Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/ElizaLo/Data-Science

Projects and awesome list for all Data Science fields
https://github.com/ElizaLo/Data-Science
awesome awesome-data-science awesome-interview-preparation awesome-list awesome-lists awesome-ml data-analysis data-analyst data-analytics data-science data-scientist interview interview-preparation-resources machine-learning udacity udemy
Last synced: about 1 month ago
JSON representation
Projects and awesome list for all Data Science fields
Host: GitHub
URL: https://github.com/ElizaLo/Data-Science
Owner: ElizaLo
License: mit
Created: 2020-05-21T16:57:45.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2023-10-05T16:07:39.000Z (12 months ago)
Last Synced: 2024-08-09T03:09:02.922Z (about 1 month ago)
Topics: awesome, awesome-data-science, awesome-interview-preparation, awesome-list, awesome-lists, awesome-ml, data-analysis, data-analyst, data-analytics, data-science, data-scientist, interview, interview-preparation-resources, machine-learning, udacity, udemy
Language: Jupyter Notebook
Homepage:
Size: 21.4 MB
Stars: 356
Watchers: 10
Forks: 65
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

ultimate-awesome - Data-Science - Projects and awesome list for all Data Science fields. (Other Lists / PowerShell Lists)
README

        

[![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FElizaLo%2FData-Science&count_bg=%23090268&title_bg=%23B5B5BC&icon=datacamp.svg&icon_color=%23E7E7E7&title=Repository+Views&edge_flat=false)](https://hits.seeyoufarm.com)

### 🔄 Constantly updated. Subscribe not to miss anything.

> - [ ] For **Natural Language Processing** (NLU = NLP + NLG) please check [Natural Language Processing](https://github.com/ElizaLo/NLP-Natural-Language-Processing) repository.

> - [ ] For **Machine Learning**  algorithms please check [Machine Learning](https://github.com/ElizaLo/Machine-Learning) repository.

> - [ ] For **Deep Learning** algorithms please check [Deep Learning](https://github.com/ElizaLo/Deep-Learning) repository.

> - [ ] For **Computer Vision** please check [Computer Vision](https://github.com/ElizaLo/Computer-Vision) repository.

# 💠 Data Science Tasks 

> Folders with all materials for specific task/domain

- [AR VR](https://github.com/ElizaLo/Data-Science/tree/master/AR%20VR)

- [Class Imbalance Problem](https://github.com/ElizaLo/Data-Science/tree/master/Class%20Imbalance%20Problem)

- [Cloud Computing](https://github.com/ElizaLo/Data-Science/tree/master/Cloud%20Computing)

  - [AWS (Amazon Web Services)](https://github.com/ElizaLo/Data-Science/tree/master/Cloud%20Computing/AWS%20(Amazon%20Web%20Services)) 

- [Data Analysis](https://github.com/ElizaLo/Data-Science/tree/master/Data%20Analysis)

- [Data Analytics](https://github.com/ElizaLo/Data-Science/tree/master/Data%20Analytics)

- [Data Engineering](https://github.com/ElizaLo/Data-Science/tree/master/Data%20Engineering)

- [Data Preprocessing](https://github.com/ElizaLo/Data-Science/tree/master/Data%20Preprocessing)

- [Data Processing](https://github.com/ElizaLo/Data-Science/tree/master/Data%20Processing)

- [Data Science Life Cycle Methodologies](https://github.com/ElizaLo/Data-Science/tree/master/Data%20Science%20Life%20Cycle%20Methodologies)

- [Data Warehouse](https://github.com/ElizaLo/Data-Science/tree/master/Data%20Warehouse)

- [Data-Centric AI](https://github.com/ElizaLo/Data-Science/tree/master/Data-Centric%20AI)

- [Data](https://github.com/ElizaLo/Data-Science/tree/master/Data)

- [Graph Neural Networks](https://github.com/ElizaLo/Data-Science/tree/master/Graph%20Neural%20Networks)

