Why learning code for data is worthwhile
There are lots of tools that have come out in the past couple of years that make data easier to handle, analyze, and visualize. Maybe you've used them. I use them all the time. However, no matter what software you use, there is always going to be a limitation in what you can do with it.
Have you ever been using an application (not just for data) and wished it could do something else? If you want a new feature, you have to wait for someone else to develop it, but if you program, you could implement your own features.
With a little bit of coding know-how, you gain more flexibility — and a little goes a long way.
I think a lot of people avoid programming, because it seems scary and they have no idea where to start. I felt the same way when I first started learning code in college. I had no idea what I was doing, and it was actually one of the reasons I wanted to get away from my engineering major and jump into statistics. That programming background came in handy though and grew more useful as I played with more data. Now it's hard for me to imagine doing data without having that flexible tool in my back pocket.
Just think of learning code as learning a new language (because, that's basically what you're doing). When you start learning a new language, you're not writing essays the first day. You learn punctuation, grammar, spelling, and other basics, and then you build up to paragraphs, essays, or even books. Same thing with code. You learn the syntax and logic, and then apply what you learn to bigger problems.
It might feel a little slow at first, but another plus is that you can reuse code, meaning you could end up saving time in the long run.
Again, this is not to say you should abandon the other tools and use code exclusively. Rather, it's another tool in your box — a powerful one that increases its utility the more it is used.
In the end, the more ways you can explore, analyze, and present your data the less likely you are to get stuck and the more likely you'll be able to figure out what your data has to say.