About data-science

This repository documents a self-directed 23-month sabbatical dedicated to mastering theoretical and applied data science through an open online university approach. It serves as a comprehensive portfolio combining handwritten notes with hands-on implementations across the full data science lifecycle. Key sections cover machine learning concepts and projects, deep learning applications, real-world case studies utilizing analyst skills, data engineering, and MLOps pipelines. The collection also includes business intelligence projects using industry-standard tools, alongside foundational mathematics implementations in probability, statistics, and linear algebra. Designed for execution in Jupyter Notebooks within an Anaconda environment, the resources offer practical exposure to core competencies required for a Master's level education. The repository tracks progress through detailed commit histories and includes solved numerical problems in PDF format. While originally a personal learning log, it invites commun

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suraj520

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Data Science

Roadmap

The repository contains hands-on implementation of various key branches of data-science.

Refer the commit history for detailed timeline of commits at link .

Solved numerical problems can be viewed at link

Section	Description
Machine learning	Various theoretical concepts covered with hand written notes along with projects.
Zero to One Deep Learning	Various deep learning projects
Case Studies	Various case studies in the domain of data science which utilise the skills of a data analyst, business analyst and a data scientist
Data Engineering and MLOps	Various theoretical concepts covered with hand written notes along with data engineering and mlops projects.
Business intelligence	Various business intelligence projects using popular BI tools.
Probability and Statistics	Various implementations of concepts used in probability and statistics for data-science and machine learning with hand-written notes.
Linear Algebra	Various implementations of concepts used in linear algebra for data-science and machine learning with hand-written notes.

Getting Started

VScode with Anaconda and jupyter notebook integration.

Usage

Most of the files can be easily executed as jupyter notebooks.

Contributing Guidelines

We welcome contributions from anyone and everyone. Please take a moment to review our guidelines before getting started:

Code of Conduct

This project follows a Code of Conduct to ensure that everyone who participates feels safe and respected. Please review it before contributing.

How to Contribute

Fork this repository
Create a branch for your feature or bug fix
Make your changes
Write tests for your changes (if applicable)
Run the tests locally
Submit a pull request

Pull Request Guidelines

Make sure that your code adheres to our code style and best practices
Include tests that cover your changes (if applicable)
Update the README and/or documentation (if applicable)
Be descriptive in your pull request description, and reference any relevant issues or pull requests

Issues and Bug Reports

If you encounter any bugs or have feature requests, please file an issue on our issue tracker. When filing a bug report, please include steps to reproduce the issue and any relevant error messages.

Development Setup

If you'd like to contribute but don't know where to start, check out the open issues on our issue tracker or reach out to us on [email protected]. We're happy to help you get set up and find something to work on!

License

By contributing to this project, you agree that your contributions will be licensed under the project's LICENSE.

data-science