This repo contains reference material for myself regarding all things Data Science!
- AB_Testing: Notebooks regarding Bayesian AB testing
- Books: Notebooks from text books
- Data Analysis: Analysis of datasets with notebooks
- Datasets_Tools_and_Packages: README listing useful data science tools, datasets and packages
- Deep_Learning: Notebooks regarding deep learning
- fastai: Notebooks from the fastai course
- Kaggle: Kaggle competition attempts
- Model_API_Example: Example of data science model deployment. There is a medium article accompanying this
- Python: Python tip, tricks, best practise and coding paradigms i've learnt
- Recommendation Systems: Building a recommendation system
- sklearn_example: Examples of sklearn usage along with code snippets
- Theory: Notebooks illustrating some theory in data science
All work is done in python 3+. Most of the work will require >= 3.6 due to f-strings
Any notebooks that require additional packages will have either a Pipfile or a pyproject.toml listing the
dependencies. Older projects will be using the Pipfile via the pipenv package manager. I have now switched to
using Poetry as my dependency manager which utilises the pyproject.toml file.