First Tools of the Trade

Tips, Tricks, & Unsolicited Data Science Opinions for the Aspiring Text Analyst

Chapter Contents

• Setting up a Computing Environment
• Tools for Data Infrastructure
• Tools for Data Science

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Reading Materials Referenced ^^^

• Practical Data Science

• On writing clean Jupyter notebooks

• Scikit-Learn Course

A recurring theme of this course, if there is one:

Text analysis is, fundamentally, an applied discipline.

This means a huge component of actually learning text analysis, NLP, etc., will necessarily focus on hands-on application. While there is certainly a need and place for deep, theoretical foundations of various algorithms and semantic structure theories… I hope this course will contribute to a holistic view of what doing text analysis involves: from the daily tricks to the dastardly pitfalls.

However…

This focus on application-over-theory means familiarizing yourself with the tools that do the application with you. So while a lot of time will be invested in discussing tangential tools and contexts, realize that these are all part of building professionalism and unification around what doing text analysis looks like.