Which Programming Language is best for Data Science in 2020



Data Scientist is the most demanding skill of the 21st century.  If you are also among those learning aspirants who are thinking to precede your career in Data Science then the question of choosing which language between R and Python must be popping up in your mind. And it is actually debated to choose what is better! This post is written with two objectives:-

Objective 1: For the experienced Data Scientists who want to get familiar with a library that takes care to deal with the issue to handle in your chosen language.

Objective 2: For the beginner Data Scientists who want to get familiar with the entire practical level knowledge of these two programming languages so you can choose between one.


Before going forward in the comparison, let us discuss what these programming languages are and what do these offers!

The Scenario of R Programming

R is a functional programming language with approx. 43% of Data Scientists using it in their tool stack as compared to the 40% of the people using Python. The R programming language is dedicated to solving statistical problems. R is known as specialized in solving problems related to Data Science.

Prerequisites to learn R programming language:

  • Fundamental knowledge of Statistics
  • Solid understanding of Mathematics
  • Understanding of Graphs for data representation
  • Prior knowledge of OOP’s or any programming language

The Scenario of Python Programming

Python is a non-functional programming language or an OOP based versatile language that can perform everything from data mining to plotting graphs. The design philosophy of Python is based upon its simplicity and readability. The algorithms written in Python are designed in an easily readable and writable manner. Indentations are used to separate the blocks of Python code. Inside each code block, you will find syntax. As per the survey done by O’Reilly, 40% of the Data Scientists use Python to solve their real-world problems which were exceeded by 36% of the respondents who use Excel.

Prerequisites to learn Python programming language:

  • Fundamentals of operating system
  • No prior experience in programming required

Programming in Data Science

The primary goal of Data Science is to solve a problem. Data Science programming needs the efficiency to perform everything it requires to analyze the data. Here, everything means implementing a multidisciplinary set of knowledge from every perspective of life to analyze the nature of data. The code snippet in Data Science is used to solve an algorithmic problem or equation in the form of a dataset.

Comparison Factors

R

Python

Easy to Learn

No

Yes

Speed

No

Yes

Data Handling Capabilities

Yes

Yes

Visualizations

Yes

No

Versatility

Yes

Yes

Commonness

No

Yes

Job Scenario

No

Yes

Community Support

Yes

Yes



Easy to learn

R has unstable desires to learn and adjust and people with less or no experience in programming believe that it's problematic in any case. At the point when you get the grasp of the language, it isn't that hard to fathom. 

Python, then again, underscores proficiency and code lucidity which makes it one of the least mind-boggling programming lingos. It is perfect on account of its effortlessness of learning and understandability. 

Speed 

R is a low-level programming language since it requires longer codes for clear procedures. This is one reason behind the diminished speed. 

Python, then again, is a high-level programming language and it has been the choice for building fundamental yet speedy applications. 

Data Handling Capabilities 

R is beneficial for investigation due to the huge number of codes, instantly usable tests and the upside of using code recipes. Regardless, it can moreover be used for primary data analysis without the foundation of any coding package. 

The Python bundles for data analysis were an issue anyway this has improved with the continuous variations. Numpy and Pandas are used for data analysis in Python. It is moreover sensible for equal estimation. 

 Visualizations

Visualizations are seen beneficially and more enough than rough qualities. R includes different packs that give advanced graphical capacities. 

Visualizations are huge while picking data analysis based programming and Python causes them to astonish observation libraries. It has logically various libraries yet they are erratic and give a spotless yield. 

Versatility 

It is anything but difficult to use complex conditions in R and moreover, the measurable tests and models are immediately open and viably used. 

Python, then again, is a versatile language concerning building something without any planning. It is similarly used for scripting a site or various applications. 

Commonness 

Directly, if we look at the distinction of both the programming languages, they started from a comparable level 10 years prior yet Python saw a huge improvement in reputation and was situated first in quite a while rundown of programming languages when stood out from R that situated 6th in the overview. 

Python customers are continuously dedicated to their language when appeared differently concerning the customers of the last as the degree of changing from R to Python is twice as huge as Python to R.

Code writing

For example, you need a number of rows in both R and Python languages, the code in both the languages will look like-

          R

          dim(nba)

          [1] 481 31



          Python

          nba.shape

          (481, 31)

The example will print the number of players and the number of columns in each. We have 481 rows, or players, and 31 columns containing data on the players.

Functional Differences between R and Python

Functional Differences

R

Python

Collect raw data

CSV’s, Excel, text files into R, Data collection packages like magrittr or Rvest

CSV’s, SQL tables, Datasets, Wikipedia tables, etc.

Process data

Using R libraries such as reshape2

Using Pandas

Explore data

Rely on 3rd party for heavy work

Data points using Time-series analysis

Performing in-depth analysis

Poisson distribution and combination of probability laws 

Numeric analysis with NumPy, Scientific calculations and computing with SciPy, Machine Learning algorithms with scikit-learn, etc.

Data Visualization

Using ggplot2

Generate basic graphs and charts using Matpolib or Plot.ly, Python notebook into HTML conversions using nbconvert function, etc.

Salary

$71,931 to $123,911 per annum

$78,176 to $111,896 per annum



Why R?

R is used profoundly by Data Scientists and is loaded with a number of packages that are available for Data Science projects. R is good at quickly testing ideas, trying different ways to visualize data and for increased prototyping work.

Why Python?

Various libraries in Python like Pandas, numpy, scipy, and scikit-learn are handy to perform Data Science projects. If you want to showcase your Data Science work, Python will be the winner! Django is an awesome web application framework that can help you create websites or web services with both Data Science and Web programming.

What to choose R or Python?

At the end of the discussion, the goal of a programming language is to allow the simplest and more efficient code that can be utilized for the job. You can use either of the two programming languages or the conjunction of it. The focus of a Data Scientist should be on the skills but not on the tools. The competing character of R and Python might help the learners to produce the simplest and the most qualitative code. Because of the hybrid nature of Data Science, there will always be a wrestling match between two, as both of them are robust. Discuss in the comment section what do you think will win this battle of the best programming language of Data Science- R or Python?

 

 

 

 

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