![911 season finale 911 season finale](https://guidedatascience.com/wp-content/uploads/2021/04/ex1.png)
Let me first clarify that I am starting my journey into data science from a programmer and database developer standpoint. Therefore, in conclusion, I think that the best answer an experienced data scientist might have to a question in regard to their preferred tool is the following: My preferred tool is the optimal one, that is the one that best fits the task at hand. Therefore, a person, who does not have an urge to explore other tools within their domain, could rank lower among candidates in the overall fit for a data science position (of course, this is quite fuzzy, as some people are very quick in learning new material, plus, people might have not had an opportunity to satisfy their interest in other tools due to various personal or workplace reasons). This is because experimentation is deeply ingrained into the nature of data science due to exploratory data analysis being a essential and, even, a crucial part of it. Having said that, if a data scientist (even experienced one) does not have knowledge (at least, basic) of modern data science tools, including big data-focused ones, it is somewhat disturbing. Data science does not automatically imply big data - there is plenty of data science work that Excel can handle quite well. This is the corollary from my above-mentioned thoughts.
![911 season finale 911 season finale](https://about.infogr.am/wp-content/uploads/2016/01/click-analysis-toolpak.png)
However, I think that most experienced data scientists recognize the need to use tools, which are optimal for particular tasks, and adhere to this approach.Ĭan you assume a lack of experience from someone who does primarily I've seen some experienced data scientists, who use Excel - either due to their preference, or due to their workplace's business and IT environment specifics (for example, many financial institutions use Excel as their major tool, at least, for modeling).
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If your code is broken, your analysis is worse than useless. If you don't have tests, your code is broken. Instead of using xls, use csv (still very complex and has lots of edge cases, but csv parsers are fairly good nowadays.) Good for collaboration and also good for reproducibility. A data analysis needs to be reproducible. To summarize what Excel doesn't have and is a must for any analysis: That's because Excel was never intended for that kind of analysis and as a consequence of this, it is incredibly easy to make mistakes in Excel (that's not to say that it is not incredibly easy to make another type of mistakes when using other tools, but Excel aggravates the situation even more.) However, someone who is supposedly experienced in data analysis simply can not use Excel as his main tool (excluding the obvious task of looking at the data for the first time).
![911 season finale 911 season finale](https://www.nonprofitaccountingbasics.org/sites/default/files/excel_dedupe.png)
Most non-technical people often use Excel as a database replacement.