With the origin of Big Data, we have seen a data revolution around us. Big Data Analytics is one of the greatest patterns in IT area right now and the buzz around it won't curb at any point in the near future. Big Data is wherever nowadays, and if the business estimates are to be trusted, then Big Data Analytics market will keep on growing greater as organizations understand the significance of settling on information driven choices. The rise of Big-Data has evolved the need to handle it in an efficient manner. There are a number of tools in Analytics fields such as R, SAS, Python, Excel, Tableau, Splunk, Qlikview, Apache spark etc. The most popular tools are R and Python.
One of the ideal approaches to measuring the popularity or market share of software for analytics is total quantity of employment ads for each.
Some of the job title that requires python programming
· Data Analyst
· Data Engineer
· Data Scientist
· Python Developer
· Data Architect
Some of the job title that requires R programming
· Data Analyst
· Data Engineer
· Data Scientist
· Investment Analyst
· Tax Staff
· Scientist
· Business Analyst
· Visualization Analyst
R is a statistical and open source programming language, which is created in 1995. The aim was to develop that concentrated on conveying a superior and more user-friendly method to do data analysis, statistics, and graphical models. At to start with, R was fundamentally utilized as a part of scholastics and research, yet of late the venture world is finding R too.
Good For:
- R is one of the best statistical tools for data visualization in the world
- R has more than 5000 packages and ecosystem across fields such as finance, pharmaceuticals, machine learning etc
- R is cross platform and has no license restrictions
- R has dynamic client bunches where questions can be asked and are regularly immediately reacted to, frequently by the very individuals who built up the environment
Bad For:
- R has steep learning curve.
- R is also considered as slower than its other tool like python
- R's shortcomings in security and memory management
- R likewise has an exceptionally poor documentation which doesn't generally make it available a more extensive group of onlookers.
Python
Python is a high-level programming language. It was created during 1985-1990 by Guido van Rossum. Python can run on multiple platforms. It is easy to learn and easy to use. Its gain popularity among the data science group has seen a comparable development in data science packages on the Python Package list, PyPi.
Good For:
- Object oriented programming language
- Python has deep learning
- Python easy syntax increases the speed at which you can write a program
- A large number of resources are available for Python.
Good For:
- Python provides a limited number of packages. It doesn’t offer an alternative to the hundreds of R packages.
- It is not good for multiprocessor/multi-core work
- Has limitation with database access.
- It's near impossible to build a high-graphic 3D game using Python
When we looked at job trends of python R, Sas from indeed.com, we can see that job listing for “R statistics” were on the rise. This illustrates that the rise of R statistics as the hottest choice of job and to be noticed python is also no far behind it in comparison with other analytical tools.
As we see, R outperformed python as far as data science jobs
in 2017. These are, obviously, altogether different language and a speedy job
of sets of responsibilities will demonstrate that the R employments are a great
deal more centered on the utilization of existing strategies for examination,
while the Python jobs have to a greater extent a custom-programming edge to
them. With an
ever increasing number of jobs moving towards analytics the openings for work
for the same are likewise rising exponentially, leaving an intense rivalry
among the IT experts.
