My data science studies started as a Masters in Applied Statistics. One of the courses was in machine learning and it had a similar approach to what you are describing. So, I can empathize a little with your current view. But, just like other things you might have learned in life, the way you do things in an academic setting and the way you do things in a business environment (i.e. for a client) are completely different. Here's what I've learned since my initial studies:
1 - Learn Python
Sure, there's other tools out there and they're fine (I used to write R code with the best of them) but Python is where the future is at, period. Plus, very few tools scale as well as Python and that's important if you want to work on some really cool stuff.
2 - It all comes down to implementation
Guess what? All those things you're learning now (confusion matrices, factor reduction, etc) don't mean a thing to your clients. They're going to just look at you and say, "What's the product? When are you going to deploy something to my phone? Where my webapp to click on?". A large part of your job will be to turn all your work into a product and you will find yourself wearing a quasi-software-developer hat. This is also another good reason to learn python.
3 - Data pipelines take time
A LOT of your work will be on data manipulation and just making sure that the data pipelines you need are there. Sure, you have a database - but how are you going to update it? What pre-processing do you need? Where are you results stored? You will spend A LOT of time figuring out this stuff. You'll miss your school days when datasets were given to you in a nice and clean fashion :)
4 - Neural networks kick ass
Once you take a bite of this apple, it's hard to go back :). Learn Keras and enjoy the ride. After a while, you'll have to remind yourself what decisions trees are :)
5 - Model searches are much easier now
To be 100% clear, the "model search" approach you are doing now is VERY valuable experience. You should definitely work hard at those classes. However, if you have the time, look at either (1) Data Robot or (2) Watson Analytics. Both of those packages do, essentially, the same thing. They will take your dataset and find you the best model for it. All of the items you described above are done for you in a matter of seconds. It's almost scary how fast they are and they're very effective in helping you reduce your work. However, be warned that these packages only support supervised data. You will still have to do it the old-fashioned way for unsupervised data (or label some and use a neural network).
6 - I still use the theory behind other models
Even if I use neural networks a lot, the other models are still useful. You'll still use linear regression or decision trees for basic problems. It's also helpful when I decide to read some research papers on archivx or whatever. So, I'll still use them for my own study and understanding, but that's about it.