Big data has started to play a larger role across sectors nowadays, making professionals with data analytical skills highly sought after.
Tempted to pick up a new skill? For newbie data analysts, if you find it difficult to differentiate between different data analytic languages such as R and Python, here’s a simple guide for you.
What is R?
This open-source language was originally designed for statisticians and individuals in academia in mind.
In recent years, R has become arguably the most common and popular data science language. It has various field-specific advantages such as great features for data visualization, which means the data can be understood more effectively than the raw numbers alone.
Amateur data analysts with zero statistics knowledge might find themselves struggling with the steep learning curve because the syntax particularly is hard to manage.
Fortunately, it has a Comprehensive R Achieve Network (CRAN) that seeks to provide easy access to users to the algorithms and latest functionalities. Users can start from there instead of needing to develop everything from scratch.
What is Python?
Python is the treasure for amateur data analysts. Its learning curve is relatively flat because it is syntactically clear and easily interpretable. It is recommended for beginners to first pick up Python instead of R.
Emphasizing code readability and productivity, Python has a general-purpose and fully-fledged language that is highly applicable to many fields such as biological sciences, medicine, web development, and data science. It is favored by a lot of developers and programmers because of its easy-to-learn syntax.
Along with the installation of scientific computer software like NumPy and pandas, Python will be rendered usable for effective data analysis and data mining.
