide](INSTALLATION.md) to set up your environment. 2. Review the [Usage Guide](USAGE.md) to learn how to work with the curriculum. -3. Begin with Lesson 1 and progress sequentially. +3. Start with Lesson 1 and progress sequentially. 4. Join our [Discord community](https://aka.ms/ds4beginners/discord) for support. ## 👩‍🏫 For Teachers -> **Teachers**: We have [included some suggestions](for-teachers.md) on how to use this curriculum. Share your feedback [in our discussion forum](https://github.com/microsoft/Data-Science-For-Beginners/discussions)! +> **Teachers**: We have [included some suggestions](for-teachers.md) on how to use this curriculum. We'd love your feedback [in our discussion forum](https://github.com/microsoft/Data-Science-For-Beginners/discussions)! ## Meet the Team @@ -71,12 +88,12 @@ Get started with these resources: **Gif by** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal) -> 🎥 Click the image above to watch a video about the project and the team behind it! +> 🎥 Click the image above for a video about the project and the team behind it! ## Pedagogy +We have selected two educational principles while designing this curriculum: ensuring it is project-based and includes frequent quizzes. By the end of this series, students will have learned the basic principles of data science, including ethical concepts, data preparation, various methods of working with data, data visualization, data analysis, real-world applications of data science, and more. -This curriculum is built on two key principles: project-based learning and frequent quizzes. By the end of the series, students will have gained foundational knowledge in data science, including ethical considerations, data preparation, data visualization, data analysis, real-world applications, and more. -In addition, a low-stakes quiz before a class helps students focus on learning a topic, while a second quiz after class reinforces retention. This curriculum is designed to be flexible and enjoyable, and can be completed in full or in parts. The projects start small and gradually become more complex by the end of the 10-week cycle. +Additionally, a low-pressure quiz before a class helps set the student's focus on the topic, while a second quiz after class reinforces retention. This curriculum is designed to be flexible and enjoyable, and it can be completed in its entirety or in parts. The projects start small and gradually increase in complexity over the 10-week cycle. > Find our [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), and [Translation](TRANSLATIONS.md) guidelines. We welcome your constructive feedback! @@ -86,22 +103,22 @@ In addition, a low-stakes quiz before a class helps students focus on learning a - Optional supplemental video - Pre-lesson warmup quiz - Written lesson -- Step-by-step guides for project-based lessons +- For project-based lessons, step-by-step guides on how to build the project - Knowledge checks - A challenge - Supplemental reading - Assignment - [Post-lesson quiz](https://ff-quizzes.netlify.app/en/) -> **A note about quizzes**: All quizzes are located in the Quiz-App folder, with a total of 40 quizzes containing three questions each. They are linked within the lessons, but the quiz app can be run locally or deployed to Azure; follow the instructions in the `quiz-app` folder. Localization is ongoing. +> **A note about quizzes**: All quizzes are located in the Quiz-App folder, with a total of 40 quizzes, each containing three questions. They are linked within the lessons, but the quiz app can also be run locally or deployed to Azure; follow the instructions in the `quiz-app` folder. Localization is ongoing. ## 🎓 Beginner-Friendly Examples **New to Data Science?** We've created a special [examples directory](examples/README.md) with simple, well-commented code to help you get started: - 🌟 **Hello World** - Your first data science program -- 📂 **Loading Data** - Learn how to read and explore datasets -- 📊 **Simple Analysis** - Calculate statistics and identify patterns +- 📂 **Loading Data** - Learn to read and explore datasets +- 📊 **Simple Analysis** - Calculate statistics and find patterns - 📈 **Basic Visualization** - Create charts and graphs - 🔬 **Real-World Project** - Complete workflow from start to finish @@ -117,43 +134,42 @@ Each example includes detailed comments explaining every step, making it perfect | Lesson Number | Topic | Lesson Grouping | Learning Objectives | Linked Lesson | Author | | :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: | -| 01 | Defining Data Science | [Introduction](1-Introduction/README.md) | Learn the basic concepts behind data science and its relationship to artificial intelligence, machine learning, and big data. | [lesson](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | -| 02 | Data Science Ethics | [Introduction](1-Introduction/README.md) | Concepts, challenges, and frameworks related to data ethics. | [lesson](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | +| 01 | Defining Data Science | [Introduction](1-Introduction/README.md) | Learn the basic concepts behind data science and how it’s related to artificial intelligence, machine learning, and big data. | [lesson](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | +| 02 | Data Science Ethics | [Introduction](1-Introduction/README.md) | Data Ethics Concepts, Challenges & Frameworks. | [lesson](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | | 03 | Defining Data | [Introduction](1-Introduction/README.md) | How data is classified and its common sources. | [lesson](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) | -| 04 | Introduction to Statistics & Probability | [Introduction](1-Introduction/README.md) | Mathematical techniques of probability and statistics to understand data. | [lesson](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | -| 05 | Working with Relational Data | [Working With Data](2-Working-With-Data/README.md) | Introduction to relational data and the basics of exploring and analyzing relational data using SQL (Structured Query Language). | [lesson](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | -| 06 | Working with NoSQL Data | [Working With Data](2-Working-With-Data/README.md) | Introduction to non-relational data, its various types, and the basics of exploring and analyzing document databases. | [lesson](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique) | -| 07 | Working with Python | [Working With Data](2-Working-With-Data/README.md) | Basics of using Python for data exploration with libraries like Pandas. Foundational knowledge of Python programming is recommended. | [lesson](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | -| 08 | Data Preparation | [Working With Data](2-Working-With-Data/README.md) | Techniques for cleaning and transforming data to address challenges like missing, inaccurate, or incomplete data. | [lesson](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | +| 04 | Introduction to Statistics & Probability | [Introduction](1-Introduction/README.md) | The mathematical techniques of probability and statistics to understand data. | [lesson](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | +| 05 | Working with Relational Data | [Working With Data](2-Working-With-Data/README.md) | Introduction to relational data and the basics of exploring and analyzing relational data with the Structured Query Language, also known as SQL (pronounced “see-quell”). | [lesson](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | +| 06 | Working with NoSQL Data | [Working With