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Crafting My Data Science Toolkit
Introduction
Lots of tasks require a good set of tools with different variations. As no shirt fits everyone, no single tool can address all cases. Different tools come with their own nuances and purposes. I would share in this article my takes on building a robust toolkit which help me navigating through my professional life.
My approach is to build the toolkit following a tree structure where trunks branches are domains or application area of these tools whereas the leaves would be specific tools and niches. This would make it easy to find/ navigate to the necessary tool when we deal with a particular project. Just to remind you that this is only the outline for necessary tools. Going into detail will depend on the professional journey of each person with their own projects and struggles.
One thing more to note is that one tool can serve different purpose, so our toolkit may tangle a little bit.
Here is an overview of my toolkit tree:
Trunk: Data Science Toolkit
Branch: Data Collection & Preparation
- Web Scraping Tool
- Data Wrangling Tool
Branch: Data Analysis
- Statistical Analysis Tool
- Machine Learning Library