- [Machine Learning Ops (MLOps)](https://github.com/ElizaLo/Data-Science/tree/master/MLOps)

- [Optimization](https://github.com/ElizaLo/Data-Science/tree/master/Optimization)

- [Overfitting](https://github.com/ElizaLo/Data-Science/tree/master/Overfitting)

- [Pipelines](https://github.com/ElizaLo/Data-Science/tree/master/Pipelines)

- [SQL](https://github.com/ElizaLo/Data-Science/tree/master/SQL)

- [Statistics](https://github.com/ElizaLo/Data-Science/tree/master/Statistics)

- [Tools and Tips](https://github.com/ElizaLo/Data-Science/tree/master/Tools%20and%20Tips)

# 👩🏻‍🏫 Educational Platforms

- [OpenEDU](https://openedu.ru)

# 🎓 University Courses

| Title | Description |

| :---:         |          :--- |

|[MIT OpenCourseWare](https://ocw.mit.edu)|
[MIT OpenCourseWare](https://www.youtube.com/c/mitocw) on YouTube
|

# Julia language

| Title | Description |

| :---:         |          :--- |

|[Introduction to Computational Thinking](https://computationalthinking.mit.edu/Fall20/)|


[MIT 18.S191 aka 6.S083 aka 22.S092, Fall 2020](https://computationalthinking.mit.edu/Fall20/)

[Spring 2021 / MIT 18.S191/6.S083/22.S092](https://computationalthinking.mit.edu/Spring21/)

|

## 🔢 Time Series

| Title | Description |

| :---:         |          :--- |

|[MIT 18.S096 Topics in Mathematics w Applications in Finance](https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-with-applications-in-finance-fall-2013/)|


The purpose of the class is to expose undergraduate and graduate students to the mathematical concepts and techniques used in the financial industry. Mathematics lectures are mixed with lectures illustrating the corresponding application in the financial industry. MIT mathematicians teach the mathematics part while industry professionals give the lectures on applications in finance.

[Video lectures](https://www.youtube.com/playlist?list=PLUl4u3cNGP63ctJIEC1UnZ0btsphnnoHR)

|

# 👩🏻‍🏫 Online Courses

- [Специализация Наука о данных для руководителей](https://www.coursera.org/specializations/executive-data-science)

- [Machine Learning Foundations](https://github.com/jonkrohn/ML-foundations)

  > Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science

- [DeepLearning.AI](https://www.deeplearning.ai)

  - 📹 [DeepLearning.AI](https://www.youtube.com/@Deeplearningai/playlists) YouTube channel

  - 📰 [DeepLearning.AI](https://www.deeplearning.ai/blog/)

## :octocat: GitHub Repositories

| Title | Description |

| :---:         |          :--- |

|[Data Science for Beginners - A Curriculum](https://github.com/microsoft/Data-Science-For-Beginners)|Azure Cloud Advocates at **Microsoft** are pleased to offer a 10-week, 20-lesson curriculum all about Data Science. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.|

|[Machine Learning for Beginners - A Curriculum](https://github.com/microsoft/ML-For-Beginners)|Azure Cloud Advocates at **Microsoft** are pleased to offer a 12-week, 26-lesson curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. |

|[start-machine-learning](https://github.com/louisfb01/start-machine-learning)|A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques|

|[Data Science Specialization, John Hopkins Coursera](https://github.com/mGalarnyk/datasciencecoursera)|Data Science Repo and blog for John Hopkins Coursera Courses. [Blog post - Blogging through the Data Science Specialization, John Hopkins Coursera](https://medium.com/@GalarnykMichael/blogging-through-the-data-science-specialization-john-hopkins-coursera-2ea63fb99ab5#.ckgc10iif)|

# 📚 Books

- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/)

- [Hands-On Machine Learning with Scikit-Learn and TensorFlow](https://www.oreilly.com/library/view/hands-on-machine-learning/9781491962282/)

  - [Machine Learning Notebooks](https://github.com/ageron/handson-ml)