Data](2-Working-With-Data/README.md) | Introduction to non-relational data, its various types and the basics of exploring and analyzing document databases. | [lesson](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| +| 07 | Working with Python | [Working With Data](2-Working-With-Data/README.md) | Basics of using Python for data exploration with libraries such as Pandas. Foundational understanding of Python programming is recommended. | [lesson](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | +| 08 | Data Preparation | [Working With Data](2-Working-With-Data/README.md) | Topics on data techniques for cleaning and transforming the data to handle challenges of missing, inaccurate, or incomplete data. | [lesson](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | | 09 | Visualizing Quantities | [Data Visualization](3-Data-Visualization/README.md) | Learn how to use Matplotlib to visualize bird data 🦆 | [lesson](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) | | 10 | Visualizing Distributions of Data | [Data Visualization](3-Data-Visualization/README.md) | Visualizing observations and trends within an interval. | [lesson](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) | | 11 | Visualizing Proportions | [Data Visualization](3-Data-Visualization/README.md) | Visualizing discrete and grouped percentages. | [lesson](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) | -| 12 | Visualizing Relationships | [Data Visualization](3-Data-Visualization/README.md) | Visualizing connections and correlations between data sets and their variables. | [lesson](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) | -| 13 | Meaningful Visualizations | [Data Visualization](3-Data-Visualization/README.md) | Techniques and guidance for creating visualizations that effectively solve problems and provide insights. | [lesson](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) | -| 14 | Introduction to the Data Science lifecycle | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Introduction to the data science lifecycle and its first step: acquiring and extracting data. | [lesson](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | -| 15 | Analyzing | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Techniques for analyzing data during this phase of the data science lifecycle. | [lesson](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | -| 16 | Communication | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Presenting insights from data in a way that decision-makers can easily understand. | [lesson](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | -| 17 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Introduction to data science in the cloud and its benefits. | [lesson](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) and [Maud](https://twitter.com/maudstweets) | -| 18 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Training models using Low Code tools. | [lesson](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) and [Maud](https://twitter.com/maudstweets) | -| 19 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Deploying models with Azure Machine Learning Studio. | [lesson](5-Data-Science-In-Cloud/19-Azure/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) and [Maud](https://twitter.com/maudstweets) | -| 20 | Data Science in the Wild | [In the Wild](6-Data-Science-In-Wild/README.md) | Real-world data science-driven projects. | [lesson](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) | +| 12 | Visualizing Relationships | [Data Visualization](3-Data-Visualization/README.md) | Visualizing connections and correlations between sets of data and their variables. | [lesson](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) | +| 13 | Meaningful Visualizations | [Data Visualization](3-Data-Visualization/README.md) | Techniques and guidance for making your visualizations valuable for effective problem solving and insights. | [lesson](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) | +| 14 | Introduction to the Data Science lifecycle | [Lifecycle](4-Data-Science-Lifecycle/README.md) | Introduction to the data science lifecycle and its first step of acquiring and extracting data. | [lesson](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | +| 15 | Analyzing | [Lifecycle](4-Data-Science-Lifecycle/README.md) | This phase of the data science lifecycle focuses on techniques to analyze data. | [lesson](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | | +| 16 | Communication | [Lifecycle](4-Data-Science-Lifecycle/README.md) | This phase of the data science lifecycle focuses on presenting the insights from the data in a way that makes it easier for decision makers to understand. | [lesson](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | +| 17 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | This series of lessons introduces data science in the cloud and its benefits. | [lesson](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) and [Maud](https://twitter.com/maudstweets) | +| 18 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Training models using Low Code tools. |[lesson](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) and [Maud](https://twitter.com/maudstweets) | +| 19 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Deploying models with Azure Machine Learning Studio. | [lesson](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) and [Maud](https://twitter.com/maudstweets) | +| 20 | Data Science in the Wild | [In the Wild](6-Data-Science-In-Wild/README.md) | Data science driven projects in the real world. | [lesson](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) | ## GitHub Codespaces Follow these steps to open this sample in a Codespace: 1. Click the Code drop-down menu and select the Open with Codespaces option. -2. Select + New codespace at the bottom of the pane. +2. Select + New codespace at the bottom on the pane. For more info, check out the [GitHub documentation](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace). ## VSCode Remote - Containers +Follow these steps to open this repo in a container using your local machine and VSCode using the VS Code Remote - Containers extension: -Follow these steps to open this repo in a container using your local machine and VSCode with the VS Code Remote - Containers extension: +1. If this is your first time using a development container, please ensure your system meets the pre-reqs (i.e. have Docker installed) in [the getting started documentation](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started). -1. If this is your first time using a development container, ensure your system meets the prerequisites (e.g., Docker installed) in [the getting started documentation](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started). +To use this repository, you can either open the repository in an isolated Docker volume: -To use this repository, you can either open it in an isolated Docker volume: - -**Note**: This will use the Remote-Containers: **Clone Repository in Container Volume...** command to clone the source code into a Docker volume instead of the local filesystem. [Volumes](https://docs.docker.com/storage/volumes/) are the preferred method for persisting container data. +**Note**: Under the hood, this will use the Remote-Containers: **Clone Repository in Container Volume...