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Data-science

数据科学项目的综合资源库和实践指南

Data-science项目汇集了丰富的数据科学资源,涵盖MLOps、数据管理、测试和生产力工具等领域。通过文章、代码和视频教程,该项目全面展示了数据科学工作流程,从项目管理到部署。它为数据科学家和机器学习工程师提供了提高效率、构建可靠项目的实用指南。

Data Science

View on GitHub Daily Data Science Tips View on YouTube

Collection of useful data science topics along with articles and videos.

Subscribe to:

  • CodeCut for articles and bite-sized Python tips in your mailbox
  • My YouTube channel for videos related to Python and data science

How to Download the Code in This Repository to Your Local Machine

To download the code in this repo, you can simply use git clone

git clone https://github.com/khuyentran1401/Data-science

Contents

  1. MLOps
  2. Data Management Tools
  3. Testing
  4. Productive Tools
  5. Python Helper Tools
  6. Tools for Deployment
  7. Speed-up Tools
  8. Math Tools
  9. Machine Learning
  10. Natural Language Processing
  11. Computer Vision
  12. Time Series
  13. Feature Engineering
  14. Visualization
  15. Mathematical Programming
  16. Scraping
  17. Python
  18. Logging and Debugging
  19. Linear Algebra
  20. Data Structure
  21. Statistics
  22. Web Applications
  23. Share Insights
  24. Cool Tools
  25. Learning Tips
  26. Productive Tips
  27. VSCode
  28. Book Review
  29. Data Science Portfolio

MLOps

TitleArticleRepositoryVideo
Stop Hard Coding in a Data Science Project – Use Configuration Files Instead🔗🔗🔗
Poetry: A Better Way to Manage Python Dependencies🔗🔗
Git for Data Scientists: Learn Git through Practical Examples🔗🔗
Introduction to Weight & Biases: Track and Visualize your Machine Learning Experiments in 3 Lines of Code🔗🔗
Kedro — A Python Framework for Reproducible Data Science Project🔗🔗
Orchestrate a Data Science Project in Python With Prefect🔗🔗
Orchestrate Your Data Science Project with Prefect 2.0🔗🔗🔗
DagsHub: a GitHub Supplement for Data Scientists and ML Engineers🔗🔗
4 pre-commit Plugins to Automate Code Reviewing and Formatting in Python🔗🔗🔗
BentoML: Create an ML Powered Prediction Service in Minutes🔗🔗🔗
How to Structure a Data Science Project for Maintainability (with DVC)🔗🔗🔗
How to Structure an ML Project for Reproducibility and Maintainability (with Prefect)🔗🔗
GitHub Actions in MLOps: Automatically Check and Deploy Your ML Model🔗🔗
Create Robust Data Pipelines with Prefect, Docker, and GitHub🔗🔗
Create a Maintainable Data Pipeline with Prefect and DVC🔗🔗
Build a Full-Stack ML Application With Pydantic And Prefect🔗🔗🔗
Streamline Code Updates with DVC and GitHub Actions🔗🔗🔗
Create Observable and Reproducible Notebooks with Hex🔗🔗🔗
Build Reliable Machine Learning Pipelines with Continuous Integration🔗🔗🔗
Automate Machine Learning Deployment with GitHub Actions🔗🔗🔗
How to Build a Fully Automated Data Drift Detection Pipeline🔗🔗🔗

Data Management Tools

TitleArticleRepositoryVideo
Introduction to DVC: Data Version Control Tool for Machine Learning Projects🔗🔗🔗
Great Expectations: Always Know What to Expect From Your Data🔗🔗
Validate Your pandas DataFrame with Pandera🔗🔗🔗
Introduction to Schema: A Python Libary to Validate your Data🔗🔗
How to Create Fake Data with Faker🔗🔗
Hypothesis and Pandera: Generate Synthesis Pandas DataFrame for Testing🔗🔗🔗
What is dbt (data build tool) and When should you use it?🔗🔗🔗
Streamline dbt Model Development with Notebook-Style Workspace🔗🔗🔗

Testing

TitleArticleRepositoryVideo
Pytest for Data Scientists🔗🔗🔗
4 Lessor-Known Yet Awesome Tips for Pytest🔗🔗
DeepDiff — Recursively Find and Ignore Trivial Differences Using Python🔗🔗
Checklist — Behavioral Testing of NLP Models🔗🔗
Detect Defects in a Data Pipeline Early with Validation and Notifications🔗🔗🔗
Write Readable Tests for Your Machine Learning Models with Behave🔗🔗🔗

Productive Tools

TitleArticleRepository
3 Tools to Track and Visualize the Execution of your Python Code🔗🔗
2 Tools to Automatically Reload when Python Files Change🔗🔗
3 Ways to Get Notified with Python🔗🔗
How to Create Reusable Command-Line
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