Original article was published on Artificial Intelligence on Medium
Battle of the Beasts — Python vs R in Data Science 2020
Data Science is one of the trending job fields today. I myself am very fascinated with the analytical and logical approach of data science. In today’s world where geeky jobs are not considered to be sexy. The job of a data scientists is fascinating the researcher to a level that it is now referred to as the most Sexiest Job of the 21st century.
Although it is the sexiest one this job holds the responsibility for carefully handling the precious data sets. Data set nowadays is the most powerful tool that can change the entire scenario of the market place but only if it is analysed with utmost insight and knowledge.
To achieve useful knowledge and intelligence from the data, one requires –
- Capturing the data — data acquisition, data entry, signal reception and data extraction.
- Maintaining the data — warehousing, cleansing, staging, processing, architecture.
- Analysis of data — exploratory or confirmatory, predictive analysis, regression, text mining, qualitative analysis.
- Communicate — reporting, visualization, business intelligence, decision making.
We are living in a highly competitive world, which is always anticipating to reach heights. So, data science aspirants have no choice but to upskill themselves with adequate knowledge and logical thinking. The trend of data science is a chance for the aspirants to have a better hold on the market and research opportunities. And the knowledge of programming and logic building accelerates the workflow of this industry.
Therefore I have come up with the article which differentiates between Python and R when it comes to programming languages in data science.
The first thought that comes in the mind of most of the data scientists is Python. Python is close to programmers heart. As it is object-oriented, easy to learn, flexible and open-source. It has several tools and programming languages specially designed for data science like Python, NumPy, Matplotlib. There is a huge community of developers working on Python where data scientists work, ask queries, answers questions. This language has been in trend for a long time and it is very much expected to remain the first choice of data scientist and engineers.
Python is a structured and high-level programming language for advanced applications with general-purpose programming. It was created by Guido Van Rossum and first published in 1991. Python has a clean and simple syntax. It emphasizes code learning and thus debugging is also much easier and more convenient in Python.
R and Python are equally good at finding outliers. But when it comes to developing a web service to enable other people to upload data and find vendors, Python is better than R. Python has modules for website design, databases interaction. In general, Python is a better choice.
Python, with the help of libraries like SciPy and packages such as statsmodels, only covers the most common techniques.
Python has few major libraries which make it easier to work on machine learning and data analysis — Scikit learns and Pandas. To accomplish required tasks it is much easier to achieve specialization.
R is a language which is very unique in terms of its features. It has some features which one can not even expect in other languages. Tasks like vector manipulation is a cakewalk for R. It is specially designed for the data scientists and statisticians. It is an interpreted language and supports matrix arithmetic, supports procedural programming with function and OOPs (Object Oriented Programming) with generic functions.
R is a programmable and free programming language for math and graphic computer, supported by Statistical Computing. It was developed by Ross Ihaka and Robert Gentleman and was first released in August 1993. It is widely used among statisticians and data analysts for building statistical software and data analysis.
R packages are very useful in mathematical work as it covers advance techniques. There are many useful R packages provided by CRAN. From Psychometric to genetics to finance, R covers a lot.
To accomplish needful data science tasks, R has hundreds of packages and ways. It makes it difficult for inexperienced developers to achieve certain goals, although it allows to have the desired perfection in completing the task.