** command to clone the source code in a Docker volume instead of the local filesystem. [Volumes](https://docs.docker.com/storage/volumes/) are the preferred mechanism for persisting container data. Or open a locally cloned or downloaded version of the repository: @@ -163,9 +179,9 @@ Or open a locally cloned or downloaded version of the repository: ## Offline access -You can run this documentation offline using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`. +You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`. -> Note: Notebooks will not be rendered via Docsify, so when you need to run a notebook, do so separately in VS Code using a Python kernel. +> Note, notebooks will not be rendered via Docsify, so when you need to run a notebook, do that separately in VS Code running a Python kernel. ## Other Curricula @@ -182,28 +198,28 @@ Our team produces other curricula! Check out: - [Bash for Beginners](https://github.com/microsoft/bash-for-beginners) - [ML for Beginners](https://aka.ms/ml-beginners) - [Cybersecurity for Beginners](https://github.com/microsoft/Security-101) -- [Web Dev for Beginners](https://aka.ms/webdev-beginners) -- [IoT for Beginners](https://aka.ms/iot-beginners) -- [Machine Learning for Beginners](https://aka.ms/ml-beginners) -- [XR Development for Beginners](https://aka.ms/xr-dev-for-beginners) -- [Mastering GitHub Copilot for AI Paired Programming](https://aka.ms/GitHubCopilotAI) -- [XR Development for Beginners](https://github.com/microsoft/xr-development-for-beginners) -- [Mastering GitHub Copilot for C#/.NET Developers](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) -- [Choose Your Own Copilot Adventure](https://github.com/microsoft/CopilotAdventures) +- [Web Development for Beginners](https://aka.ms/webdev-beginners) +- [IoT for Beginners](https://aka.ms/iot-beginners) +- [Machine Learning for Beginners](https://aka.ms/ml-beginners) +- [XR Development for Beginners](https://aka.ms/xr-dev-for-beginners) +- [Mastering GitHub Copilot for AI Paired Programming](https://aka.ms/GitHubCopilotAI) +- [XR Development for Beginners](https://github.com/microsoft/xr-development-for-beginners) +- [Mastering GitHub Copilot for C#/.NET Developers](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) +- [Choose Your Own Copilot Adventure](https://github.com/microsoft/CopilotAdventures) -## Getting Help +## Getting Help -**Having issues?** Check out our [Troubleshooting Guide](TROUBLESHOOTING.md) for solutions to common problems. +**Having trouble?** Check out our [Troubleshooting Guide](TROUBLESHOOTING.md) for solutions to common issues. -If you're stuck or have questions about building AI applications, join: +If you're stuck or have questions about building AI applications, join: -[![Azure AI Foundry Discord](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) +[![Azure AI Foundry Discord](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) -For product feedback or errors encountered during development, visit: +For product feedback or issues while building, visit: -[![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![Azure AI Foundry Developer Forum](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- **Disclaimer**: -This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be considered the authoritative source. For critical information, professional human translation is recommended. We are not liable for any misunderstandings or misinterpretations arising from the use of this translation. \ No newline at end of file +This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation. \ No newline at end of file diff --git a/translations/es/README.md b/translations/es/README.md index ec29b754..013d1e5e 100644 --- a/translations/es/README.md +++ b/translations/es/README.md @@ -1,19 +1,35 @@ # Ciencia de Datos para Principiantes - Un Currículo -Azure Cloud Advocates en Microsoft se complacen en ofrecer un currículo de 10 semanas y 20 lecciones sobre Ciencia de Datos. Cada lección incluye cuestionarios antes y después de la lección, instrucciones escritas para completar la lección, una solución y una tarea. Nuestra pedagogía basada en proyectos te permite aprender mientras construyes, una forma comprobada para que las nuevas habilidades se afiancen. +[![Abrir en GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) + +[![Licencia de GitHub](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE) +[![Contribuidores de GitHub](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/) +[![Problemas de GitHub](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/) +[![Solicitudes de extracción de GitHub](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/) +[![PRs Bienvenidos](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) + +[![Observadores de GitHub](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/) +[![Forks de GitHub](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/) +[![Estrellas de GitHub](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/) + +[![](https://dcbadge.vercel.app/api/server/ByRwuEEgH4)](https://discord.gg/zxKYvhSnVp?WT.mc_id=academic-000002-leestott) + +[![Foro de Desarrolladores de Azure AI Foundry](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) + +Los Azure Cloud Advocates de Microsoft se complacen en ofrecer un currículo de 10 semanas y 20 lecciones sobre Ciencia de Datos. Cada lección incluye cuestionarios antes y después de la lección, instrucciones escritas para completar la lección, una solución y una tarea. Nuestra pedagogía basada en proyectos te permite aprender mientras construyes, una forma comprobada de que las nuevas habilidades se afiancen. **Un agradecimiento especial a nuestros autores:** [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). -**🙏 Agradecimiento especial 🙏 a nuestros autores, revisores y colaboradores de contenido [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** en particular 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), +**🙏 Agradecimiento especial 🙏 a nuestros [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) autores, revisores y colaboradores de contenido,** entre ellos 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 por @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.es.png)| @@ -22,11 +38,13 @@ Azure Cloud Advocates en Microsoft se complacen en ofrecer un currículo de 10 s ### 🌐 Soporte Multilingüe -#### Soporte mediante GitHub Action (Automatizado y Siempre Actualizado) +#### Disponible a través de GitHub Action (Automatizado y Siempre Actualizado) -[Francés](../fr/README.md) | [Español](./README.md) | [Alemán](../de/README.md) | [Ruso](../ru/README.md) | [Árabe](../ar/README.md) | [Persa (Farsi)](../fa/README.md) | [Urdu](../ur/README.md) | [Chino (Simplificado)](../zh/README.md) | [Chino (Tradicional, Macao)](../mo/README.md) | [Chino (Tradicional, Hong Kong)](../hk/README.md) | [Chino (Tradicional, Taiwán)](../tw/README.md) | [Japonés](../ja/README.md) | [Coreano](../ko/README.md) | [Hindi](../hi/README.md) | [Bengalí](../bn/README.md) | [Maratí](../mr/README.md) | [Nepalí](../ne/README.md) | [Punyabí (Gurmukhi)](../pa/README.md) | [Portugués (Portugal)](../pt/README.md) | [Portugués (Brasil)](../br/README.md) | [Italiano](../it/README.md) | [Polaco](../pl/README.md) | [Turco](../tr/README.md) | [Griego](../el/README.md) | [Tailandés](../th/README.md) | [Sueco](../sv/README.md) | [Danés](../da/README.md) | [Noruego](../no/README.md) | [Finlandés](../fi/README.md) | [Holandés](../nl/README.md) | [Hebreo](../he/README.md) | [Vietnamita](../vi/README.md) | [Indonesio](../id/README.md) | [Malayo](../ms/README.md) | [Tagalo (Filipino)](../tl/README.md) | [Swahili](../sw/README.md) | [Húngaro](../hu/README.md) | [Checo](../cs/README.md) | [Eslovaco](../sk/README.md) | [Rumano](../ro/README.md) | [Búlgaro](../bg/README.md) | [Serbio (Cirílico)](../sr/README.md) | [Croata](../hr/README.md) | [Esloveno](../sl/README.md) | [Ucraniano](../uk/README.md) | [Birmano (Myanmar)](../my/README.md) + +[Árabe](../ar/README.md) | [Bengalí](../bn/README.md) | [Búlgaro](../bg/README.md) | [Birmano (Myanmar)](../my/README.md) | [Chino (Simplificado)](../zh/README.md) | [Chino (Tradicional, Hong Kong)](../hk/README.md) | [Chino (Tradicional, Macao)](../mo/README.md) | [Chino (Tradicional, Taiwán)](../tw/README.md) | [Croata](../hr/README.md) | [Checo](../cs/README.md) | [Danés](../da/README.md) | [Holandés](../nl/README.md) | [Estonio](../et/README.md) | [Finlandés](../fi/README.md) | [Francés](../fr/README.md) | [Alemán](../de/README.md) | [Griego](../el/README.md) | [Hebreo](../he/README.md) | [Hindi](../hi/README.md) | [Húngaro](../hu/README.md) | [Indonesio](../id/README.md) | [Italiano](../it/README.md) | [Japonés](../ja/README.md) | [Coreano](../ko/README.md) | [Lituano](../lt/README.md) | [Malayo](../ms/README.md) | [Maratí](../mr/README.md) | [Nepalí](../ne/README.md) | [Noruego](../no/README.md) | [Persa (Farsi)](../fa/README.md) | [Polaco](../pl/README.md) | [Portugués (Brasil)](../br/README.md) | [Portugués (Portugal)](../pt/README.md) | [Punyabí (Gurmukhi)](../pa/README.md) | [Rumano](../ro/README.md) | [Ruso](../ru/README.md) | [Serbio (Cirílico)](../sr/README.md) | [Eslovaco](../sk/README.md) | [Esloveno](../sl/README.md) | [Español](./README.md) | [Swahili](../sw/README.md) | [Sueco](../sv/README.md) | [Tagalo (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Tailandés](../th/README.md) | [Turco](../tr/README.md) | [Ucraniano](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamita](../vi/README.md) + -**Si deseas que se admitan idiomas adicionales, los idiomas compatibles están listados [aquí](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Si deseas que se admitan idiomas adicionales, consulta la lista [aquí](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** #### Únete a Nuestra Comunidad [![Azure AI Discord](https://dcbadge.limes.pink/api/server/kzRShWzttr)](https://aka.ms/ds4beginners/discord) @@ -39,10 +57,10 @@ Tenemos una serie de aprendizaje con IA en Discord en curso, aprende más y úne Comienza con los siguientes recursos: -- [Página del Hub para Estudiantes](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) En esta página, encontrarás recursos para principiantes, paquetes para estudiantes e incluso formas de obtener un voucher de certificación gratuito. Es una página que querrás marcar y revisar de vez en cuando, ya que cambiamos el contenido al menos mensualmente. +- [Página del Hub para Estudiantes](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) En esta página encontrarás recursos para principiantes, paquetes para estudiantes e incluso formas de obtener un voucher de certificación gratuito. Es una página que querrás marcar y revisar de vez en cuando, ya que cambiamos el contenido al menos mensualmente. - [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Únete a una comunidad global de embajadores estudiantiles, esta podría ser tu puerta de entrada a Microsoft. -# Cómo Empezar +# Comenzando ## 📚 Documentación @@ -53,13 +71,13 @@ Comienza con los siguientes recursos: - **[Para Profesores](for-teachers.md)** - Orientación para la enseñanza y recursos para el aula ## 👨‍🎓 Para Estudiantes -> **Principiantes Completos**: ¿Nuevo en ciencia de datos? Comienza con nuestros [ejemplos para principiantes](examples/README.md)! Estos ejemplos simples y bien comentados te ayudarán a entender los conceptos básicos antes de sumergirte en el currículo completo. -> **[Estudiantes](https://aka.ms/student-page)**: para usar este currículo por tu cuenta, haz un fork del repositorio completo y completa los ejercicios por tu cuenta, comenzando con un cuestionario previo a la lección. Luego, lee la lección y completa el resto de las actividades. Intenta crear los proyectos comprendiendo las lecciones en lugar de copiar el código de solución; sin embargo, ese código está disponible en las carpetas /solutions en cada lección orientada a proyectos. Otra idea sería formar un grupo de estudio con amigos y revisar el contenido juntos. Para un estudio adicional, recomendamos [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). +> **Principiantes Completos**: ¿Nuevo en ciencia de datos? Comienza con nuestros [ejemplos para principiantes](examples/README.md). Estos ejemplos simples y bien comentados te ayudarán a entender los conceptos básicos antes de sumergirte en el currículo completo. +> **[Estudiantes](https://aka.ms/student-page)**: para usar este currículo por tu cuenta, haz un fork del repositorio completo y completa los ejercicios por tu cuenta, comenzando con un cuestionario previo a la lección. Luego, lee la lección y completa el resto de las actividades. Intenta crear los proyectos comprendiendo las lecciones en lugar de copiar el código de solución; sin embargo, ese código está disponible en las carpetas /solutions en cada lección orientada a proyectos. Otra idea sería formar un grupo de estudio con amigos y revisar el contenido juntos. Para un estudio más profundo, recomendamos [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum). **Inicio Rápido:** -1. Revisa la [Guía de Instalación](INSTALLATION.md) para configurar tu entorno -2. Consulta la [Guía de Uso](USAGE.md) para aprender cómo trabajar con el currículo -3. Comienza con la Lección 1 y avanza de manera secuencial +1. Consulta la [Guía de Instalación](INSTALLATION.md) para configurar tu entorno +2. Revisa la [Guía de Uso](USAGE.md) para aprender cómo trabajar con el currículo +3. Comienza con la Lección 1 y avanza de forma secuencial 4. Únete a nuestra [comunidad en Discord](https://aka.ms/ds4beginners/discord) para obtener apoyo ## 👩‍🏫 Para Profesores @@ -75,11 +93,11 @@ Comienza con los siguientes recursos: > 🎥 Haz clic en la imagen de arriba para ver un video sobre el proyecto y las personas que lo crearon. ## Pedagogía +Hemos elegido dos principios pedagógicos al desarrollar este plan de estudios: asegurarnos de que sea basado en proyectos y que incluya cuestionarios frecuentes. Al final de esta serie, los estudiantes habrán aprendido los principios básicos de la ciencia de datos, incluyendo conceptos éticos, preparación de datos, diferentes formas de trabajar con datos, visualización de datos, análisis de datos, casos de uso reales de la ciencia de datos y más. -Hemos elegido dos principios pedagógicos al construir este currículo: asegurarnos de que sea basado en proyectos y que incluya cuestionarios frecuentes. Al final de esta serie, los estudiantes habrán aprendido los principios básicos de la ciencia de datos, incluidos conceptos éticos, preparación de datos, diferentes formas de trabajar con datos, visualización de datos, análisis de datos, casos de uso reales de la ciencia de datos y más. -Además, un cuestionario de bajo nivel antes de una clase establece la intención del estudiante hacia el aprendizaje de un tema, mientras que un segundo cuestionario después de la clase asegura una mayor retención. Este plan de estudios fue diseñado para ser flexible y divertido, y puede completarse en su totalidad o en partes. Los proyectos comienzan pequeños y se vuelven cada vez más complejos al final del ciclo de 10 semanas. +Además, un cuestionario de baja presión antes de una clase establece la intención del estudiante hacia el aprendizaje de un tema, mientras que un segundo cuestionario después de la clase asegura una mayor retención. Este plan de estudios fue diseñado