Data Science Notes

WIP repository

This repository is intended to gather all knowledge I have gained from various sources learning Data Science and give a concise view of the field. Furthermore, it is dedicated to help everyone get started, who is interested in Data Science in general or those ones, who already have some experience in the field, but wishes to refresh his/her knowledge.

The original intention is to create a complete overview of the field from a theoretical perspective. The different concepts are introduced from a theoretical perspective, and then implement them in different programming languages (Python, R, Octave) with using as few libraries as possible to get a better understanding of the concepts. The emphasis is on keeping the scripts clear, they are not optimized.

Table of contents:

Intel logo Among others, I would like to thank Intel for giving me the initiative of starting this blog with accepting my application to the Student Ambassador for AI program. Furthermore, thank you for all the remarkable teachers both at SDU and all over the world, who took the time to either record their lectures or created tutoring videos in their free-time.

Resources and References:

Academic teaching: Online courses, tutorials:
  • Probability and Statistics:
    • Khan Akademy
  • Machine Learning:
    • Geoffrey Hintion
    • Andrew NG
    • Hugo Larochelle
    • Nando de Freitas
Books:
  • Probability and Statistics:
    • Book1
  • Machine Learning:
    • Tom M. Mitchell (1997): "Machine Learning"