What is the best language for Data Science?

Data Science is the ‘sexy profession’ of the 21st century. There in no doubt about it. Professionals choose it for the challenging combination of their statistical & quantitative skills with their ability to apply programming in the real-world.

If you are emerging in the field right now, there are a few things to take under consideration. One of them being which programming language to specialise in.

I am not a programmer myself, but was interested in understanding the main differences between programming languages. As thus, I decided to share the information I collected on the most known programming languages. Below the list with a short analysis on the pros & cons.

1. Python

Python was born in 1991. It’s a popular general purpose language. Widely used within the data science community.

License

Completely Free.

Pros

• Easy language to learn. Ideal for first learners!
• Offers an extensive range of purpose-built modules.
• Large & active community support.
• A solid option for advanced ML applications.
• Libraries such as Scikit-learn, Keras, PyTorch and Tensorflow make Python a very exciting language to work in for ML and Deep Learning.

Cons

• Python is a dynamically typed language, type errors are to be expected from time-to-time.
• R may be a bit better for statistical and data analysis purposes.2. R

Released in 1995 from the older programming language S.

License

Completely Free.

Pros

• Complete Rstudio IDE
• Fantastic for statistics & data science purposes.
• Very comprehensive, in-built statistical functions and methods.
• Excels at data visualization applications.
• Being open source has created a very large & active community.

Cons

• R is not a quick language.
• Not great for general purpose programming.
• There are some unusual features that may catch out programmers who have experience with other languages.

3. SQL

Born in 1974. Language for accessing and manipulating languages.

License

Some implementations are free, others patented.

Pros

• Very efficient at querying, updating & manipulating relational databases.
• Very readable language.
• Used across a range of applications. This makes it very useful to be familiar with.
• You can integrate SQL with other languages.

Cons

• Not appropriate for solving data science problems.
• Analytical capabilities options may be limited.
• There are many different implementations of SQL. It may make inter-operability something of a headache.

4. Java

A popular and general purpose language, supported by Oracle Corporation.

License

Version 8 is Free. Legacy versions are patented.

Pros

• It is everywhere! Many systems & applications are built on a Java back-end.
• Ensures type safety. Perfect for mission-critical big data applications.
• High-performance, general purpose & compiled language.

Cons

• Java’s wordiness makes it an unlikely first choice.
• Low number of libraries available for advanced statistical methods in Java.

5. Scala

What you need to know

Released in 2004, a multi-paradigm language. It enables both object-oriented and functional approaches.

License

Completely Free.

Pros

• Ideal choice of language for those working with high-volume data sets.
• Multi-paradigmatic.
• Runs on a JVM, allowing inter-operability with Java.

Cons

• Not a straightforward language for beginners.
• The syntax and type system are often described as complex.
• Productivity may be much higher using other languages — i.e. R or Python.

6. Julia

What you need to know

Released 5 years ago, is strong in numerical computing.

License

Completely Free.

Pros

• Offers good performance, with simple, dynamic-typing & scripting capabilities.
• Purpose-designed for numerical analysis.
• Easy to read.

Cons

• Instability when using packages.
• Limited packages & small development community.

7. MATLAB

An established numerical computing language used throughout academia and industry. Developed and licensed by MathWorks.

License

Patented.

Pros

• Well-suited for quantitative applications with sophisticated mathematical requirements.
• Great inbuilt plotting capabilities for Data Visualisation
• Taught as part of many undergraduate courses in quantitative subjects.

Cons

• Patented , expensive licence.
• Not an obvious choice for general-purpose programming.

I hope this guide helps individuals who are considering following a career in Data Science. Your choice should be based on your usage requirements in terms of generality vs specificity, as well as, your personal preferred development style of performance vs productivity.

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