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Data Science
Collection of useful data science topics along with articles and videos.
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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
- MLOps
- Data Management Tools
- Testing
- Productive Tools
- Python Helper Tools
- Tools for Deployment
- Speed-up Tools
- Math Tools
- Machine Learning
- Natural Language Processing
- Computer Vision
- Time Series
- Feature Engineering
- Visualization
- Mathematical Programming
- Scraping
- Python
- Logging and Debugging
- Linear Algebra
- Data Structure
- Statistics
- Web Applications
- Share Insights
- Cool Tools
- Learning Tips
- Productive Tips
- VSCode
- Book Review
- Data Science Portfolio
MLOps
| Title | Article | Repository | Video |
|---|---|---|---|
| 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
| Title | Article | Repository | Video |
|---|---|---|---|
| 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
| Title | Article | Repository | Video |
|---|---|---|---|
| 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 | 🔗 | 🔗 | 🔗 |
