One of my readers asked: “Any Python practice projects we can work on for learning you can suggest?”
1) A Django Webapp
This is especially for those of you who haven’t done web development.
(Data scientists: I’m looking at you.)
Being able to create a web application is a valuable skill for any developer. The reason is that it allows you to take any other kind of programming you do, and package it in a way that’s accessible to the masses.
If you haven’t done web dev before, this needs to be your #1 priority, compared to others on the list. (If you *have* done web dev, skip to the next item… get out of your comfort zone.)
What framework do you use? Google will point out a dozen great choices for you. It doesn’t matter too much which you use. You can pick the one you like.
But if you want a recommendation, I’ll give you one:
It’s a great full-stack framework and well documented.. If you find yourself spending more than a few minutes choosing a framework, just use Django and get coding.
So that’s one project idea. Next one:
2) A Command Line Tool
If you haven’t learned to create command-line programs… you’re missing out. So here you can learn.
When you take your program, and package it in a scriptable command-line interface…
With configuration controllable by options and flags…
And inputs and outputs for the program controlled by command-line args…
This ALWAYS increases the value of your program. Always. 100% of the time.
So if you haven’t ever done it before… you need to learn.
Basically, this means learning the “argparse” module. It’s built into Python’s standard library.
There are other libraries for building command-line interfaces out there, which are not in Python’s standard library. They have their fanatical fans who are already writing angry emails to me, full of misspelled words, for having the gall to recommend argparse instead of their favorite libwhateverz.
Ignore them. Argparse is full-featured, and hard to improve on. And it’s a battery included with Python.
So next time you write a Python program, generalize it. Use argparse to make it more automatable, flexible, scriptable, and overall better.
So that’s the second project suggestion. And finally:
3) Machine Learning
If you haven’t ridden this hype train yet, you ought to take at least a short day trip.
Yes, all the yapping about artificial machine learning Intelligenz is over-hyped. But. It has real substance, too. And you will benefit from learning it.
You have two options for what to do. I recommend you learn a library called sci-kit-learn. It includes tools for both supervised and unsupervised learning, and for building pipelines.
That’s one option, and what I recommend you start with. Another option is to learn Tensorflow. I actually think you’ll do better if you go to that one after you have some experience with sci-kit-learn, but if you insist on skipping ahead, at least make sure you learn math for dealing with “compute graphs” first.
So how do you use your new ML library? Well, it’s best if you can apply it to problems you’re facing in your work. But that’s hard to do while you’re learning the ropes.
So there’s a training ground: Kaggle.
Just search for “Kaggle Competitions”, and look for the “Getting Started” category. They make it easy for you.
Source by Aaron Maxwell