Data science is among the trendiest fields in technology. The demand for data science professionals is huge – so much so that Glassdoor named it the number-one job in America for four consecutive years. 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.
In this post, however, I’d like to compare two languages that lead Stack Overflow’s Top Software Development Languages survey – Python and Java. Let’s discuss the benefits, drawbacks, and applications of these technologies in data science.
Python: A Popular Choice in Academia and Enterprise
At the moment, Python dominates data science. According to a Kaggle survey, 93% of data scientists use the language – SQL’s 54% and R’s 46% are bleak in comparison. 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.
What is the reason for such widespread use of Python in data science? 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 (e.g., BeautifulSoup). 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 among my peers aren’t happy to manually white-space their code.
Wide Data Modeling Toolset
Data modeling 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 modeling operations – numerical modeling, 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 modeling – 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.
The community of Python learners is so vibrant and devoted that, if you ever ask, “Which programming language should I learn first?” on a tech forum, without a doubt, you’ll get a handful of replies mentioning Python.
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.
Facilitates Algorithm Deployment
When tech team leaders want to start leveraging data science algorithms’ power, rather than changing the entire infrastructure of their platforms, they prefer hiring candidates who are skilled in Java and can connect the algorithm to the rest of the codebase.
That’s why coding in Java is and will be a prerequisite for most DS positions in enterprises. Another reason for tech team leaders to prefer Java/Python over Python-only developers is their workplace flexibility.
Coders who are skilled in both languages can be easily allocated to a new project or a task.
A Lot of AI and Data Handling Libraries
By the robustness of data science infrastructure, Java is on par with Python. There are a lot of frameworks and libraries that help developers streamline and automate workflows. Here are some of the most widely used Java-written data science tools:
- ADAMS – a workflow engine used in machine learning.
- Deeplearning4j – a robust deep learning library for Scala and Java distributed under an open-source license.
- Mahout – a Java-based machine learning framework, a part of the Hadoop ecosystem.
- Stanford Classifier – a tool, written in Java that’s used to group items into k-classes.
Conclusion
When it comes to choosing a technology for data science, Python and R are still top choices for many developers. However, it doesn’t mean that aspiring data scientists should disregard Java as a part of their learning curve. We talk about Java mainly when it comes to deploying DS algorithms – however, it has plenty of standalone applications in machine learning and artificial intelligence.
Although learning two programming languages at once is not easy, with enough determination and a thoughtfully selected list of resources, you shouldn’t have issues mastering both Java and Python and becoming a skilled, versatile data scientist!