    - > A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

- 

# :octocat: GitHub Repositories

| Title | Description |

| :---:         |          :--- |

|[Awesome Artificial Intelligence (AI)](https://github.com/owainlewis/awesome-artificial-intelligence)|A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.|

|[ml-surveys](https://github.com/eugeneyan/ml-surveys)|Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.|

|[awesome-analytics-engineering](https://github.com/Victoriapm/awesome-analytics-engineering)|Awesome list of resources for analytics engineers.|

|[Complete-Life-Cycle-of-a-Data-Science-Project](https://github.com/achuthasubhash/Complete-Life-Cycle-of-a-Data-Science-Project)| |

|[Data Science Learning Path](https://github.com/data-folks/data-science-learning-path)|A complete guide to learn data science for beginners|

|[Project Based Learning](https://github.com/practical-tutorials/project-based-learning/blob/master/README.md)|A list of programming tutorials in which aspiring software developers learn how to build an application from scratch. These tutorials are divided into different primary programming languages. Tutorials may involve multiple technologies and languages.|

# ⚙️ Tools

| Title | Description |

| :---:         |          :--- |

|||

# 📄 Papers 

- :octocat: [papers-with-video](https://github.com/amitness/papers-with-video)

| Title | Description, Information |

| :---:         |          :--- |

|[2021: A Year Full of Amazing AI papers- A Review / 📌 [work in progress...]](https://github.com/louisfb01/best_AI_papers_2021)|A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code. [work in progress]|

# 📜 Certifications

- [ ] [TensorFlow Developer Certificate](https://www.tensorflow.org/certificate?hl=en)

- [ ] [Certified Analytics Professional (CAP)](https://www.certifiedanalytics.org/)

- [ ] [Cloudera Certified Associate: Data Analyst](https//www.cloudera.com/about/training/certification/cca-data-analyst.html)

- [ ] [Cloudera Certified Professional: CCP Data Engineer](https://www.cloudera.com/about/training/certification/ccp-data-engineer.html)

- [ ] [Data Science Council of America (DASCA) Senior Data Scientist (SDS)](https://www.dasca.org/data-science-certifications/senior-data-scientist)

- [ ] [Data Science Council of America (DASCA) Principal Data Scientist (PDS)](https://www.dasca.org/data-science-certifications/principal-data-scientist)

- [ ] [Dell EMC Data Science Track](https://education.dellemc.com/content/emc/en-us/home/certification-overview/find-exam/data-science-associate.html)

- [ ] [Google Certified Professional Data Engineer](https://cloud.google.com/certification/data-engineer)

- [ ] [Google Data and Machine Learning](https://cloud.google.com/training/data-ml)

- [ ] [IBM Data Science Professional Certificate](https://www.edx.org/professional-certificate/ibm-data-science)

- [ ] [Microsoft MCSE: Data Management and Analytics](https://www.microsoft.com/en-us/learning/mcse-data-management-analytics.aspx)

- [ ] [Microsoft Certified Azure Data Scientist Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-data-scientist)

- [ ] [Open Certified Data Scientist](https://certification.opengroup.org/opencds)

- [ ] [SAS Certified Advanced Analytics Professional](https://www.sas.com/ru_ua/certification/credentials/advanced-analytics/advanced-analytics-professional.html)

- [ ] [SAS Certified Big Data Professional](https://www.sas.com/en_us/certification/credentials/data-management/big-data-professional.html)

- [ ] [SAS Certified Data Scientist](https://www.sas.com/ru_ua/certification/credentials/advanced-analytics/data-scientist.html)

# 🗣️ Online Conferences, Meetups, Data Summer Schools

- Live Webinars & On-demand Recordings by [ODSC COMMUNITY](https://app.aiplus.training/bundles/odsc-webinars)

- [Data Science fwdays'19](https://www.youtube.com/playlist?list=PLPcgQFk9n9y8CCapH9u99Va7Ufs5bVZZn&utm_source=medium_gid_ds20&utm_medium=youtube_video_ds19&utm_campaign=ds20_online) (playlist)

