R VS PYTHON – Which is better for Data Science ?
Let’s say a great idea strikes you and you would like to put it to reality in the form of a brand new machine learning product. You feel you can be the next Mark Zuckerberg and can revolutionize the world with the help of your new invention. Now the only dilemma you would be. “which data science programming language should you use to reach to glory? “
Let’s walk you through Data Science Technology and it’s 2 widely used programming languages – R and Python.
Some Facts about Data Science
Isn’t it true that you take even the simplest of decisions about anything in everyday life based on some data or facts supporting it? Same goes with business, rich data is the asset of every organization. With the increase in the use of internet, all operations being conducted online. And the majority of target customers being found online through the increased use of various gadgets. Businesses are investing more and more on data analytics. Almost 80% of business have their investments in big data. We are not only creating humongous data but also learning to put it to good use for strategic business decisions. The science of studying raw data, extracting, organizing and analyzing it to draw meaningful insights or knowledge is called as Data science.
Career in Data Science
Surveys say that by 2020, almost 50 billion devices all over the world will be connected, collecting, sharing and analyzing data. About 2 megabytes of new information is said to be created for every human every second in the near future. Digital information will be scattered around in numerous quantities like stars in the universe. All these facts conclude that in the next 5 years, all types of businesses. Large and small will be making use of some of the other forms of Data Analytics to impact their business decisions. This is the reason why Data Analyst is considered to be the “Sexiest Job of the 21st Century”. The need for good Data Analysts having great hands-on skills in various Data Science tools. It is always going to be in good demand, with handsome salary packages.
There are various tools, techniques, and languages used for studying data. However, the 2 main languages dominating the top charts of the most used and best Data Science languages are R Programming and Python. I think there are questions like “Which is better for data analysis: R or Python?” , “what should I use for machine learning? “ and more running through your mind in a dilemma to choose the right data science language for you especially. If you are a newbie data analyst looking where to start from.
This blog talks about the strengths and weaknesses of both languages to help you figure out answers to your questions.
R Vs Python – Comparison Attributes
To make things simpler, let us see a general comparison between the 2 statistical programming languages. This from the perspective of a data scientist or statistician.
R and Python are majorly used by startups and mid-sized organizations for their data science needs. While python is a free open source general purpose language known for it’s easy to understand syntax, R programming is also a free, multi-purpose open source programming language known for it’s field specific advantages like ability for good data visualization, data mining libraries and active community. Even since these two languages are increasing in demand in the data analytics industry. They are waging war to become to become the favorite language of a data scientist. A recent survey was conducted by an HR Firm named Burtch Works. Wherein they questioned around 1000 professionals about the language that they preferred for Data Analytics. It was found that about 45% people preferred R Programming, 20% for Python and remaining for other expensive legacy systems like SAS and MATLAB. SAS and MATLAB are generally preferred by large organizations having huge budgets. R and Python were developed specifically for data analysis and data science as a solution to replace these.
R was developed by Ross Ihaka and Robert Gentleman and released in the year 1995. It is an open source statistical computing language designed or data analysis, statistical and graphical models. R has had a long trusted history and a very active support community on whom you can rely easily for online support on using the language. R’s capabilities can also be easily extended by with the help of plenty of publicly released CRAN packages (about 5 to 6000) repository. R integrates easily with other major programming languages like C, C++, Java and is good for complex exploratory data analysis, heavy advanced statistical analysis, large computations and graphs. One thing about R is that it’s syntax is quirky and hard to pick up especially if you are from a non-programming background.
Python was developed by Guido Van Rossum in the year 1991. It is a high level general purpose programming language used for data engineering, wrangling, data munging, web app building, scraping etc. Python is based on OOPs concepts and will be easier for you if you have earlier worked on Object oriented programming languages like Java or C++. Being an OOP language, you can write good robust codes using Python. Python does not have a huge library of packages as compared to R. But it can be used very well in conjunction with other PyPi packages like Pandas, Numpy, Scipy, Scikit – Learn and Seaborn. Python is easier to learn than R due to the simplicity of its syntax and fits more naturally in the coding environment.
Cost and Availability
Both R and Python are open-source and free of cost.
R can be easily installed by running an executable file on your workstation.
Python is pre-installed on many OS like Linux and Mac. This for Windows you can easily download it from Python.org and install it.
R programming was designed with a purpose to serve for user friendly data analysis, graphical modelling and visualization and statistics.
Python was designed with a purpose to serve for development activities , productivity and code readability.
R is a low level programming language and difficult to learn and understand the code. Python has simple code and can be learnt well even by beginners.
R is a low lever programming language developed by statisticians for statisticians. Hence, it aims at making their work easy and concentrates less on making computing tasks faster. This the reason it is slow as compared to Python which is a high level programming language.
R has more advanced tools to help you in graphical representation and visualization of Data. Python can also do it, but the results are less user friendly and not attractive to the eye.
Packages and libraries
R has numerous packages on CRAN that make implementation fast and easy. Python has relatively less number of packages but is soon catching up with R. Python packages infance has improved a lot with the advent of NumPy and Pandas.
R has a more vibrant and active community support forum as compared to Python. Help is readily available through previously faced similar kind of issues and solutions, documentations, mailing lists or by talking on the online forums or facebook groups easily when needed.
Task and usage
R programming is useful for exploratory tasks for data analysis using standalone computing or individual servers.
Python is useful for full-fledged programming, integrating with web apps and production use.
I might highlight that, if you are more inclined towards statistics, research and data science, you can go for R.
If your inclination is towards engineering, development and programming to make something novel never made before, choose Python.
KDnuggets’ annual software poll indicated Pythons usage rising high and fast in the data analytics industry. While facts and figures show that the market is bending towards Python. We cannot make a single conclusion on what will prevail because the data analytics industry and technologies are very dynamic in nature. R is developing at a rapid pace and might soon catch up in the race. But till then, it can be said that Python is a more mature, flexible and fully-fledged choice when it comes to machine learning.
Based on all above attributes, you can make a decision on which suits you as a best career option. Depending upon your aspirations, educational background, experience, interest and your current stage in career. Data science the most promising job of the 21st century and luckily. You cannot say that you have made a mistake or error choosing either of these, both have their pros and cons and both are equally useful.
You can consider the below few points while making a decision –
- R is generally preferred by mathematicians and statisticians for research. Whereas Python is preferred by computer scientists, programmers and software engineers. If you have to work on data that’s organized and cleaned up, R can be easily used for analysis. But if you are the who has to work on cleaning, scraping jumbled data, Python will be of help.
- According to a research conducted by IBM, data analytics jobs are expected to increase from 2.3 million to around 2.7 million by 2020. It is said that with the expansion of the Python ecosystem, there are more jobs in the market for Python as compared to R.
- If you are a data/business or financial analyst, you major job role would be to finally derive meaningful insights from data. In this case R will be more helpful because it can derive conclusions from data quickly and is good for working with tabular data and visualizations.
- If you are a data scientist and have to analyze data as well as do production-level work like developing machine learning products. Python will be the choice for you.
- Python is used for academic research purposes as it is associated with machine learning and deep learning. It can be a great choice if you would like to contribute more to the growth of this industry.
- Job roles you can expect after learning R Programming can be those of a “data analyst”, “data miner”, or “statistician”.
- Job roles after learning python would be of “ big data architect”, “machine learning/ data engineer”.