Since they’re 2 of the easiest programming languages to learn (and most important), it’s time for Career Karma to weigh-in on the Python vs Java debate.
Python is routinely used by both middle schoolers writing their first for loop and top-notch machine learning engineers creating deep convolutional neural networks to push the state of the art in their fields. Any tool which capably spans that range of expertise is worth looking at.
Java, on the other hand, is often simply touted as the most widely-used coding language in history, a claim which, as far as I can tell, is entirely plausible. Any tool with that kind of staying power is worth being familiar with.
Given their astonishing variety of use cases and the fact that many careers will require you to learn and write them at some point, it’s only natural to wonder about how they compare to each other when choosing a language.
Which works better for tasks like building websites or training AI models? Which is better for game development? Which would it make more sense to focus on if you want to be a front end engineer vs a data scientist? Which has a cleaner semantics and better code readability?
In this article, we’re going to probe these and related questions in an effort to give you what you need to succeed.
Python vs Java for Web Development
Web development is the process of conceptualizing, building, and maintaining web sites or web applications. ‘Front end’ tasks are those that directly impact the user, and include considerations like making applications attractive and easy to use. ‘Backend’ tasks are those that most users never see, and involve things like shuttling data between websites and servers.
Python for Web Development
Being that you can’t turn over a rock without finding a 3rd-party Python library lurking underneath, it’s understandable to ask ‘what are the advantages of Python for web development’. In this sphere, Python is almost exclusively used for backend, server-facing work.
This is almost always done with the popular Python framework Django. With its famous ‘batteries included’ approach to making developers more productive, Django does a lot to make backend web development easier and more efficient.
Java for Web Development
Since its introduction by James Gosling in 1996, Java has grown to be a stupendously popular programming language, used in data science, machine learning, web development, and almost everything else.
As with Python, Java’s use in building web applications is mostly confined to backend, server-facing work. A number of Java ‘flavors’ are available for these tasks, including Java Enterprise Edition, the JavaServer Pages Standard Tag Library, and the JavaServer Pages Standard Tag Library.
Where Python has one single dominant platform for backend development work, Java has many. This means you have lots of options for accomplishing whatever goal you’re currently tackling, but can be a little overwhelming.
So Should I Learn Java or Python for Web Development?
The short answer is that you should learn both. Neither Python or Java is going anywhere anytime soon, and if you plan on building a career as a full stack web developer you’ll be well served by learning to effectively use each.
If you’re not willing to do that, or want to choose a single technology to focus on right now, then the thing to do is to find out whether Django or one of the Javas is better suited to the project you’re wanting to work on. Though Java and Python are general-purpose languages, sometimes better tools exist in one language than the other.
Alternatively, if you’re planning on applying to a particular company, find out whether they prefer Java or Python for web development, and learn that.
And though this is going to take us a bit afield it’s worth bearing in mind that, while Python can be used for web application development, Java is one of the two primary languages for the Android platform. As Android is one of the most popular operating systems in the world, this is hardly a small difference. Python can technically be used for mobile app development, but this requires more workarounds.
Java vs Python for Data Science
Data science is often cited as being among the fields that will define the future. If it’s true that ‘data is the new oil’, then it’s probably worth examining the different approaches and tools for doing data science afforded by Python and Java.
Python for Data Science
Python is simply the standard language for doing data science today. While there are places like academia where R might by more popular, and languages like Scala and Java are indeed better for certain tasks, you’d be hard pressed to find many data science roles that don’t expect you to be proficient in Python.
And, as we’ve pointed out numerous times in this article, Python has many outstanding 3rd-party libraries for almost every conceivable task. I have to say that this is one of those things that I’ve found remarkable in my job as a data scientist. Whether it’s getting results from a search engine, parsing datetimes, or processing tricky data, I can count on one hand the number of times there wasn’t already a library out there to do whatever it was I was trying to do.
Java for Data Science
If Python were taken off the table and I were forced to name a 2nd-most-popular data science language, it would probably be Java. I’ll even go further and say that there are absolutely domains, like extracting truly enormous amounts of data, in which the fact that Java is a compiled language makes it the better choice.
