Resources for Data Scientist

The following are a collection of links and resources I have gathered over the past few years in my involvement with Data Science. Topics range from fundamental data concepts to state-of-the-art artificial intelligence algorithms. Good luck in your journey learning to teach machines to think like humans. Note that these lists are not in any specific order. I plan to update them regularily and eventually provide a better curriculum to learn Data Science more efficiently. For now I will keep it simple and random, and provide as much references to as various topics as possible. Resources include papers, code repositories, slides, my own notes, courses, syllabus, tutorials, blog articles, other websites, etc.

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Text Mining

• Books:
• Slides:
  • Quick Tour of Text Mining (Professor Yi-Shin Chen, 2017)

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Data Mining

• Books:
• Slides:
  • Quick Tour of Data Mining (Professor Yi-Shin Chen, 2017)

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Natural Language Processing

• Books:
  • Natural Language Processing - A Friendly Handbook (Elvis Saravia, 2017)

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Deep Learning

• Books:+
  • Deep Learning (Ian Goodfellow and Yoshua Bengio and Aaron Courville)

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Computational Mathematics

• Websites:
  • Lectures by Professor Chaur-Chin Chen

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Probability Theory & Statistics

• Websites:
  • Lectures by Professor Chaur-Chin Chen)

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Machine Learnining

• Books:
  • Machine Learning - A Friendly Handbook (Elvis Saravia, 2017)

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Data Structures

Coming Soon

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Advance Database Concepts

Coming Soon

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Operating System

• Books:
  • Operating Systems - A Friendly Handbook (Elvis Saravia, 2017)

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Academic Research Methods

Coming Soon

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If you have some suggestions to improve this list, please comment below or get in touch via my social handles; I will eventually create a Github repo to accept pull requests.

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