para ser flexible y divertido, y puede tomarse en su totalidad o en partes. Los proyectos comienzan pequeños y se vuelven cada vez más complejos al final del ciclo de 10 semanas. -> Encuentra nuestro [Código de Conducta](CODE_OF_CONDUCT.md), [Guía para Contribuir](CONTRIBUTING.md), [Guía de Traducción](TRANSLATIONS.md). ¡Agradecemos tus comentarios constructivos! +> Encuentra nuestro [Código de Conducta](CODE_OF_CONDUCT.md), [Contribuciones](CONTRIBUTING.md), [Directrices de Traducción](TRANSLATIONS.md). ¡Agradecemos tus comentarios constructivos! ## Cada lección incluye: @@ -116,13 +134,13 @@ Cada ejemplo incluye comentarios detallados que explican cada paso, ¡perfecto p |:---:| | Ciencia de Datos para Principiantes: Hoja de Ruta - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | -| Número de Lección | Tema | Agrupación de Lección | Objetivos de Aprendizaje | Lección Vinculada | Autor | +| Número de Lección | Tema | Agrupación de Lecciones | Objetivos de Aprendizaje | Lección Vinculada | Autor | | :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: | | 01 | Definiendo Ciencia de Datos | [Introducción](1-Introduction/README.md) | Aprende los conceptos básicos detrás de la ciencia de datos y cómo se relaciona con la inteligencia artificial, el aprendizaje automático y los grandes datos. | [lección](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | | 02 | Ética en Ciencia de Datos | [Introducción](1-Introduction/README.md) | Conceptos, desafíos y marcos de ética en datos. | [lección](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | | 03 | Definiendo Datos | [Introducción](1-Introduction/README.md) | Cómo se clasifican los datos y sus fuentes comunes. | [lección](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) | | 04 | Introducción a Estadística y Probabilidad | [Introducción](1-Introduction/README.md) | Las técnicas matemáticas de probabilidad y estadística para entender los datos. | [lección](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | -| 05 | Trabajando con Datos Relacionales | [Trabajando con Datos](2-Working-With-Data/README.md) | Introducción a los datos relacionales y los conceptos básicos de exploración y análisis de datos relacionales con el Lenguaje de Consulta Estructurada, también conocido como SQL (pronunciado “sequel”). | [lección](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | +| 05 | Trabajando con Datos Relacionales | [Trabajando con Datos](2-Working-With-Data/README.md) | Introducción a los datos relacionales y los conceptos básicos de exploración y análisis de datos relacionales con el Lenguaje de Consulta Estructurado, también conocido como SQL (pronunciado “see-quell”). | [lección](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | | 06 | Trabajando con Datos NoSQL | [Trabajando con Datos](2-Working-With-Data/README.md) | Introducción a los datos no relacionales, sus diversos tipos y los conceptos básicos de exploración y análisis de bases de datos de documentos. | [lección](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| | 07 | Trabajando con Python | [Trabajando con Datos](2-Working-With-Data/README.md) | Conceptos básicos de uso de Python para la exploración de datos con bibliotecas como Pandas. Se recomienda una comprensión fundamental de la programación en Python. | [lección](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | | 08 | Preparación de Datos | [Trabajando con Datos](2-Working-With-Data/README.md) | Temas sobre técnicas de datos para limpiar y transformar los datos para manejar desafíos de datos faltantes, inexactos o incompletos. | [lección](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | @@ -131,9 +149,9 @@ Cada ejemplo incluye comentarios detallados que explican cada paso, ¡perfecto p | 11 | Visualizando Proporciones | [Visualización de Datos](3-Data-Visualization/README.md) | Visualizando porcentajes discretos y agrupados. | [lección](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) | | 12 | Visualizando Relaciones | [Visualización de Datos](3-Data-Visualization/README.md) | Visualizando conexiones y correlaciones entre conjuntos de datos y sus variables. | [lección](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) | | 13 | Visualizaciones Significativas | [Visualización de Datos](3-Data-Visualization/README.md) | Técnicas y orientación para hacer que tus visualizaciones sean valiosas para resolver problemas de manera efectiva y obtener ideas. | [lección](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) | -| 14 | Introducción al Ciclo de Vida de Ciencia de Datos | [Ciclo de Vida](4-Data-Science-Lifecycle/README.md) | Introducción al ciclo de vida de la ciencia de datos y su primer paso de adquirir y extraer datos. | [lección](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | +| 14 | Introducción al Ciclo de Vida de la Ciencia de Datos | [Ciclo de Vida](4-Data-Science-Lifecycle/README.md) | Introducción al ciclo de vida de la ciencia de datos y su primer paso de adquisición y extracción de datos. | [lección](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | | 15 | Analizando | [Ciclo de Vida](4-Data-Science-Lifecycle/README.md) | Esta fase del ciclo de vida de la ciencia de datos se centra en técnicas para analizar datos. | [lección](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | | -| 16 | Comunicación | [Ciclo de Vida](4-Data-Science-Lifecycle/README.md) | Esta fase del ciclo de vida de la ciencia de datos se centra en presentar los hallazgos de los datos de manera que facilite la comprensión para los tomadores de decisiones. | [lección](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | +| 16 | Comunicación | [Ciclo de Vida](4-Data-Science-Lifecycle/README.md) | Esta fase del ciclo de vida de la ciencia de datos se centra en presentar los conocimientos obtenidos de los datos de manera que sea más fácil para los tomadores de decisiones entenderlos. | [lección](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | | 17 | Ciencia de Datos en la Nube | [Datos en la Nube](5-Data-Science-In-Cloud/README.md) | Esta serie de lecciones introduce la ciencia de datos en la nube y sus beneficios. | [lección](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) y [Maud](https://twitter.com/maudstweets) | | 18 | Ciencia de Datos en la Nube | [Datos en la Nube](5-Data-Science-In-Cloud/README.md) | Entrenamiento de modelos usando herramientas de bajo código. |[lección](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) y [Maud](https://twitter.com/maudstweets) | | 19 | Ciencia de Datos en la Nube | [Datos en la Nube](5-Data-Science-In-Cloud/README.md) | Despliegue de modelos con Azure Machine Learning Studio. | [lección](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) y [Maud](https://twitter.com/maudstweets) | @@ -149,17 +167,17 @@ Para más información, consulta la [documentación de GitHub](https://docs.gith ## VSCode Remote - Containers Sigue estos pasos para abrir este repositorio en un contenedor usando tu máquina local y VSCode con la extensión VS Code Remote - Containers: -1. Si es la primera vez que usas un contenedor de desarrollo, asegúrate de que tu sistema cumpla con los requisitos previos (por ejemplo, tener Docker instalado) en [la documentación de introducción](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started). +1. Si es la primera vez que usas un contenedor de desarrollo, asegúrate de que tu sistema cumple con los requisitos previos (es decir, tener Docker instalado) en [la documentación de introducción](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started). Para usar este repositorio, puedes abrirlo en un volumen aislado de Docker: -**Nota**: En segundo plano, esto usará el comando Remote-Containers: **Clone Repository in Container Volume...