python could use for machine learning, visualization, statistic, database. python just like a king in chess!
So, why the company still need other skills like R, SQL, Tableau, and Power BI?
as a junior data learner, this kind of question has confused me a lot.
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.
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.
Hi Andrew! Thanks for reaching out. Please accept my apologies for the delayed response. Thank you for sharing your observations with the Community! Essentially, you are absolutely correct. May I please add that each using each of these tools may become quite a challenge for more advanced tasks. I.e. I just wanted to clarify that e.g. an advanced Python user may still struggle with Tableau even if they are quite proficient with Tableau as well. Other than that, you are quite right about SQL in the sense that there aren’t too many tools to learn about. However, with these tools you can obtain a great variety of output. So, eventually, using SQL can be quite a challenge, too. Moreover, it relates to using relational databases, and relational database theory is more often than not a challenge. Of course, that’s only my personal observation and view on this subject. But since I thought this turned into an interesting topic, I wanted to join, if you don’t mind! Hope this helps. Best, Martin
So to summarise, every tool gets harder as you want to do more complex things 🙂 Agreed on understanding relational databases for SQL; although I think that is true for all of the tools where any data joining occurs, even Excel with the vlookup. I guess the difference is the technical level of difficulty. The less-techy you are, the harder it’ll be to learn the pure coding languages such as python and SQL. Even Power BI to some degree, it requires more tech knowledge than Tableau to begin.
Well said, Andrew! Thanks for the summary and yes, I think you put the rest of it in very good explanations!