What are the best programming languages to learn in 2020 for a data scientist
By: Zahra Elhamraoui
Tags: Sql, Python, R, Life, Matlab

A lot of media hype is actually around data science and machine learning, in the recent years make it a destination for freshly graduates and people who want to switch their career into data science, and the time a programming language takes for a complete beginner to make a complete real word project is a crucial thing, what are the demand and the overall salary that you can make with this programming language in 2020? and what productivity is going to be using that specific programming language?

Python

Python is becoming more and more popular because of plenty of reasons. It is even considered that it is mandatory to master the Python syntax before doing anything interesting like data science. Though there are plenty of reasons to learn Python, one of the key reasons is that it is the best language to master if you want to analyze the data or get into the field of data analysis and data sciences. In order to start your data science journey, you will have to first learn the bare minimum syntax. After that, you would have to focus on developing structured projects. Then, you may want to start developing your very own, free-form projects.

Over 83% of the almost 24,000 data professionals used Python, use python on a daily basis.

It Is also one of the best-paid programming languages in the world with over 122k annually.

As of May 5, 2020, the average annual pay for a Python Data Scientist in the United States is $122,233 a year.

R Language

R is an integrated suite of software facilities for data manipulation, calculation, and graphical display. It includes

an effective data handling and storage facility,

a suite of operators for calculations on arrays, in particular matrices,

a large, coherent, integrated collection of intermediate tools for data analysis,

graphical facilities for data analysis and display either on-screen or on hardcopy, and

a well-developed, simple and effective programming language that includes conditionals, loops, user-defined recursive functions and input, and output facilities.

R’s analysis-oriented community has developed open-source packages for specific complex models that a data scientist would otherwise have to build from scratch. R also emphasizes quality reporting with support for clean visualizations and frameworks for creating interactive web applications. On the other hand, slower performance and a lack of key features like unit testing and web frameworks are common reasons that some data scientists prefer to look elsewhere.

R also is a very good-paid programming language in the world with over 87k annually.

SQL

SQL (Structured Query Language) is a domain-specific language used in programming and designed for organizing data held in a relational database Management system. As the job of a Data Scientist is to transform raw data into significant bits of knowledge, hence they essentially use SQL for data recovery. To be a powerful data Scientist, they should know how to wrangle and concentrate data from the databases utilizing SQL language.

SQL takes the 3rd place as a very good-paid programming language in the world with over 84k annually.

Java

Java is a general-purpose programming language that is class-based, object-oriented, and designed to have as few implementation dependencies as possible. … Java applications are typically compiled to bytecode that can run on any Java virtual machine (JVM) regardless of the underlying computer architecture, it is perhaps the oldest language used for business development. The majority of the well known Big Data tools on the likes of Spark, Flink, Hive, Spark and Hadoop are written in Java.

Java ranks very high when it comes to salary with 89k annually

Matlab

MATLAB can call functions and subroutines written in the programming languages C or Fortran. A wrapper function is created allowing MATLAB data types to be passed and returned. MEX files (MATLAB executables) are the dynamically loadable object files created by compiling such functions, it has local support for the sensor, picture, video, telemetry, binary, and other formats. It offers a full arrangement of insights and machine learning usefulness, in addition to cutting edge techniques, for example, nonlinear optimization, system ID, and a great many prebuilt algorithms for picture and video processing, money related modeling, control framework structure. Its numeric schedules scale directly to parallel processing on clouds and clusters.

Matlab ranks also very high when it comes to salary with over 97k annually.

Conclusion

In conclusion, You should look beyond it and embrace all tools for their respective strengths. Using more tools will only make you better as a data scientist.

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