** para clonar el código fuente en un volumen de Docker en lugar del sistema de archivos local. [Volúmenes](https://docs.docker.com/storage/volumes/) son el mecanismo preferido para persistir datos de contenedores. +**Nota**: En segundo plano, esto usará el comando Remote-Containers: **Clone Repository in Container Volume...** para clonar el código fuente en un volumen de Docker en lugar del sistema de archivos local. [Los volúmenes](https://docs.docker.com/storage/volumes/) son el mecanismo preferido para persistir datos de contenedores. O abrir una versión clonada o descargada localmente del repositorio: - Clona este repositorio en tu sistema de archivos local. - Presiona F1 y selecciona el comando **Remote-Containers: Open Folder in Container...**. -- Selecciona la copia clonada de esta carpeta, espera a que el contenedor inicie y prueba las cosas. +- Selecciona la copia clonada de esta carpeta, espera a que el contenedor se inicie y prueba las cosas. ## Acceso sin conexión @@ -182,28 +200,28 @@ Puedes ejecutar esta documentación sin conexión usando [Docsify](https://docsi - [Bash para Principiantes](https://github.com/microsoft/bash-for-beginners) - [ML para Principiantes](https://aka.ms/ml-beginners) - [Ciberseguridad para Principiantes](https://github.com/microsoft/Security-101) -- [Desarrollo Web para Principiantes](https://aka.ms/webdev-beginners) -- [IoT para Principiantes](https://aka.ms/iot-beginners) -- [Aprendizaje Automático para Principiantes](https://aka.ms/ml-beginners) -- [Desarrollo XR para Principiantes](https://aka.ms/xr-dev-for-beginners) -- [Dominando GitHub Copilot para programación en pareja con IA](https://aka.ms/GitHubCopilotAI) -- [Desarrollo XR para principiantes](https://github.com/microsoft/xr-development-for-beginners) -- [Dominando GitHub Copilot para desarrolladores de C#/.NET](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) -- [Elige tu propia aventura con Copilot](https://github.com/microsoft/CopilotAdventures) +- [Desarrollo Web para Principiantes](https://aka.ms/webdev-beginners) +- [IoT para Principiantes](https://aka.ms/iot-beginners) +- [Aprendizaje Automático para Principiantes](https://aka.ms/ml-beginners) +- [Desarrollo XR para Principiantes](https://aka.ms/xr-dev-for-beginners) +- [Dominando GitHub Copilot para Programación en Pareja con IA](https://aka.ms/GitHubCopilotAI) +- [Desarrollo XR para Principiantes](https://github.com/microsoft/xr-development-for-beginners) +- [Dominando GitHub Copilot para Desarrolladores de C#/.NET](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers) +- [Elige Tu Propia Aventura con Copilot](https://github.com/microsoft/CopilotAdventures) -## Obtener ayuda +## Obtener Ayuda -**¿Tienes problemas?** Consulta nuestra [Guía de solución de problemas](TROUBLESHOOTING.md) para encontrar soluciones a problemas comunes. +**¿Tienes problemas?** Consulta nuestra [Guía de Solución de Problemas](TROUBLESHOOTING.md) para encontrar soluciones a problemas comunes. -Si te quedas atascado o tienes preguntas sobre cómo construir aplicaciones de IA, únete a: +Si te quedas atascado o tienes preguntas sobre cómo construir aplicaciones de IA, únete a: -[![Discord de Azure AI Foundry](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) +[![Discord de Azure AI Foundry](https://img.shields.io/badge/Discord-Azure_AI_Foundry_Community_Discord-blue?style=for-the-badge&logo=discord&color=5865f2&logoColor=fff)](https://aka.ms/foundry/discord) -Si tienes comentarios sobre el producto o errores mientras desarrollas, visita: +Si tienes comentarios sobre el producto o encuentras errores mientras desarrollas, visita: -[![Foro de desarrolladores de Azure AI Foundry](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![Foro de Desarrolladores de Azure AI Foundry](https://img.shields.io/badge/GitHub-Azure_AI_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- **Descargo de responsabilidad**: -Este documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por garantizar la precisión, tenga en cuenta que las traducciones automáticas pueden contener errores o imprecisiones. El documento original en su idioma nativo debe considerarse la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos hacemos responsables de malentendidos o interpretaciones erróneas que puedan surgir del uso de esta traducción. \ No newline at end of file +Este documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por garantizar la precisión, tenga en cuenta que las traducciones automáticas pueden contener errores o imprecisiones. El documento original en su idioma nativo debe considerarse como la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos hacemos responsables de malentendidos o interpretaciones erróneas que puedan surgir del uso de esta traducción. \ No newline at end of file diff --git a/translations/et/1-Introduction/01-defining-data-science/README.md b/translations/et/1-Introduction/01-defining-data-science/README.md new file mode 100644 index 00000000..3c9def35 --- /dev/null +++ b/translations/et/1-Introduction/01-defining-data-science/README.md @@ -0,0 +1,176 @@ + +# Andmeteaduse määratlemine + +| ![ Sketchnote autorilt [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/01-Definitions.png) | +| :----------------------------------------------------------------------------------------------------------: | +| Andmeteaduse määratlemine - _Sketchnote autorilt [@nitya](https://twitter.com/nitya)_ | + +--- + +[![Andmeteaduse määratlemise video](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.et.png)](https://youtu.be/beZ7Mb_oz9I) + +## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ds/quiz/0) + +## Mis on andmed? +Meie igapäevaelus ümbritsevad meid pidevalt andmed. Tekst, mida sa praegu loed, on andmed. Sõprade telefoninumbrite loetelu sinu nutitelefonis on andmed, samuti kellaaeg, mis on näidatud sinu kellal. Inimestena töötleme andmeid loomulikult, näiteks raha lugedes või kirju kirjutades. + +Kuid andmed muutusid palju olulisemaks arvutite loomisega. Arvutite peamine ülesanne on teha arvutusi, kuid nad vajavad selleks andmeid. Seega peame mõistma, kuidas arvutid andmeid salvestavad ja töötlevad. + +Interneti tekkimisega suurenes arvutite roll andmete käsitlemise seadmetena. Kui järele mõelda, siis kasutame arvuteid üha enam andmete töötlemiseks ja suhtlemiseks, mitte niivõrd arvutuste tegemiseks. Kui kirjutame sõbrale e-kirja või otsime internetist teavet, siis loome, salvestame, edastame ja manipuleerime andmetega. +> Kas sa mäletad, millal viimati kasutasid arvutit tegelikult millegi arvutamiseks? + +## Mis on andmeteadus? + +[Wikipedia](https://en.wikipedia.org/wiki/Data_science) määratleb **andmeteaduse** kui *teadusvaldkonna, mis kasutab teaduslikke meetodeid, et saada teadmisi ja arusaamu struktureeritud ja struktureerimata andmetest ning rakendada neid teadmisi ja praktilisi järeldusi erinevates rakendusvaldkondades*. + +See määratlus toob esile järgmised olulised aspektid andmeteaduses: + +* Andmeteaduse peamine eesmärk on **teadmiste hankimine** andmetest, teisisõnu - **andmete mõistmine**, varjatud seoste leidmine ja **mudeli** loomine. +* Andmeteadus kasutab **teaduslikke meetodeid**, nagu tõenäosus ja statistika. Tegelikult, kui termin *andmeteadus* esmakordselt kasutusele võeti, väitsid mõned, et see on lihtsalt uus moodne nimi statistikale. Tänapäeval on selge, et valdkond on palju laiem. +* Saadud teadmisi tuleks rakendada, et