Languages, Tools, and Software (Cont.)
Of course, there are many more packages out there that will come in handy when you’re working on data science projects, such as Scikit-Learn, NumPy, Matplotlib, Statsmodels, etc. In the industry, you’ll also find that commercial SAS and SPSS do well, but also other tools such as Tableau, Rapidminer, Matlab, Excel, Gephi will find their way to the data scientist’s toolbox.
| You see again that one of the main distinctions between data engineers and data scientists, the emphasis on data visualization and storytelling, is reflected in the tools that are mentioned. Tools, languages, and software that both parties have in common, as you might have already guessed, are Scala, Java, and C#. |
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Educational Background
Data scientists and data engineers might also have something in common: their Computer Science backgrounds. This study area is widely popular for both professions. Of course, you’ll also see that data scientists have often studied econometrics, mathematics, statistics and operations research.
| They often have a little bit more business acumen than data engineers. You often see that data engineers also come from engineering backgrounds, and more often than not, they have had some prior education in computer engineering. |
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Successful people are always looking for opportunities to help others. Unsuccessful people are always asking, “What’s in it for me?” ― Brian Tracy |