Data Science is an exciting field to work in, combining advanced statistical and quantitative skills with real-world programming ability. There are many potential programming languages that an aspiring data scientist might consider learning. Whilst there is no correct answer for the best one to master, there are several things to take into consideration before you begin. Here we take a closer look at the three most commonly learned languages in the field.
1. R - Released in 1995 as a direct descendant of the older S programming language, R has since gone from strength to strength. R is a powerful language that excels at a variety of statistical and data visualization applications, and being open source allows for a very active community of contributors. Its recent growth in popularity is proof of how effective it is at what it does.
2. Python - Guido van Rossum introduced Python back in 1991. Python is a very good choice of language for data science, and not just at entry-level. Much of the data science process revolves around the extraction transformation loading process. This makes Python’s generality ideally suited. Libraries such as Google’s Tensorflow make Python a very exciting language to work in for machine learning.
3. SQL - SQL stands for Structured Query Language. It defines, manages and queries relational databases. The language appeared by 1974 and has since undergone many implementations, but the core principles remain the same. SQL is more useful as a data processing language than as an advanced analytical tool.
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