luua **praktilisi järeldusi**, st rakendatavaid teadmisi, mida saab kasutada reaalsetes ärisituatsioonides. +* Me peaksime suutma töötada nii **struktureeritud** kui ka **struktureerimata** andmetega. Räägime hiljem kursuse käigus erinevatest andmetüüpidest. +* **Rakendusvaldkond** on oluline mõiste, ja andmeteadlased vajavad sageli vähemalt mingil määral teadmisi probleemivaldkonnas, näiteks: rahandus, meditsiin, turundus jne. + +> Teine oluline aspekt andmeteaduses on see, et see uurib, kuidas andmeid saab koguda, salvestada ja töödelda arvutite abil. Kuigi statistika annab meile matemaatilised alused, rakendab andmeteadus matemaatilisi kontseptsioone, et tegelikult andmetest järeldusi teha. + +Üks viis (omistatud [Jim Grayle](https://en.wikipedia.org/wiki/Jim_Gray_(computer_scientist))) andmeteaduse vaatamiseks on pidada seda eraldi teadusparadigmaks: +* **Empiiriline**, kus tuginetakse peamiselt vaatluste ja katsete tulemustele +* **Teoreetiline**, kus uued kontseptsioonid tekivad olemasolevatest teadmistest +* **Arvutuslik**, kus avastatakse uusi põhimõtteid arvutuseksperimentide põhjal +* **Andmepõhine**, mis põhineb andmetes olevate seoste ja mustrite avastamisel + +## Seotud valdkonnad + +Kuna andmed on kõikjal, on ka andmeteadus lai valdkond, mis puudutab paljusid teisi distsipliine. + +
+
Andmebaasid
+
+Oluline on **kuidas andmeid salvestada**, st kuidas neid struktureerida nii, et töötlemine oleks kiirem. On erinevat tüüpi andmebaase, mis salvestavad struktureeritud ja struktureerimata andmeid, mida käsitleme oma kursusel. +
+
Suured andmed
+
+Sageli peame salvestama ja töötlema väga suuri andmehulkasid suhteliselt lihtsa struktuuriga. Selleks on olemas spetsiaalsed lähenemisviisid ja tööriistad, mis võimaldavad andmeid hajutatult salvestada arvutite klastris ja neid tõhusalt töödelda. +
+
Masinõpe
+
+Üks viis andmete mõistmiseks on **luua mudel**, mis suudab ennustada soovitud tulemust. Mudelite loomist andmetest nimetatakse **masinõppeks**. Võid tutvuda meie Masinõppe algajatele õppekavaga, et sellest rohkem teada saada. +
+
Tehisintellekt
+
+Masinõppe valdkond, mida tuntakse tehisintellektina (AI), tugineb samuti andmetele ja hõlmab keerukate mudelite loomist, mis jäljendavad inimeste mõtteprotsesse. AI meetodid võimaldavad sageli struktureerimata andmeid (nt loomulik keel) muuta struktureeritud järeldusteks. +
+
Visualiseerimine
+
+Suured andmehulgad on inimesele arusaamatud, kuid kui loome kasulikke visualiseeringuid, saame andmetest paremini aru ja teha järeldusi. Seega on oluline teada mitmeid viise teabe visualiseerimiseks - midagi, mida käsitleme kursuse 3. osas. Seotud valdkonnad hõlmavad ka **infograafikat** ja **inimese-arvuti interaktsiooni** üldiselt. +
+
+ +## Andmetüübid + +Nagu juba mainitud, on andmed kõikjal. Me lihtsalt peame need õigesti kinni püüdma! Kasulik on eristada **struktureeritud** ja **struktureerimata** andmeid. Esimesed on tavaliselt esitatud hästi struktureeritud kujul, sageli tabelina või tabelite kogumina, samas kui viimased on lihtsalt failide kogum. Mõnikord räägitakse ka **poolstruktureeritud** andmetest, millel on mingi struktuur, mis võib oluliselt varieeruda. + +| Struktureeritud | Poolstruktureeritud | Struktureerimata | +| ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | --------------------------------------- | +| Inimeste nimekiri koos nende telefoninumbritega | Wikipedia leheküljed koos linkidega | Encyclopedia Britannica tekst | +| Temperatuur kõigis hoone ruumides iga minut viimase 20 aasta jooksul | Teadusartiklite kogum JSON-formaadis koos autorite, avaldamiskuupäeva ja abstraktiga | Ettevõtte dokumentide failikogu | +| Andmed hoonesse sisenevate inimeste vanuse ja soo kohta | Interneti leheküljed | Toores videovoog turvakaamerast | + +## Kust saada andmeid + +Andmeid on võimalik saada paljudest allikatest, ja kõiki neid oleks võimatu loetleda! Siiski mainime mõningaid tüüpilisi kohti, kust andmeid saab: + +* **Struktureeritud** + - **Asjade internet** (IoT), sealhulgas andmed erinevatest sensoritest, nagu temperatuuri- või rõhuandurid, pakub palju kasulikke andmeid. Näiteks, kui kontorihoone on varustatud IoT sensoritega, saame automaatselt kontrollida kütet ja valgustust, et minimeerida kulusid. + - **Küsitlused**, mida palume kasutajatel täita pärast ostu või veebisaidi külastamist. + - **Käitumise analüüs** võib näiteks aidata meil mõista, kui sügavale kasutaja veebisaidil läheb ja mis on tüüpiline põhjus saidilt lahkumiseks. +* **Struktureerimata** + - **Tekstid** võivad olla rikkalikud allikad järelduste jaoks, näiteks üldine **meeleolu skoor** või märksõnade ja semantilise tähenduse eraldamine. + - **Pildid** või **videod**. Videoturvakaamera salvestust saab kasutada liikluse hindamiseks teel ja inimeste teavitamiseks võimalikest ummikutest. + - Veebiserveri **logid** võivad aidata mõista, milliseid lehti meie saidil kõige sagedamini külastatakse ja kui kaua. +* Poolstruktureeritud + - **Sotsiaalvõrgustike** graafikud võivad olla suurepärased andmeallikad kasutajate isiksuste ja potentsiaalse tõhususe kohta teabe levitamisel. + - Kui meil on peo fotode kogum, saame proovida eraldada **grupidünaamika** andmeid, luues graafiku inimestest, kes teevad üksteisega pilte. + +Teades erinevaid võimalikke andmeallikaid, võid proovida mõelda erinevatele stsenaariumidele, kus andmeteaduse tehnikaid saab rakendada olukorra paremaks mõistmiseks ja äriprotsesside parandamiseks. + +## Mida saab andmetega teha + +Andmeteaduses keskendume järgmistele andmete teekonna etappidele: + +
+
1) Andmete hankimine
+
+Esimene samm on andmete kogumine. Kuigi paljudel juhtudel võib see olla lihtne protsess, näiteks andmed, mis jõuavad andmebaasi veebirakendusest, vajame mõnikord spetsiaalseid tehnikaid. Näiteks IoT sensorite andmed võivad olla ülekaalukad, ja hea tava on kasutada puhverduspunkte, nagu IoT Hub, et koguda kõik andmed enne edasist töötlemist. +
+
2) Andmete salvestamine
+
+Andmete salvestamine võib olla keeruline, eriti kui räägime suurtest andmehulkadest. Otsustades, kuidas andmeid salvestada, on mõistlik ette näha, kuidas soovid tulevikus andmeid pärida. Andmeid saab salvestada mitmel viisil: + +
+
3) Andmete töötlemine
+
+See on andmete teekonna kõige põnevam osa, mis hõlmab andmete teisendamist algsest vormist vormi, mida saab kasutada visualiseerimiseks või mudeli treenimiseks. Kui töötleme struktureerimata andmeid, nagu tekst või pildid, võime vajada AI tehnikaid, et eraldada **omadusi** andmetest, muutes need struktureeritud vormiks. +
+
4) Visualiseerimine / Inimeste järeldused
+
+Sageli, et andmetest aru saada, peame neid visualiseerima. Omades mitmeid erinevaid visualiseerimistehnikaid oma tööriistakastis, saame leida õige vaate, et teha järeldusi. Sageli peab andmeteadlane "mängima andmetega", visualiseerides neid mitu korda ja otsides seoseid. Samuti võime kasutada statistilisi tehnikaid, et testida hüpoteese või tõestada korrelatsiooni erinevate andmeosade vahel. +
+
5) Ennustava mudeli treenimine
+
+Kuna andmeteaduse lõppeesmärk on teha otsuseid andmete põhjal, võime kasutada masinõppe tehnikaid, et luua ennustav mudel. Seda mudelit saame kasutada ennustuste tegemiseks uute sarnase struktuuriga andmekogumite põhjal. +
+
