I recently dove head first into the world of data science. Without much prior experience in programming language, learning Python was a challenge in and of itself. In fact, my fear of learning Python is what actually delayed my exploration of data science. Even after starting my data science journey, it took me a while to REALLY buy into Python. After all, why go through all the pain of learning Python when I can just do everything I need to do in Excel?
I come from a traditional finance background where most of my work was done through a complicated web of spreadsheets filled with pivot tables, vlookups, sumifs, conditional statements, and so on. If you come from a similar background as me, you can probably relate to my sentiment and attachment to spreadsheets.
In some sense, it’s a legitimate point. After all, what can’t Excel do? I’ve made my through a number of big companies where the majority of all the heavy lifting related to analysis, reporting, and planning were done through what I call the “Excel eco-system”. Additional features like Hyperion Essbase, SmartView, PowerPivot, PowerBI, and a number of really cool features available on Excel that do make it possible to work with data and get stuff done. In my field, Excel, Alteryx, and Tableau were sort of the go-to “Holy Trinity”.
So the question remains. Why Python?
#data-science #python #excel #developer
