Best Languages to Learn as a Data Scientist

Data Scientist

Technology is a fast-paced industry where everything changes very quickly. Each year, we have new innovations that are introduced including programming languages, frameworks and technologies. With this much innovation being churned out every minute, programmers, developers, and anyone working the field of tech are expected to constantly remain on their toes. In this article, we are going to list the 9 best languages to learn for Data Scientist.

For developers and programmers, it has become essential to constantly learn new languages and skills to remain relevant. And if that is not enough, each project comes with its own set of demands and tool set.

This is very prominent in the field of data science, where data is always being manipulated and changed depending on the requirements of the scientist. This makes it difficult to pick the right language and learn it.

What is Data Science?

Before we can talk about data science, let’s take a quick look at what data is. Data is basically information or rather a collection of information. It is simply a collection of facts and statistics on a given subject.

Data science is simply defined as, “concept to unify statistics, data analysis and their related methods” in order to “understand and analyze actual phenomena” using data. It uses technique and theories from different fields such as mathematics, statistics, information science, and computer science.

Data science has recently received more interest with the introduction of Machine Learning that is taking it to new heights. The integration of machine learning is allowing analysts to understand more complex problems and get more detailed insights from the data. However, to receive these insights it is important to be able to understand the programming language.

This is a great list for newbie data analysts that are looking to pick from the list of confusing languages as well as intermediate analysts who are looking to pick up a new language.

1. Python

Python isn’t exactly a new language, it was introduced in 1991, but since then it has become a popular general programming language that can be used for multiple projects. Because of its flexible writing style and ease of learning, Python has become one of the most popular programming languages for data science. It is a great tool for medium-scale data processing and has the advantage of a rich community, toolkits and vast features. Packages such as pandas and Tensorflow make Python a great language for machine learning applications.

2. R

R has become another popular language for data science and if that’s not enough, it also has a complete runtime environment that has been designed specifically for data science and data visualization. This language has been around since 1995 built on the older S language. R offers a range of high-quality, domain specific and open source packages for every quantitative and statistical application. R offers packages for applications such as non-linear regression, advance plotting, neural networks and can also handle matrix algebra.

3. SQL

SQL, short for Structured Query Language, is used to define, manage and query relational databases. It has a declarative syntax that makes it a very readable language and also exceptionally easy to learn. SQL is also excellent at querying, updating and manipulating relational databases. It is commonly used for large databases because of its fast processing time.

4. Go Lang

This isn’t a common one, but Go Lang, a powerful language by Google, is starting to become a great language for data science. It was introduced in 2009 and is a statically typed, compiled language built on C with memory safety, garbage collection, structural typing, and CSP-style concurrency. It offers great libraries such as Gorgonia and Gonum that work exceptionally for machine learning but these do take a little getting used to.

5. Java

Java is a popular general programming language and is one of the most practical languages currently designed. It is used by a large number of companies and is commonly used for building backend systems and desktop apps. While Java does not provide the same quality of visualizations as R and Python, it is great for building large systems and writing code that works the same across multiple platforms. It is also great for ensuring type safety.

6. F#

F# is a mature, cross-platform, open-source, and functional-first language that allows companies to tackle complex programming languages using simple, maintainable, and robust code. It can be used for a wide range of applications as it combines efficient execution, REPL-scripting, powerful libraries and scalable data integration.

7. MATLAB

MATLAB is fast, stable and offers algorithms for complex math problems. It was designed for particularly this purpose by MathWorks and is now an established numerical computing language. MATLAB is great for quantitative applications with requirements such as signal processing, Fourier transforms, matrix algebra and image processing. It works great for statistical analysis and is extremely popular in that field.

8. Julia
Julia is a high-level programming language that has been built for high performance numerical analysis and computational science. Julia is a fairly new language that was released about 5 years ago for numerical computing. It is a JIT (just-in-time) compiled language that comes with features such as dynamic typing, simplicity and scripting capabilities of Python. It also offers good readability and is easy to learn. The base library written in Julia can be integrated with open-source C and Fortan libraries for different problems such as linear algebra, random number generation, signal processing, and string processing.

9. Scala

Scala is a multi-paradigm language that includes both object-oriented and functional approaches. It runs on the JVM (Java Virtual Machine) and released in 2004 for multiple functionalities. Since Scala runs on the JVM, it allows the language to run on any system that runs Java. The language is highly flexible and functional and is becoming popular for high-volume data sets or building high-level algorithms.

Data Science is not an easy job and definitely requires scientists to constantly update their skills with the latest technologies and languages. With more languages and frameworks that are constantly coming in, the field of data science has been revolutionized. These languages are some of the most popular languages in the world of data science.

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