R vs Python in Datascience

Data science deals with identifying, representing and extracting meaningful information from data sources to be used to perform some business logics.The data scientist uses machine learning, statistics, probability, linear and logistic regression and more in order to make out some meaningful data. Finding patterns and similar combinations and cracking the best possible path way according to the business logic is the biggest job of analysis. R, Python, SQL, SAS, Tableau, MATLAB, etc. are of the most useful tools for data science, R and Python being the most used ones. But still, it becomes confusing for any newbie to choose the better or the most suitable one among the two, R and Python. Let’s try to visualize the difference. 

Overview: R and Python are both popular programming languages used in data science. Each language has its own strengths and weaknesses, and the choice between them ultimately depends on the specific needs of the project and the preferences of the data scientist. Here are some general points to consider:

R is a language designed specifically for statistical computing and data analysis and has a large number of packages and libraries for statistical analysis and visualization. R is known for its ease of use and readability, making it a good choice for exploratory data analysis and data visualization. R has a strong community of users, which can be helpful for finding answers to specific questions and getting support. R may be a better choice for smaller datasets and for tasks that involve traditional statistical methods, such as hypothesis testing and linear regression.

Python is a general-purpose programming language that is versatile and can be used for a wide range of tasks, including data science.
Python has a larger number of libraries and packages for machine learning and deep learning than R, making it a good choice for projects that require these techniques. general-purposePython is a popular language in the software development community, making it a good choice for integrating data science into larger software projects. Python may be a better choice for larger datasets and for tasks that involve data preprocessing and cleaning.
Ultimately, the choice between R and Python depends on the specific needs of the project and the preferences of the data scientist. It is worth noting that many data scientists use both languages and choose the language that is best suited for the specific task at hand.

R	Python
R is a programming language and free software environment for statistical computing and graphics, supported by the R Foundation for Statistical Computing. It was designed by Ross Ihaka and Robert Gentleman and first released in August, 1993. It is widely used among statisticians and data miners for developing statistical software and data analysis.	Python is an Interpreted high-level programming language for general purpose programming. It was created by Guido Van Rossum and was first released in 1991. Python has a very clean and simple code syntax. It emphasizes code readability and thus debugging is also far simpler and easier in Python.

Specialties for data science :

R	Python
R packages cover advanced techniques which very useful for statistical work. The CRAN text view provides you with many useful R packages. R packages cover everything from Psychometrics to Genetics to Finance. On the other hand, Python, with the help of libraries like SciPy and packages like statsmodels, covers only the most common techniques.	R and Python are equally good for finding outliers in a data set, but for developing a web service to enable other people to upload datasets and find outliers, Python is better. People have built modules to create websites, interact with a variety of databases, and manage users in Python. In general, to create a tool or service that uses data analysis, Python is a better choice.

Functionalities :

R	Python
R has inbuilt functionalities for data analysis. R was built by eminent statisticians with statistics and data analysis in mind, so many tools that have been externally added to Python through packages are built in R by default.	Python is a general purpose programming language. So most of the data analysis functionalities are not inbuilt and are available through packages like Numpy and Pandas, which are available in PyPi(Python Package Index).

Key domains of application :

R	Python
Data visualization is a key aspect of analysis, as visual data is best understood. R packages like ggplot2, ggvis, lattice, etc. make data visualization easier in R. Python is catching up with packages like Bokeh, Matplotlib, etc. but is still far behind in this regard.	Python is better for deep learning. Packages like Lasagne, Caffe, Keras, Mxnet, OpenNN, Tensor flow, etc. allows development of deep neural networks far more simple in Python. Although some of these, like tensor flow, are being ported to R(packages like deepnet, H2O, etc.) but it is still better in Python.

Availability of Packages :

R	Python
R has hundreds of packages and ways to accomplish needful data science tasks. Although it allows to have desired perfection in completing the task, it makes it difficult for inexperienced developers to achieve certain goals.	Python relies on a few main packages, viz., Scikit learn and Pandas are the packages for machine learning data analysis respectively. It makes easier to accomplish required tasks but consequently it becomes difficult to achieve specialization.

Ultimately it’s the job of data scientist itself to choose the most suitable language as needed. For statistical background, R might be a better option. But for the CS background or even a beginner, Python is most suitable option. But, it’s better to have sound knowledge of both cause both might be useful at times in data science career.

Advantages of R in Data Science:

1. R has a rich collection of statistical libraries and packages, making it an ideal language for statistical analysis and visualization. 
2. R has a strong and supportive community that provides a wealth of resources, tutorials, and forums for data scientists to learn and collaborate.
3. R is free and open source, making it accessible to users with limited budgets and allowing for easy customization.
4. R has a well-established ecosystem of tools and frameworks for data cleaning, transformation, and analysis.
5. R is a relatively easy language to learn and use, with intuitive syntax and many built-in functions for common data manipulation tasks.

Disadvantages of R in Data Science:

1. R may not be as fast as other languages, such as Python, which can be a disadvantage when dealing with large datasets or complex machine-learning models.
2. R may not have as wide a range of libraries and packages as Python, particularly in areas such as deep learning and natural language processing.
3. R can have a steeper learning curve for users who are not familiar with statistical methods or programming in general.
   R may not be as suitable
4.  for large-scale projects that require collaboration with software engineers or integration with other programming languages or systems.

Advantages of Python in Data Science:

1. Python has a vast array of libraries and packages for data analysis, machine learning, and deep learning, making it a powerful language for data science. 
2. Python is a general-purpose language that can be used for a wide range of applications beyond data science, making it a versatile tool for developers.
3. Python is easy to learn and use, with a clean and intuitive syntax and many online resources and tutorials available.
4. Python is fast and efficient, making it suitable for large-scale projects and computations.
5. Python has a strong community and ecosystem of tools and frameworks, making it easy to collaborate and integrate with other systems.

Disadvantages of Python in Data Science:

1. Python can be more difficult to set up and configure than R, particularly when dealing with complex data analysis or machine learning tasks. 
2. Python may require more code to perform certain tasks than R, which can be a disadvantage for users with limited programming experience.
3. Python can have a steeper learning curve for users who are not familiar with programming in general or who are more comfortable with statistical software.
4. Python can have more verbose and complicated code when dealing with certain types of data manipulation or analysis tasks.