What do you think? Below is my answer.

Using tools that your competitors are not using, or developing ad-hoc solutions, and mastering them, is more important than the tool itself. Home-made solutions can be tested in a matter of hours, here is one example. For the creative mind, it is even easier than going through the painful learning curve associated with some of the tools. Think about this: How can you beat competing stock traders if you are using the same tools as they do? Sure, if you master these tools, you will beat many of them who are just beginners, but you will be crushed by those who completely master these tools, and by those coming with out-of-the-box solutions (as well as better hardware / Internet bandwidth / data sources.)

Having business acumen / communication skills may in itself substantially help you to outperform the best technical experts, even if your data science skills are limited and you are using basic tools such as Excel.

I think it depends also on what kind of data scientist you are. BI analysts tend to use tools with great dashboards, while developers like a command-line / programming environment (most have nice libraries nowadays.) In my case, I use both, including Excel! Also, anything that helps automate your task is very helpful.

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Agreed. I've always encouraged my teams to use whatever tools they want/need to get the job done the most effectively and efficiently.

Clean data?

Collaboration is something often overlooked here. Working in tools that encourage collaboration actively between a team (such as Dataiku's enterprise solution), or those that have active online communities naturally lend themselves to productivity. Hitting a wall in your code using a tool that peers are unfamiliar with is far more daunting than doing so in one that is widely supported and used.


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