Published By - Brian Curtis

Top Languages for Machine Learning & Data Science

macbook laptop machine learning

Data science and machine learning are growing at rapid rate, and All major tech giants are investing enormously in machine learning and data science to improve their products. IBM predicts that by 2020, the number of jobs for all United States data professionals will increase by 365,000 openings to 2,730,000.

So what is Data science and machine learning, Data science is an interdisciplinary field of scientific methods which includes the study of where information comes from, what it represents and how it can be turned into a valuable means to solve analytically complex problems. And Machine learning is a field of computer science or is an application of artificial intelligence that provides machine or system the ability to learn and improve from experience without being explicitly programmed automatically.

In recent times, machine learning and data science fields have witnessed exponential growth. So, what programming languages should one learn to land a machine learning or data science job?

To work in this field of machine learning and data science, you need to learn some particular programming languages and skills.

To do this, skills were searched in conjunction with the prominent programming languages for machine learning like:

1. Python

Python is an interactive, interpreted, object-oriented and high-level programming language for general-purpose programming. Python source code is available through the GNU General Public License (GPL). It is used by thousands of people to do things from testing microchips at Intel to building video games with the PyGame library, to powering Instagram.

2. Java

Java is a general-purpose computer-programming language that is class-based, concurrent and object-oriented. Java has a web plug-in that allows you to run apps in your browser.

3. R

R is a programming language and also a free software environment for statistical computing and graphics. It is supported by the R Foundation. IIt is widely used among data miners and statisticians for developing statistical software and data analysis.

4. C++

It is a general-purpose programming language. C++ is an extension of the C language. It has imperative, object-oriented and generic programming features. C++ runs on Several platforms, such as Windows, Mac OS, and the various versions of UNIX.

5. C

C is a high-level and general-purpose programming language. It is ideal for developing firmware or portable applications. It was Originally intended for writing system software, C was developed at Bell Labs by Dennis Ritchie for the Unix Operating System in the early 1970s.C prevents many unintended operations

6. Scala

It is a general-purpose programming language providing support for functional programming and a strong static type system. Scala’s static types help complex applications to avoid bugs.
Its Java Virtual Machine and JavaScript runtimes let you build high-performance systems with easy access to huge ecosystems of libraries.

7. Julia

Julia is a dynamic high-level programming language designed to address the needs of high-performance numerical analysis and computational science.

Overall popularity of machine learning languages.

Python leads the set, with 56% of data scientists and machine learning developers using it and 32% prioritizing it for the development. R is often compared to Python, but they are nowhere near comparable in terms of User base and popularity: R comes third in overall usage (31%).

R is the language with the lowest prioritization, with only 17% of developers who use it, prioritize it. This means that in most of the cases R is an alternative language, not a first choice. The same ratio for Python is at 56%, the highest by far among the languages, a clear indication that the usage trends of Python are the exact opposite to those of R. Not only is Python the most widely used language, it is also the primary choice for the majority of its users.

If you want faster computation, and to benchmark your algorithm, nothing can beat C/C++.

C/C++ is a second alternative to Python, both in usage (44%) and prioritization (19%). Java follows C/C++ very closely, while JavaScript comes fifth in usage, although with a slightly better performance than R.

We researched about other languages used in machine learning, including Julia, Scala, Octave, Ruby, SAS, and MATLAB, but they all fall below the 5% mark of prioritization and below 26% of usage.

If you are a beginner in programming and planning to start with machine learning, we suggest Python as the best option to go, given its wealth of libraries and ease of use and If, on the other hand, you’re dreaming of a job in an IT environment, be prepared to use Java. It is time for machine learning, and the journey is guaranteed to be a great one, irrespective of the language you are opting for.

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