Data Science

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

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

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

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	🔗	🔗	🔗

Productive Tools

Title	Article	Repository
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