Kotlin or Python: Which Language is Better for Data Science?

Comparative Analysis of Kotlin and Python: Superior Language for Data Science Applications

Data science spans diverse fields such as mathematics, statistics, and computer science, making it a multifaceted discipline. With the rapid expansion of big data and the increasing importance of data-driven decision-making, choosing the right programming language for data science is more important now than ever before. This article aims to compare two popular languages – Kotlin and Python to help you find out which one is better for data science endeavors.

Kotlin Overview

JetBrains developed Kotlin, an open-source, statically typed programming language. Originally designed for Android app development, Kotlin has gained traction in the data science community due to its expressiveness, safety guarantees, and interoperability with Java. Kotlin's adoption in the data science realm is still in its infancy, yet it offers compelling advantages over established languages like Python.

Python Overview

Python is a dynamically typed, open-source programming language that enjoys widespread popularity among data scientists worldwide. Known for its simplicity, readability, and extensive library ecosystem, Python provides a friendly entry point for beginners while supporting advanced data science concepts. Python's dominance in the field of data science is unquestionable, having served as the backbone for countless research papers, academic courses, and commercial products.

Ease of Use

Python's simple syntax and straightforward structure make it an accessible choice for aspiring data scientists. On the contrary, Kotlin requires familiarity with Java conventions and adherence to strict typing rules, which might deter beginners. Nevertheless, Kotlin's expressiveness and safety guarantees compensate for its slightly steeper learning curve.

Library Ecosystem

Python's extensive library ecosystem, comprising NumPy, SciPy, pandas, scikit-learn, and many more, caters to virtually any data science requirement. Contrastingly, Kotlin's data science library ecosystem is comparatively smaller, though it continues to grow steadily. Noteworthy libraries include ktlxnet, kotest, and kotlinx serialization.

Performance

Python's dynamic typing and interpreted execution result in slower performance when compared to Kotlin's static typing and Just-In-Time (JIT) compilation. However, Python's performance disadvantage diminishes when working with small to moderate-sized datasets. When dealing with large datasets, Kotlin's performance advantage becomes apparent, owing to its lower overhead and higher optimization opportunities.

Community and Documentation

Python's vibrant community and abundant documentation resources ensure quick resolution of issues and easy discovery of relevant materials. Kotlin's community is growing at a steady pace, albeit not as expansive as Python's. Both languages benefit from active developer communities and ample documentation resources.

Production Readiness

Python's maturity and ubiquity make it a preferred choice for production-ready data science applications. Kotlin, despite its impressive features, lacks the same degree of production readiness as Python. That said, Kotlin's adoption in the data science sphere is gradually gaining momentum, and it could become a serious contender shortly.

Ultimately, the choice between Kotlin and Python for data science largely depends upon the specific requirements of your project. If you prioritize ease of use, extensive library support, and a thriving community, then Python would likely prove to be the better fit. Alternatively, if you seek improved performance, stronger type checking, and increased production readiness, then Kotlin merits closer examination. Regardless of your final decision, both languages offer valuable contributions to the data science community and warrant further investigation.