This post is directed at Python programmers (in particular, data scientists) traveling in the opposite lane; I’ll detail some of the initial conceptual hurdles you may face when learning to adapt your Python instincts to Java territory.
Seven conceptual hurdles you might face when learning a new programming language
Confession: my personal experience is almost the complete opposite of the title of this article. I actually started with C++ in college, moved to Java to teach AP Computer Science A, and then entered Python territory to work with all of the snazzy data science libraries available. Now that I’m working on a Java project again (a neural network package that is really pushing my understanding of the underlying math to its limits), I’m really paying attention to the little differences in how the languages work. In particular, I’ve been making notes about the places where standard Python habits can become huge roadblocks when traveling through Java.
This post is directed at Python programmers (in particular, data scientists) traveling in the opposite lane; I’ll detail some of the initial conceptual hurdles you may face when learning to adapt your Python instincts to Java territory. In particular, I’m going to be avoiding superficial differences like
camelCase or how Java requires you to end a statement with a semicolon while Python makes it optional. I’m also going to avoid diving too deeply into OOP vs. functional programming. The focus of this post is the way in which Java requires you to think differently about how to solve whatever problem you’re working on.
Although it may seem daunting, just remember that many programmers before you have successfully learned Java, so it absolutely can be done.
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