Data science and machine learning are growing at the fastest pace. Needless to say, big giants are investing in machine learning and data science to develop new applications.
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. You might have a question, what are Data Science and Machine Learning exactly?
Keep reading further to know!
What’s Data Science and Machine Learning?
Data science is basically a study of data with an aim to extract meaningful results or insights. It strategically combines the principles from the fields of mathematics, AI, and statistics to analyze huge volumes of data.
Machine learning is a field of computer science or an application of artificial intelligence that provides machines or systems 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. In this article, we’ll explore the 7 best programming languages you need to master for machine learning and data science.
To do this, skills were searched in conjunction with the prominent programming languages for machine learning like:
7 Best Languages for Machine Learning and Data Science
1. Python
Python is an interactive, interpreted, object-oriented, and high-level programming language for general-purpose programming. The Python 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. This is a commonly used language for developing certain applications. has a web plug-in that allows you to run apps in your browser.
3. SQLÂ
SQL (Structured Query Language) is a popular language for Data science. It lets programmers to work on relational databases using queries. Furthermore, analyze the data thoroughly to create test environments for data.
4. R
R is a programming language and also a free software environment for statistical computing and graphics. It is supported by the R Foundation. Additionally, widely used among data miners and statisticians for developing statistical software and data analysis.
5. C++
A general-purpose programming language, C++. It is an extension of the C language and has imperative, object-oriented, and generic programming features. C++ runs on Several platforms, such as Windows, Mac OS, and the various versions of UNIX.
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.
The 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 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%). It is the language with the lowest prioritization, with only 17% of developers who use it, prioritize it. This means that in most 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 of those of R.
Not only is Python the most widely used language, but it is also the primary choice for the majority of its users.
More about the languages
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 slightly better performance than R.
With extensive research on other languages 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. On the other hand, if 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. To stay updated with more such technical information, and stay updated with our blogs, we publish top-quality content regularly.
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