And most of the advantages of Python are advantages of Java, if perhaps not always to the same extent. Java also has many 3rd-party libraries, has also been around for a long time, and also has a large and active community that has grown up around it.
So, Should You Learn Python or Java for Data Science?
Honestly, learn them both.
I’d like to offer a last piece of advice as we close out this section: completely apart from the actual coding part of data science, you need to learn to work with relational databases because almost everything is stored in one. For this you’ll need to master Structured Query Language or SQL.
Trust me, no data science resume is complete without it.
Oh, and throw in Scala and R for good measure. It’ll only make you better.
Python vs Java: Machine Learning
You can hardly say the phrase ‘machine learning’ without conjuring up images of arcane mathematics, powerful algorithms, and cutting-edge computer science technologies. And these are indeed characteristic of the field. But whether you study machine learning on your own or at a data science bootcamp, it is also a concrete way to do high-impact work that’s exciting, challenging, and rewarding.
Given all this, it isn’t surprising that there’s been a growth in the number of machine learning jobs and the enthusiasm for them in the popular imagination. If you want to prepare for a career in this branch of artificial intelligence, a good place to start is to review the capabilities of both Python and Java.
Python for Machine Learning
Given the virtues enumerated above, it’s no shock that Python has found widespread adoption in the machine learning community. Using Python for machine learning can boil down to building actual algorithms in raw Python, or doing the same thing with a popular library like Sklearn or TensorFlow.
As part of the Galvanize Data Science Immersive, I carefully studied several ways of using Python to build neural networks from scratch. This taught me a lot about the nuts-and-bolts of neural networks, which I wouldn’t have learned if I had let a software package do all the heavy lifting for me.
While working on my Capstone, however, I turned to TensorFlow to accelerate the process of building bigger and more powerful neural networks to generate text.
Having done machine learning the long way and the short way, I can say first hand that Python significantly reduces the pain associated with the process. Learning Python as a start to becoming a machine learning engineer is a great choice.
Java for Machine Learning
One reason to use Java for machine learning is simply because there is so much of it around. Many companies have huge Java codebases, and much of the open-source stack for processing big data is written in Java. This means that Java-based machine learning projects will likely be easier to integrate with existing repositories.
Like Python, there are also plenty of 3rd party Java libraries for machine learning. Deeplearning4j allows for the creation of any kind of neural network, and furnishes support for popular algorithms like linear regression and k-nearest neighbors. If you don’t like that, you could also go with Neuroph, and alternative Java library for neural networks. Hyperparameter tuning–a fancy phrase which points at an exceptionally important part of getting machine learning algorithms to run effectively–can be handled with the Arbiter Java library.
So Should I Learn Python or Java for Machine Learning?
My personal verdict is that you should use Python for machine learning, but there is absolutely a case to be made for going with Java. As recommended before, you should probably consider which language works best for the projects or companies you are looking at.
Java vs Python: General Language Features
With two languages this popular and widely used it can be hard to catalogue all their similarities and differences in tidy sections. In the interest of giving you the maximum information possible, I’ve included a miscellany of similarities and differences below, in case we missed anything in previous sections.
Technical Differences Between Python and Java
While Java and Python are both high-level OO languages, there are some important differences between them. Arguably the biggest is that Python is interpreted while Java is compiled. An interpreted language is one that can be run line-by-line, while a compiled language has to be translated into bytecode before it can be executed.
Java specifically compiles down to what is called the Java virtual machine, a kind of abstract virtual ‘computer’. Other languages, like Ruby, Scala, and even Python can be compiled down to the Java Virtual Machine, which allows for an astonishing degree of cross platform support.
A common, related question is whether Python or Java is faster. One of the tradeoffs for being a compiled language is that Java code tends to be faster than Python code, even when compile time is taken into account.
Java’s famous ‘just-in-time compiler’ performs compilation steps only when they need to be run. This, along with features like support for concurrency, is one of the things that makes Java an excellent language for compute-intensive tasks.
Though both languages have many third party modules to extend their functionality, the Python ‘standard’ (read: core) library is more extensive than Java’s standard library. This can impact how quickly a novice programmer is able to pick these languages up.
