Top 7 Programming Languages For Data Scientists To Learn

Programming Languages For Data Scientists
Data science is a field that makes use of algorithms, processes, scientific methods and systems to extract insights and knowledge from data whether structured or unstructured. When it comes to a programming language, it is considered like the superpower of any developer. In the past centuries, there wasn’t much data being produced but in this century, there is an unlimited amount of data being produced on a daily basis because internet technology has become common now. Hence, in order to survive in today’s competitive market; data scientists have no other option but to upgrade their skills so as to meet the industry’s demand. They should also acquire the super-power of programming languages.

Top 7 Programming Languages Data Scientists Must Learn

If data scientists learn the following 7 languages then they can generate a huge amount of opportunities for themselves. Being a data scientist, you already have mastery in your field but put some efforts to learn these languages and then you will see the difference yourself:

1. Python:

Python is a general language that can be used to build desktop and web applications. It is an amazing Programming language that provides and eases of use function. Because of this reason, Python language is widely used to develop complex applications based on heavy software.

Reasons why data scientists should learn the Python programming language:

Better Data Analytics Tools

Data analytics is actually one of the important features of Data Science. In assessing and evaluating the performance of any business, data analytics play a major role because they provide useful information about various matrices. In order to build data analytics tools, Python programming language is considered as the top of the list item.

Ease Of Use

One of the reasons that python has become the fast-growing language of the world is that it is easy to use. This language is much easier to learn as well as use. Data scientists can easily use Python for simplifying complex data sets and making it understandable.

Deep Learning Algorithms

Another reason to learn Python is that it helps data scientists to build deep learning algorithms and such algorithms are very effective for organizations in different ways. Actually, Deep learning algorithms create such networks that can evaluate the behaviour of the human brain. Hence such algorithms are useful for biasing input elements and for creating desired output.

High Flexibility

This programming language provides high flexibility to data scientists so they can work on any given problem and can solve it in a very short span of time. Hence, if you want to quickly, shortly and efficiently solve any problem then python is the best choice.

Worldwide Popularity

Python programming language is famous throughout the world and that means data scientists must learn this language. Once they will have learnt Python; many new doors of opportunities will be opened for them in their career.

Based on the above reasons, data scientists should not miss learning this language.

2. Structured Query Language (SQL):

SQL is a domain-specific language that can manage and retrieve data from a relational database management system. It is actually an open-source programming language and it is the foundation of the data science field. Every data scientist must learn Python because it will make it much easier to communicate with relational databases. The reasons why you should learn SPL is that it is helpful in the deep understanding of datasets, simple to learn, manages huge datasets, can easily integrate with scripting languages and most importantly, it brings new opportunities for data scientists in the data science field.

3. R:

Another powerful programming language is R and it is mainly used for statistical computing and data analysis. This language was developed in the 1990s and so far, much new advancement has been made to improve the user interface of R language. R has become a common programming tool for a data scientist in almost every industry from marketing to banking to insurance to pharmaceutical development. Actually, R and Python can pretty do the same tasks but for making a statistical analysis of the huge amount of data simple, R provides the following features:
  • Time-series analysis
  • Linear and non-linear modelling
  • easy extensibility to other programming languages
  • clustering
Those data scientists who have already learn Python will have no difficulty in learning the R language.

4. MATLAB:

when it comes to MATLAB, it is being applied widely in a number of industries, for example, finance, industrial automation, energy and medical devices and its use is very common in the field of data science as well. This language is useful for mathematical operations-basically. MATLAB has rich ML libraries. Hence, this language offers capabilities for deep learning and it also provides end-to-end integrated workflow.

MATLAB programming language is even perfect for matrices calculation and no other language can compete for this one for this operation. Matrices are fundamentals of basic algebra and are widely used to describe many ML algorithms. Matrices fundamentals are also great for dealing with images and MATLAB is the best solution for matrices calculation. Some common reasons top use MATLAB are numerical integration, differential equations, statistics, filtering, Fourier analysis, optimization and linear algebra. All these operations are used in data science in some way. Hence, every data scientist must invest time in learning MATLAB programming. Not only it will bring ease for them in their field but it will bring new opportunities as well.

5. JAVA:

One of the oldest programming languages includes JAVA. Although it one of the oldest but it is still in use in the 21st century as well. There are many data science tools that are written in JAV for example, Hadoop, Spark, Hive, etc. In order to learn these tools, the data scientists must learn JAVA language. Its native scalability, big data frameworks and machine learning libraries allow for accessing huge amount of storage. Hence, JAVA is the most basic thing for data scientists to know. Actually, JAVA programming language is very effective for making application scaling much easier. Here are the top reasons why data scientists should learn JAVA:
  • great toolsets
  • Java is strongly typed
  • Java virtual machine
  • JVM has Scala
  • Java is fast
  • Scaling of applications
Go ahead, invest some time in learning JAVA and add up to your skills and opportunities in the field of data science.

6. Scala:

Scala is actually a multi-paradigm programming language that supports functional as well as object-oriented programming. It can work wonders for data scientists as data scientists can use this programming language in their field in many different ways. It is a high-level programing language and it is based on the JVM platform.

This language is simply perfect for dealing with a huge amount of data and it is because of the reason that it is considered as highly scalable. It is basically an extension of JAVA language and adds some more features in JAVA. Not only it is effective for anonymous functions but it is equally effective for high order functions as well. Hence, if data scientists learn Scala then it will serve as a positive step towards the success of their career.

The good thing about Scala programming language is that it is super easy to learn for those scientists who have already learned JAVA language.

7. Julia:

Whether you already know about Julia programming language or not, let me tell you that this language has also been found as the point of interest for data scientists. It is actually an open-source programming language that is highly efficient and intuitive. The reason why data scientists are showing great interest to learn Julia programming language is that it works faster than Python, R and many other programming languages. Hence, it makes things much easier and quicker for data scientists as they need to deal with a huge amount of data. This language was created in 2009 and in a very short span of time; it has earned such great fame in the world of programming.

Julia has a math-friendly syntax that makes it super simple for programmers and even for non-programmers to use math formulas without any complications and to make calculations within no time.

As Julia is super-fast and easy to use, so you must spend time to learn this language being a data scientist and believe me that you will enjoy its fruits later on in your data science career in many ways.

Conclusion

We on the behalf of GearWisdom tried to present top 5 programming languages for data scientists that may help them in different ways throughout their career. All these languages have their own individual benefits for example; these provide faster and better results as compared to other languages. The field of data science is changing day by day according to the changing trends in the technology and data scientists have to equip with the latest skills so as to survive in the industry. What you had learned during studies was just the basics of Data science but keep adding to that knowledge is important. Hence, you are recommended to learn either all of these languages or some of these languages.

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