+ +Muidugi, sõltuvalt tegelikest andmetest, võivad mõned sammud puududa (nt kui andmed on juba andmebaasis või kui mudeli treenimist pole vaja), või mõned sammud võivad olla mitu korda korduvad (näiteks andmete töötlemine). + +## Digitaliseerimine ja digitaalne transformatsioon + +Viimase kümnendi jooksul on paljud ettevõtted hakanud mõistma andmete tähtsust äriliste otsuste tegemisel. Andmeteaduse põhimõtete rakendamiseks ettevõtte juhtimisel tuleb esmalt koguda andmeid, st tõlkida äriprotsessid digitaalsesse vormi. Seda nimetatakse **digitaliseerimiseks**. Andmeteaduse tehnikate rakendamine nendele andmetele otsuste suunamiseks võib viia märkimisväärse tootlikkuse kasvuni (või isegi ärilise pöördeni), mida nimetatakse **digitaalseks transformatsiooniks**. + +Vaatame ühte näidet. Oletame, et meil on andmeteaduse kursus (nagu see), mida pakume veebis tudengitele, ja tahame kasutada andmeteadust selle parandamiseks. Kuidas seda teha? + +Võime alustada küsimusega "Mida saab digitaliseerida?" Lihtsaim viis oleks mõõta aega, mis kulub igal tudengil iga mooduli läbimiseks, ja mõõta omandatud teadmisi, andes iga mooduli lõpus valikvastustega testi. Arvutades keskmise läbimisaja kõigi tudengite seas, saame teada, millised moodulid põhjustavad tudengitele kõige rohkem raskusi, ja töötada nende lihtsustamise kallal. +> Võib väita, et see lähenemine pole ideaalne, kuna moodulid võivad olla erineva pikkusega. Tõenäoliselt oleks õiglasem jagada aeg mooduli pikkusega (tähemärkide arvus) ja võrrelda neid väärtusi. + +Kui hakkame analüüsima valikvastustega testide tulemusi, saame proovida kindlaks teha, milliseid kontseptsioone õpilastel on raske mõista, ja kasutada seda teavet sisu parandamiseks. Selleks peame kujundama testid nii, et iga küsimus seostuks kindla kontseptsiooni või teadmiste osaga. + +Kui tahame minna veelgi keerukamaks, saame joonistada graafiku, kus on näidatud iga mooduli läbimiseks kulunud aeg vastavalt õpilaste vanusekategooriale. Võime avastada, et mõne vanusekategooria puhul võtab mooduli läbimine ebamõistlikult kaua aega või et õpilased loobuvad enne selle lõpetamist. See võib aidata meil anda mooduli jaoks vanusesoovitusi ja vähendada inimeste rahulolematust valede ootuste tõttu. + +## 🚀 Väljakutse + +Selles väljakutses proovime leida andmeteadusega seotud kontseptsioone, uurides tekste. Võtame Wikipedia artikli andmeteaduse kohta, laadime alla ja töötleme teksti ning loome sõnapilve, mis näeb välja selline: + +![Sõnapilv andmeteaduse kohta](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.et.png) + +Külastage [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore'), et koodi läbi vaadata. Samuti saate koodi käivitada ja näha, kuidas see teostab kõiki andmetransformatsioone reaalajas. + +> Kui te ei tea, kuidas Jupyter Notebookis koodi käivitada, vaadake [seda artiklit](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). + +## [Loengu järgne viktoriin](https://ff-quizzes.netlify.app/en/ds/quiz/1) + +## Ülesanded + +* **Ülesanne 1**: Muutke ülaltoodud koodi, et leida seotud kontseptsioone **Big Data** ja **Machine Learning** valdkondade jaoks. +* **Ülesanne 2**: [Mõelge andmeteaduse stsenaariumidele](assignment.md) + +## Autorid + +Selle õppetunni on koostanud ♥️ [Dmitry Soshnikov](http://soshnikov.com) + +--- + +**Lahtiütlus**: +See dokument on tõlgitud AI tõlketeenuse [Co-op Translator](https://github.com/Azure/co-op-translator) abil. Kuigi püüame tagada täpsust, palume arvestada, et automaatsed tõlked võivad sisaldada vigu või ebatäpsusi. Algne dokument selle algses keeles tuleks pidada autoriteetseks allikaks. Olulise teabe puhul soovitame kasutada professionaalset inimtõlget. Me ei vastuta selle tõlke kasutamisest tulenevate arusaamatuste või valesti tõlgenduste eest. \ No newline at end of file diff --git a/translations/et/1-Introduction/01-defining-data-science/assignment.md b/translations/et/1-Introduction/01-defining-data-science/assignment.md new file mode 100644 index 00000000..527abb05 --- /dev/null +++ b/translations/et/1-Introduction/01-defining-data-science/assignment.md @@ -0,0 +1,46 @@ + +# 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: + +1. Milliseid andmeid saab koguda? +1. Kuidas te neid koguksite? +1. Kuidas te andmeid salvestaksite? Kui suured need andmed tõenäoliselt on? +1. Milliseid teadmisi võiks nendest andmetest saada? Milliseid otsuseid saaksime andmete põhjal teha? + +Proovige mõelda kolmele erinevale probleemile/protsessile ja kirjeldage iga punkti ülaltoodud küsimuste kohta iga valdkonna jaoks. + +Siin on mõned probleemivaldkonnad ja probleemid, mis aitavad teil mõtteid käivitada: + +1. Kuidas saaks andmeid kasutada laste haridusprotsessi parandamiseks koolides? +1. Kuidas saaks andmeid kasutada vaktsineerimise kontrollimiseks pandeemia ajal? +1. Kuidas saaks andmeid kasutada selleks, et tagada tööalane produktiivsus? + +## Juhised + +Täitke järgmine tabel (asendage vajadusel soovitatud probleemivaldkonnad enda omadega): + +| Probleemivaldkond | Probleem | Milliseid andmeid koguda | Kuidas andmeid salvestada | Milliseid teadmisi/otsuseid saame teha | +|-------------------|----------|--------------------------|---------------------------|----------------------------------------| +| Haridus | | | | | +| Vaktsineerimine | | | | | +| Produktiivsus | | | | | + +## Hindamiskriteeriumid + +Näidiskvaliteet | Piisav | Vajab parandamist +--- | --- | -- | +On suudetud tuvastada mõistlikud andmeallikad, andmete salvestamise viisid ja võimalikud otsused/teadmised kõigi probleemivaldkondade jaoks | Mõned lahenduse aspektid ei ole üksikasjalikud, andmete salvestamist ei ole arutatud, vähemalt 2 probleemivaldkonda on kirjeldatud | Kirjeldatud on ainult osa andmelahendusest, käsitletud on ainult ühte probleemivaldkonda. + +--- + +**Lahtiütlus**: +See dokument on tõlgitud AI tõlketeenuse [Co-op Translator](https://github.com/Azure/co-op-translator) abil. Kuigi püüame tagada täpsust, palume arvestada, et automaatsed tõlked võivad sisaldada vigu või ebatäpsusi. Algne dokument selle algses keeles tuleks pidada autoriteetseks allikaks. Olulise teabe puhul soovitame kasutada professionaalset inimtõlget. Me ei vastuta selle tõlke kasutamisest tulenevate arusaamatuste või valesti tõlgenduste eest. \ No newline at end of file diff --git a/translations/et/1-Introduction/01-defining-data-science/notebook.ipynb b/translations/et/1-Introduction/01-defining-data-science/notebook.ipynb new file mode 100644 index 00000000..4248f4d2 --- /dev/null +++ b/translations/et/1-Introduction/01-defining-data-science/notebook.ipynb @@ -0,0 +1,431 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Väljakutse: Teksti analüüs andmeteaduse kohta\n", + "\n", + "Selles näites teeme lihtsa harjutuse, mis hõlmab kõiki traditsioonilise andmeteaduse protsessi samme. Koodi kirjutamine pole vajalik, saate lihtsalt allolevaid lahtrid klõpsata, et neid käivitada ja tulemust jälgida. Väljakutsena julgustatakse teid proovima seda koodi erinevate andmetega.\n", + "\n", + "## Eesmärk\n", + "\n", + "Selles õppetunnis oleme arutanud erinevaid andmeteadusega seotud mõisteid. Proovime avastada rohkem seotud mõisteid, tehes **teksti kaevandamist**. Alustame andmeteaduse teemalisest tekstist, eraldame sellest märksõnad ja proovime seejärel tulemust visualiseerida.\n", + "\n", + "Tekstina kasutan Wikipedia lehte andmeteaduse kohta:\n" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 62, + "source": [ + "url = 'https://en.wikipedia.org/wiki/Data_science'" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## Samm 1: Andmete hankimine\n", + "\n", + "Iga andmeteaduse protsessi esimene samm on andmete hankimine. Selleks kasutame `requests` teeki:\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 63, + "source": [ + "import requests\r\n", + "\r\n", + "text = requests.get(url).content.decode('utf-8')\r\n", + "print(text[:1000])" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\n", + "\n", + "Data science - Wikipedia\n", + "