Data science is a hot field today, and the demand for data scientists is more significant than ever. A couple of years ago, Business Insider listed data scientist as “the number one job in America.” The position continues to place high in more recent job demand lists.
Programming languages are the most important, most often used tool in a data scientist’s repertoire. So, which of the two most popular data science languages gets the top spot? That would be Python, and we are about to show you why.
Read on to know the advantages of Python for Data Science over Java. Be careful: you may end up a Python convert yourself if you’re not already one! Let’s dig into more about programming languages and, specifically, Python vs Java.
An Overview of Data Science Languages
There are many programming languages (or data science languages, if you prefer) to choose from. Here, for instance, is a list of the top 10 most popular programming languages as of 2020.
Not all programming languages last, however. A quick Google search reveals plenty of articles and blogs that herald the impending death of selected data science languages. For instance, AnalyticsIndiamag.com predicts that the following languages will be gone in a few years.
Alert readers will notice that two of the possibly defunct languages also appear on the top 10.
Python and Java, however, remain consistently popular. Python, however, has pulled ahead in this rivalry. According to Statisticsdata.org, Python is more popular than Java as of May 2021, although, in the interests of full disclosure, C has them both beat.
But we’re here to talk about the Python vs Java question, so let’s move on.
Python or Java: The Eternal Question
But we’re here to talk about which to choose for data science - Python or Java; so let’s move on.
Difference between Python and Java - in general
Let’s compare Python and Java in a head-to-head competition, breaking the languages down by essential characteristics to see the clear advantages of python over Java when it comes to working on data science, AI and machine learning projects.
Extremely popular for mobile and web applications. Suitable for Desktop GUI apps, embedded systems, and web application services.
Favored for Artificial Intelligence, machine learning, and Internet of Things. Ideal for scientific and numeric computing functions.
Very lengthy and verbose. It requires ten lines of code just to read from a Java file.
Efficient. Requires just two lines of code to read from a Python file.
Object-oriented programming language. Compiled language.
Scripting language. Interpreted language.
Comparatively harder than Python, more significant learning curve.
Easy to learn, shorter learning curve.
Has a long history in enterprise. Java legacy systems usually are more extensive and more numerous.
Has less legacy baggage, although it’s the older of the two languages, released in 1991.
Since it’s a compiled language, it takes less time to execute code, making Java faster.
Python is comparatively slower because it’s an interpreted language that determines the data type at run time.
Static or Dynamic
As you can see, both languages have strong and weak points. They’re both popular, enjoy extensive support, and have their areas of specialty.
However, when it comes to data science, there are specific advantages of Python over Java.
Why advantages of Python over Java make it better for Data Science
Both languages are popular, but we must consider that we’re discussing data scientists today, and there lies the critical difference. Data scientists often work with Artificial Intelligence and machine learning, two rapidly growing disciplines, and Python works best for both.
Java is excellent if you’re designing web pages, but if you’re a data scientist working with thinking machines or automated functions, you need to use Python. These programming language usage statistics among data scientists reinforce the point.
Here are some additional interesting tidbits that contribute to the clear advantages of Python over Java, making it the best choice for data scientists:
- It’s good for web development. Yes, Java reigns supreme with web developers, but Python is also a great choice, making it a good tool with enough versatility for both Data Scientists and web developers. Consequently, if a data scientist wants to try their hand at web development, they don’t have to learn another programming language — Python has them covered! There are also many libraries and full-stack frameworks dedicated to web applications, considerably speeding up coding and making the entire development process more efficient. Here are some better-known full-stack frameworks for Python:
- It has lots of libraries. Python has a vast collection of hundreds of time-saving libraries and frameworks. Many of Python’s libraries focus on machine learning, big data, and data analytics. These libraries include NumPy, Pandas, and SciPy.
- Python is scalable. Scalability means flexibility, a trait that data science needs. Python offers programmers more options for problem-solving, usually involving new updates that are easily added in.
- Python has a large community. Extensive user communities are helpful because they provide advice, answers, workarounds, and new patches or content. Communities like Stackoverflow offer support for fellow Python users.
Could Python eventually supplant Java even in areas where Java dominates? The advantages of Python over Java concerning its applications in Data Science, Artificial Intelligence and Machine Learning is clearly a given. So, anything’s possible, although it can be challenging to change from the familiar to the new, especially if the usual does a good enough job. Why make unnecessary changes? For now, we know that data scientists prefer Python and its many advantages. It will be intriguing to see if the language can make sufficient inroads into web and app development and become the go-to language across all IT-related fields.
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