By the way it should be Timeseries struggling.
It’s basically fight of tools. When you have plethora of tools available, you always get confused with which one to follow. In my case it’s R vs. Python
I like the ease of R to do data wrangling and get more insight but it’s Damm slow when it comes to running forecasts locally.
Python on other hand has huge ML libraries, and seems to be optimised for average PCs. Though the language itself is simple to learn, it’s not as nimble as R in RStudio
R also has many packages to do wonderful analytics specially forecasting, as that’s my interest. Python also has similar packages but mostly ML is it’s strength.
Now when I get data, I really spend time is weighing the tools to be used by using them, and it’s killing the time. I think in need to Jordon my workflow on what to use for what and stick with it.
In this journey, I think I’ll be sharing some nice shortcuts and some challenges those I’ll be facing and coming up with some solutions, optimised or not.
Let’s discuss this in future posts.