- [Webinars 2020, Computer Science UCU](https://www.youtube.com/playlist?list=PLr1w0qwTp9lDX4ZaS1iRvdWLJEzpBKjWz)

- [Eastern European Machine Learning Summer School, 2020 (Deep Learning and Reinforcement Learning](https://www.eeml.eu/home)

  - [Program](https://www.eeml.eu/program)

  - [Practical Sessions 2020](https://github.com/eemlcommunity/PracticalSessions2020), GitHub Repository

# 🐦 Twitter

- [Christopher Manning](https://twitter.com/chrmanning)

- [Sebastian Ruder](https://twitter.com/seb_ruder)

- [Prof. Sam Bowman](https://twitter.com/sleepinyourhat)

# 🎧 Podcasts

- [Lex Fridman Podcast | Artificial Intelligence (AI)](https://podcasts.apple.com/ua/podcast/lex-fridman-podcast-artificial-intelligence-ai/id690305972?l)

- [Machine Learning, Andrew Ng, Stanford](https://podcasts.apple.com/ua/podcast/machine-learning/id384233048?l)

- [Awesome Data Podcasts](https://github.com/DataTalksClub/awesome-data-podcasts)

# 🗞️ Blogs

## Companies Blogs

- - :octocat: [Software Engineering Blogs](https://github.com/kilimchoi/engineering-blogs)

  - > A curated list of engineering blogs

- [Amazon | Science](https://www.amazon.science)

- [AWS Machine Learning Blog](https://aws.amazon.com/ru/blogs/machine-learning/)

- [Meta AI](https://ai.facebook.com)

- [The Netflix Tech Blog](https://netflixtechblog.com)

- [Uber Engineering](https://eng.uber.com)

- [NVIDIA Developer](https://developer.nvidia.com)

- [The Stanford AI Lab Blog](https://ai.stanford.edu/blog/)

## Other Blogs

- [Towards AI](https://towardsai.net/p/category/editorial)

  - [Tutorials](https://github.com/towardsai/tutorials) 

    - > AI-related tutorials.

- [Data Notes](https://data-notes.co)

- [Louis Bouchard | @What's AI - Making AI Accessible](https://www.louisbouchard.ai)

- [Michael Galarnyk](https://medium.com/@GalarnykMichael)

- [Data Science Dojo](https://online.datasciencedojo.com)

- [DeepLearning.AI](https://www.deeplearning.ai/blog/)

## 📰 Articles

- [AI & ML дайджест от DOU](https://dou.ua/lenta/digests/ai-ml-digest-18/?from=tg-tech)

- [Data Science и Machine Learning: с чего начать и где учиться](https://dou.ua/lenta/columns/study-data-science-and-ml/?from=recent)

## 🫂 Communities 

| Title | Description |

| :---:         |          :--- |

|Coursera Comminity [Data Science](https://coursera.community/data-science-8?utm_medium=email&utm_source=community&utm_campaign=Hw2psJDeEeqxW2EAYUwV1Q)| |

|[Locally Optimistic](https://locallyoptimistic.com)|A community for current and aspiring data analytics leaders. Started in NYC in early 2018 as an outgrowth of a slack channel / extremely informal meetup group, we hope to share our thoughts / opinions / experiences / trials / tribulations with others in the community.|

|[Deepchecks Community](https://deepcheckscommunity.slack.com/join/shared_invite/zt-y28sjt1v-PBT50S3uoyWui_Deg5L_jg#/shared-invite/email)|A place to talk about MLOps news, articles, conferences, and really just anything in the MLOps space.|

## 🔹 Telegram Chanels

- [DataScience Digest](https://t.me/DataScienceDigest)

  - Collection of the top articles, videos, events, books and jobs on Machine Learning, Deep Learning, NLP, Computer Vision and other aspects of Data Science.