Another thing worth keeping in mind is that Python and Java have different type systems. Python is dynamically typed while Java is statically typed. Without going too far down this long and fascinating computer science rabbithole.
What this means is that in Python a variable can contain a piece of text and, later, this same variable can be overwritten with an integer.
The variable ‘types’ can be changed dynamically, hence ‘dynamically typed’.
In Java, once a variable has been assigned a string it can’t later be overwritten with an integer. Variable types are static, hence ‘statically typed’.
Finally, in Python all classes are public classes, in the sense that their variables and methods are visible to the rest of the program. This is not so with Java, in which classes have to be specifically designated as public classes for this to be true.
While this could seem like a rather arcane bit of trivia, it can impact how secure a program is, whether code is easy to read, and many other aspects of working with the language.
Python vs Java: Jobs
There are plenty of Python jobs available, and more springing up all the time. The payoff for learning Python, therefore, could be extremely good.
Python is known for its famously straightforward syntax and consequently being easier to read than many other languages; I’m not sure it’s quite as natural as a language like Ruby, but it’s up there.
Because of this, coders have taken Python and expanded it into something that’s almost a platform. There are 3rd-party Python libraries to do almost everything, and Python frameworks for everything from systems programming to game development.
Relative to languages like PHP and C#, Python adoption has grown at a staggering rate, with little evidence of slowing down. And Python programmers earn salaries that are comparable to developers working in other languages.
On the other hand, Java was one of the earliest and most successful languages aiming at cross platform support. So, like Python, there are more 3rd-party Java libraries than you could hope to explore if you lived to be as old as Gandalf.
Some of the largest companies in the world use software that is either written in Java or relies heavily on it. But Java is also growing in popularity as a language for data science and machine learning, two fields becoming more prominent everyday.
For these reasons, Java isn’t going anywhere. Learning it would be a great use of your time.
But what if you want to know how Java and Python developer salaries stack up? It depends upon the sources you consult, but based on my research a mid-level Python or Java developer can expect to earn a similar (and respectable) salary in the low six figures.
Less important than the language is your technical proficiency and real world experience. It also never hurts to have done some open source programming.
Java vs Python: Interviews
Let’s face it, interviews are one of the most important parts of landing a job in technology. While it’s not impossible to overcome bad first impressions and subpar interviews, it’s extremely difficult.
Instead, wouldn’t it be better to master the interview process so you don’t even have to worry about it? Of course it would! With that in mind, let’s take a look at some questions commonly seen in Python interviews.
- Q: How are lists and tuples different in Python?
- A: Lists are slower than tuples and mutable, and they use brackets instead of parentheses.
- Q: What does it mean to say that Python is an interpreted language?
- A: It means that Python isn’t compiled, and instead executes code line-by-line at runtime.
- Q: What’s the difference between global and local variables in Python?
- A: A global variable is one that’s declared outside a function and accessible by the whole program, a local variable is one that lives in a function and is only active there.
These questions are entirely software-related and require an understanding of how Python handles variables and basic data structures.
What does this process look like in Java?
- Q: How is Java platform-independent?
- A: Java compiles down to a byte code that is executable on any hardware system.
- Q: Why is Java not totally object-oriented?
- A: Java contains a number of primitive data types that don’t act like standard objects in object-oriented programming.
- Q: Provide one difference between heap and stack memory in Java.
- A: Stack memory is only accessible by one thread, while anything stored in heap memory is accessible by anything in an application.
You can see that these are more likely to focus on hardware and distinctive features of Java.
Now, you could in principle encounter almost any question in an interview for either Java or Python, but hopefully seeing a couple of standard questions for each gives you some sense of what you can expect.
By and large, interview questions are going to challenge you to demonstrate basic proficiency with the language and may push into deeper topics like the mechanism which makes Java platform-independent.
For practice, I recommend solving as many problems as possible through both whiteboarding and pair programming. Both are popular vehicles for assessing the abilities of potential candidates.
A Final Word on Python vs Java
We’ve covered a lot of ground in this comprehensive overview, and I sincerely hope it has helped. The truth is that Java and Python are both great languages with more than enough to recommend them. As the proverb goes, the first step is often the hardest, so I recommend diving in over agonizing about which language to learn.