If Python of the library has enormous packages, why we still need to use R, SQL, Tableau, and Power BI?

Hi Yanhao! Thanks for reaching out. Please accept my apologies for the delayed response. This is a great question indeed.  Usually, each tool has its advantages and disadvantages, as well as areas where it is strong and such where it is not that applicable. An advantage (and theoretically sometimes a disadvantage) for R and Python is the fact that they are open-source. Therefore, they are accessible to millions of people and allow you to do analysis and analytics and practically no cost. They are not the tools that are best for working with relational database management systems. In fact, SQL is the language that has been designed to do that. This means it has greater capacity to deliver specific parts of the given data set when the tables in it have been related in a specific way. Power BI and Tableau join the picture with another very strong ability - they can very quickly interrelate and integrate the work of several software tools, as well as provide you with a very large set of visualisations that you can quickly and efficiently create. Therefore, each software can be used for obtaining a different goal. Moreover, sometimes different software tools can be integrated to solve more complicated tasks. As a matter of fact - that's what we'll do later in the Program - we'll show you later how to integrate Python, SQL, and Tableau to solve a certain business task. Hope this helps.
Best,
Martin

Because Python is far more difficult to learn than the others (R excepted, coding ability is needed for R). Tableau and Power BI are desktop tools that a decent Excel user can use. Plus it's far quicker to build a dashboard in Tableau than Python. SQL is a simple language to learn, there aren't many commands to manipulate and interrogate the data. SELECT, FROM, WHERE, JOIN, GROUP BY, ORDER BY covers the majority of things most analysts would ever need to do. And it's great for manipulating relational data sources.