Java or Python: Which One Should a Data Scientist Learn?

Data science is among the trendiest fields in technology. The demand for data science professionals is huge

Despite the buzz it generates, data science is intimidating for many programmers since it requires a strong mathematical backbone and is unapproachable for mathematicians because of coding prerequisites.

That’s why the discrepancy between demand and supply in data science is vast.

There’s a word in the street that, if you want to acquire skills that’ll land you jobs, data science is your best option.

At the start of your data science journey, you will need to choose a programming language to run algorithms. There are many programming languages developers use, such as R, Clojure, Julia, or Scala.

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Python: A Popular Choice in Academia and Enterprise

With three out of four programmers choosing the language for DS projects, it’s clear that the love for Python in the tech community is strong. Let’s name just a few:

Ease of Data Collection

Data gathering lies at the core of data science. The ability to process large sets of information in different formats determines any scientist’s next project’s efficiency and success.

In that respect, Python is a powerful choice: it supports the most popular data formats (CSV, JSON, TSV, and more), and there are many libraries to help automate the process. A robust data-gathering infrastructure plays a huge part in Python’s emergence as a default language for machine learning and AI.

Object-Orientedness

Learning the concepts of OOP is a part of most computer science curriculums. Most languages developers initially learn are object-oriented: Java, C, and others. That’s why, when working on DS projects, programmers would prefer using an object-oriented language as well – Python is one.

The object-oriented nature of Python makes it much easier to learn than Scala or R. I should mention that Python isn’t A+ when it comes to the convenience of coding – for example, many of my peers aren’t happy to manually white-space their code.

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Wide Data Modelling Tools

Data modelling is an essential part of executing any project since it allows developers to reduce the dimensions of a data set and increase algorithm execution speed. There are a lot of data modelling operations – numerical modelling, scientific computing, and others.

Having the infrastructure to power through this process is useful for developers – that’s where Python fully hits the mark.

The language offers tools to streamline data modelling – NumPy for numerical operations, Scikit Learn for applying ML algorithms to a data set, or SciPy for scientific computing.

Ease of Learning

One of the reasons developers are using Python more than other programming languages is that more developers know how to code in Python. The technology is included in most university CS curriculums and boasts many textbooks, online courses, and tutorials.

Java: A Programming Language We Love to Hate But Can’t Live Without

Many developers are hesitant to learn Java – either because they feel intimidated by a sea of learning material or because they don’t agree with the executive decisions Oracle makes (like suing Google for copyright infringement).

Also, since Java has been around for so long, it no longer rings fresh or exciting to programmers. Having said that, as you browse data science job openings, you’ll mostly see Java and Python listed in a list of required skills. At the end of the day, the language plays an essential role in data science and comes with a handful of benefits:

The Backbone for Data Science Tools

One of the reasons to learn Java for data science is that it’s the language at the base of the Hadoop Ecosystem. Even the tools that aren’t directly built on Java (like Storm or Spark, all of which are Scala-based) run on Java Virtual Machine. Thus, having a solid ground in Java programming will help you work faster and make the most of the instruments at your disposal.

High Performance

Although Java has its weaknesses (e.g., unparalleled code verbosity), it’s a cut above Python in code speed and scalability. Since Java is compiled where Python isn’t, it executes the application code considerably faster. As for scalability, Java beats Python in the following:

• Multi-threading support.

• Security. A lot of developers prefer building large-scale tools in Java because they can use cryptography, complex authentication, and access control.

• Reduced number of runtime errors – as a statically-typed language, Java has a type of safety system that encourages developers to proofread their applications.

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