Data Science Track (Introducion)

A complete track on data science: from introduction to advanced topics.
Each project is a separate repository with completed assignments.

Repositories

#	Project	Description	Link	Stack
01	ds-module-03	This project aims to teach you the basics of object-oriented programming in Python. The tasks are designed in a sequential manner. From creating a simple class to implementing logging and integrating with external services.	Open	Python, OOP
02	ds-module-10	The first project of the introductory ML course covers binary and multiclass classification, regression, and clustering. Model evaluation methods and overfitting prevention techniques are also explored. All practice is based on real user activity data.	Open	Python, Jupyter Notebook, Scikit-learn, Pandas, Nmpy, Matplotlib
03	ds-module-11	This project aims to explore advanced machine learning techniques using the scikit-learn library. The tasks range from learning about regularization and hyperparameter tuning to creating ensembles and organizing code into an OOP-based pipeline structure.	Open	Python, Jupyter Notebook, Scikit-learn, Pandas, Nmpy, Joblib, Tqdm

Progress

Introduction Stage

#	Project	Status
01	ds-module-01	🕑 Waiting to add
02	ds-module-02	🕑 Waiting to add
03	ds-module-03	✅ Completed
04	ds-module-04	🕑 Waiting to add
05	ds-module-05	🕑 Waiting to add
06	ds-module-06	🕑 Waiting to add
07	ds-module-07	🕑 Waiting to add
08	ds-module-08	🕑 Waiting to add
09	ds-module-09	🕑 Waiting to add
10	ds-module-10	✅ Completed
11	ds-module-11	✅ Completed