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Add a book (#6756)

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* ADD PythonDataScienceHandbook For data-analyst

Python Data Science Handbook is a well-known book about Python data processing and analysis. Its original author has made the contents of this book and the corresponding notebook source code open source on GitHub. It is worth a look, especially for those who want to understand or urgently need Python's functions in data processing and analysis.

* Update src/data/roadmaps/data-analyst/content/learn-a-programming-lang@i2uEcaO4bJhcZ5ayRs2CQ.md

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Co-authored-by: Kamran Ahmed <kamranahmed.se@gmail.com>
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We have two main programming languages when it comes to data analysis: Python and R. Both have extensive libraries to help with decision-making processes in various situations, assisting in manipulating, modeling, and visualizing data. Python is a versatile language, used not only for data analysis but also for web development, automation, artificial intelligence, and more. R, on the other hand, was specifically created for statistical analysis and data visualization, making it an excellent choice for statisticians and researchers. It is known for its advanced visualization capabilities, allowing the creation of highly customizable and sophisticated graphs and plots.
With potential doubts about which language to choose to advance in a data career, it is ideal to consider your goals and/or the current market needs and choose which language to learn. If you are more interested in a career that combines data analysis with software development, automation, or artificial intelligence, Python may be the best choice. If your focus is purely on statistics and data visualization, R might be more suitable.
With potential doubts about which language to choose to advance in a data career, it is ideal to consider your goals and/or the current market needs and choose which language to learn. If you are more interested in a career that combines data analysis with software development, automation, or artificial intelligence, Python may be the best choice. If your focus is purely on statistics and data visualization, R might be more suitable.
Learn more from the following resources:
- [@article@Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/)