Data science has been tagged as one of the ‘sexiest career domains in the 21st century by Forbes magazine. Owing to increasing data science applications across different industries, data science careers are also multiplying by the day.
Before you rush off to sign up for a bachelors in the data science programme, it would do you good to learn everything you can about the field. This includes the diversity of data science careers, their applications, and the types of tools and coding languages used on the job.
If you fancy a data science career, then you should be very proficient in computer coding. In fact, the sooner you start the better. However, choosing the right coding language can be tricky since the domain has plenty of programming languages, each with its own strengths and challenges.
Let’s dive into the top programming languages that are preferred by today’s data scientists and the reasons behind their popularity.
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What are the most popular computer coding languages used in the data science field?
For a newcomer in the data science field, computer coding can be very overwhelming since there are hundreds of them with diverse use-cases. However, only a handful of them can be appropriate for creating data science algorithms and data modelling techniques.
Here is a list of some of the most popular programming languages used in data science.
- Python: Python is ranked among the top 5 programming languages by several data science indices like the PYPL and the TIOBE. It is a general-purpose coding language with wide applicability and many open-source code libraries.
- R: Although not as highly rated as python, R language can still hold its own within the data science community and is a top favourite for aspiring data professionals. It is a perfect language for data manipulation and visualisation since it has been explicitly designed for data science.
- SQL: Structured Query Language or SQL is a conventional coding choice in the data science field. In fact, the majority of the world’s data is stored in SQL databases. Thus, learning this language can help you get a suitable job in any data science organisation.
- Java: Java is another highly rated programming language by data scientists and has been ranked second in the PYPL index. It is open-source and is known for its top-class performance and efficiency. Many data science technologies and applications today rely on the Java ecosystem.
Other popular coding languages in this field include Swift, Go, SAS and MATLAB. Some other options include Julia, Scala, C, C++ and JavaScript.
For you to become a successful data scientist, you might be required to be proficient in more than one programming language. Gaining proficiency in computer coding can become easier if you enrol in suitable data science and AI programme.
Such a programme can introduce you to the specifics of the most popular data science coding languages and their industry relevance. You can also gain expertise in different data science concepts and modelling techniques.
Start your search for appropriate data science courses from reputed universities today to become an accomplished data science professional.
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