To work as a data scientist, one must have analytical and quantitative problem-solving skills and ability to act quickly in times, when it is direly needed.
However, being a data scientist does not limit it only to the ability to solve problems. In this age, when new languages are being developed so frequently, it is the need of the hour that the data scientists know the working of main languages.
If you have made your mind to study and work in the field of data science, then here are a few languages that you would like to start with.
Do not overwhelm yourself with many languages but start slow, get a hold of one language and then move to the next.
Pick the language that is more in demand in the marketplace.
Here are the Top 10 Programming Languages for Data Science
Guido van Rossum designed Python in the year 1991. Since its inception, it has been very popular and one of the favorite general purpose languages among the data scientists.
If you are a new programmer, Python is a language that is highly suggested to be learned at the beginning of language learning career.
It has lots of modules and community support for functionality.
So there will never be an issue of support system. It has a collection of libraries such a Google’s Tensorflow which makes it more interesting.
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2. R Programming
R was released in the year 1995. It is an updated version of S programming language. R has been improving in its functions day by day.
It includes a good range of open-source packages which are of high quality. It has an exhaustive library of all the packages be it for a quantitative application or statistical application.
R has the capability of handling the matrix algebra. The libraries included are ggplot2, and the main strength is data visualization.
It has many community programmers that contribute to the open-source. Its accessibility is free so that means you can learn it from anywhere.
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If you have worked on a computer, you are already well aware of the popular Java language. It is a general-purpose language, and it works on Java Virtual Machine.
Java backend is used to build many modern systems. It has this system where the developers can integrate the existing code base and the data science methods. It is a compiled language. Once you know the Python or R, you can consider learning Java as your next programming language.
Many companies need Java programmers as team members for sure.
Read more: 2 Major frameworks for Web development with Python
Julia is free to access programming language and has a good impact in the field of numeric computing.
Since its inception, it has been used by the financial agencies and that is why it evolved as a programming language that is good with numbers.
It has just-in-time compiling which gives excellent performance. Like Python, it can provide the capability of scripting and the dynamic type.
Even though it performs numerical based programs, it is designed for general-purpose programming as well. The base library of Julia is integrated with open source C.
Structured Query Language was developed in 1974, and since then, it has undergone many changes and improvements.
It is used to manage the relational databases by defining and managing them. It has an easy syntax that makes it fun to learn and it easily understandable.
If a company has to manage a large database system, it uses SQL for the same. It is a skill set that you will find useful at some point in time in your programming career.
It can also be used in a different type of applications. Integrating SQL with other languages can be very easy too.
MATLAB is developed by the Mathworks. It is developed in the mid-1980s. It is a paid programming language. It does the computation of numerical data.
It performs complex operations such as matrix algebra, signal processing, etc. It also has the capability of data visualization.
This language is used in the field of data science much often. This makes it an ideal language to learn.
Even though it is not for general-purpose programming, it is very useful for the numeric data.
It is a language well known in the field of mathematics and scientific matters which means that it is used in the different types of applications available today.
C is one of the oldest languages in the list of programming languages. It acts as a base of many new age languages.
If you want to learn about the basic of programming language, then starting with learning the C language is a good option.
The practical uses of C language are – it is used in the systems like car dashboards, televisions etc.
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Scala is a language developed by Martin Odersky in 2004. It has both functional and object-oriented approach which makes it a multi-paradigm language.
It runs on the Java Virtual Machine, and this makes integration with the Java language very easy.
If the developers are working with a large amount of data, then Scala is a good choice to work with. It is also good for data science.
If you have already the knowledge of Java, this will be an ideal language for you to learn.
Even though it is useful for the high amount of data, if you still use it, it provides the best productivity as compared to other languages.
F sharp is an open-source programming language which works cross-platform.
It is a reliable language developed by Microsoft and the contributors. It solves the computing issues which are complex by using robust and straightforward coding.
It is a favourite of many big companies as well as open contributors’ community. You can learn F sharp free from the internet.
SAS language is very popular in the community of data science.
It comes with the user-friendly Graphical User Interface that makes it easy for learning. It comes with lots of statistical functions that make it in-demand language.
If you want to work as a programmer in the analytics industry, it is a good option to learn SAS language.
Once you know this language, you can be confident about your career in the commercial analytics industry.
The programming languages mentioned above are presented here keeping in mind a few of the specific things that are important for data science. The language should be productive in its use whether the companies or the open contributors are using it. It should provide whatever is expected of the language.
The performance of the language should be error-free and quick. If the data is high in volume, the language should be able to manage it easily in less span of time. If you are a data scientist or planning to become one, then these languages will be beneficial for the growth of your career.
Start by choosing one language that interests you and learn it until you become efficient in one. Once you know one language well, and then proceed with the next language. It does not matter how many languages you know but what matters is that how proficient you are in the language of your choice.
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