As a data scientist, you probably want and need to learn Structured Query Language, or SQL. SQL is the de-facto language of relational databases, where most corporate information still resides. But that only gives you the ability to retrieve the data — not to clean it up or run models against it — and that’s where Python and R come in.R and Python both share similar features and are the most popular tools used by data scientists. Both are open-source and henceforth free yet Python is structured as a broadly useful programming language while R is created for statistical analysis.
R is free and has become increasingly popular at the expense of traditional commercial statistical packages like SAS and SPSS. Most users write and edit their R code using RStudio, an Integrated Development Environment (IDE) for coding in R.
For data science, there are a number of extremely powerful Python libraries. There’s NumPy (efficient numerical computations), Pandas (a wide range of tools for data cleaning and analysis), and StatsModels (common statistical methods). You also have TensorFlow, Keras and PyTorch (all libraries for building artificial neural networks - deep learning systems).
These days, many data scientists using Python write and edit their code using Jupyter Notebooks. Jupyter Notebooks allow for the easy creation of documents that are a mix of prose, code, data and visualizations, making it easy to document your process and for other data scientists to review and replicate your work.
"Historically there has been a fairly even split in the Data Science community. Typically data scientists with a stronger academic or statistical background preferred R, whereas data scientists who had more of a programming background tended to prefer Python."
Now detailed report on advantage and disadvantage of both the popular language:
● The bulk of useful libraries and tools — Similar to Python, R comprises of multiple packages which help to improve the performance of the machine learning projects. For instance — Caret boosts the machine learning capabilities of the R with its special set of functions which helps to create predictive models efficiently. R developers gain advantage from the advanced data analysis packages which cover the pre- and post-modeling stages which are directed at specific tasks like model validation or data visualization.
● Suitable for exploratory work — If you require any exploratory work in statistical models at the beginning stages of your project then R makes it easier to write them as the developers just need to add a few lines of code.
● Inconsistent — As the algorithms of R come from third parties, it happens that you might end up with inconsistencies. Every time your development team makes use of a new algorithm, all the connected resources need to learn different ways to model data and make predictions. Similar to this, every new package requires learning and there is no detailed documentation of R as it leads to a negative impact on the development speed.
● Smooth Learning Curve — Python is easy to learn and easily accessible which enables you to find the skilled developers on a faster basis.
● The bulk of Important libraries — Python basts countless libraries for munging, gathering, and controlling the information. Take an occasion of Scikit-realize which comprises tools for information mining and investigation to support the incredible AI convenience utilizing Python. Another bundle called Pandas gives engineers superior structures and data examination devices that help to diminish the improvement time. If your development team demands one of the major functionalities of R then RPy2 is the one to go for.
● Better Integration — Generally, in any engineering environment, the Python integrates better than R. Thus, regardless of whether the designers attempt to exploit a lower-level language like C, C++ or Java, it generally gives better joining different components with Python wrapper. Additionally, a python-based stack is anything but difficult to incorporate the remaining task at hand of data researchers by bringing it easily into creation.
● Boosts Productivity — The syntax of Python is exceptionally decipherable and like other programming languages, however unique in relation to R. In this way, it guarantees high profitability of the development groups.
● Due to the presence of the Global Interpreter Lock (GIL), threading in Python becomes tricky and quite problematic. Subsequently, multi-threaded CPU-bound applications act slower than single-thread ones. An AI undertaking is more valuable for executing multiprocessing instead of utilizing multithreaded programming.
When compared to R, Python is . . .
General purpose: Python is a general purpose programming language. It’s great for statistical analysis, but Python will be the more flexible, capable choice if you want to build a website for sharing your results or a web service to integrate easily with your production systems.
Increasingly popular: In the September 2019 Tiobe index of the most popular programming languages, Python is the third most popular programming language (and has grown by over 2% in the last year), whereas R has dropped over the last year from 18th to 19th place.
Better for deep learning: Most serious deep learning projects use either TensorFlow or PyTorch. Both work really well with Python, and while there is now an R interface for TensorFlow, much more deep learning work is being done with Python than with R. As deep learning becomes applicable to an increasingly wide range of domains (it started off with computer vision, now it’s becoming the default approach for most Natural Language Processing tasks as well) that’s increasingly important.
Similarity to other languages: While someone with a background in Lisp might be able to learn R fairly quickly, if someone has a background programming in a more popular general purpose programming language — like Java, C#, JavaScript or Ruby — they are going to find it easier to come up to speed with and contribute to a project written in Python.
There are still plenty of jobs where R is required, so if you have the time it doesn’t hurt to learn both, but I’d suggest that these days, Python is becoming the dominant programming language for data scientists and the better first choice to focus on.
R vs. Python: Which One to Go for?
When it comes to machine learning projects, both R and Python have their own advantages. Still, Python seems to perform better in data manipulation and repetitive tasks. Hence, it is the right choice if you plan to build a digital product based on machine learning. Moreover, if you need to develop a tool for ad-hoc analysis at an early stage of your project then go for R. The ultimate choice depends on which programming language you want to go. Till then — keep learning!
A little background on R
R was created by Ross Ihaka and Robert Gentleman — two statisticians from the University of Auckland in New Zealand. It was initially released in 1995 and they launched a stable beta version in 2000. It’s an interpreted language (you don’t need to run it through a compiler before running the code) and has an extremely powerful suite of tools for statistical modeling and graphing.R is free and has become increasingly popular at the expense of traditional commercial statistical packages like SAS and SPSS. Most users write and edit their R code using RStudio, an Integrated Development Environment (IDE) for coding in R.
A little background on Python
Python has also been around for a while. It was initially released in 1991 by Guido van Rossum as a general purpose programming language. Like R, it’s also an interpreted language, and has a comprehensive standard library which allows for easy programming of many common tasks without having to install additional libraries. It’s also available for free.For data science, there are a number of extremely powerful Python libraries. There’s NumPy (efficient numerical computations), Pandas (a wide range of tools for data cleaning and analysis), and StatsModels (common statistical methods). You also have TensorFlow, Keras and PyTorch (all libraries for building artificial neural networks - deep learning systems).
These days, many data scientists using Python write and edit their code using Jupyter Notebooks. Jupyter Notebooks allow for the easy creation of documents that are a mix of prose, code, data and visualizations, making it easy to document your process and for other data scientists to review and replicate your work.
"Historically there has been a fairly even split in the Data Science community. Typically data scientists with a stronger academic or statistical background preferred R, whereas data scientists who had more of a programming background tended to prefer Python."
Now detailed report on advantage and disadvantage of both the popular language:
Advantages of R
● Suitable for Analysis — if the data analysis or visualization is at the core of your project then R can be considered as the best choice as it allows rapid prototyping and works with the datasets to design machine learning models.● The bulk of useful libraries and tools — Similar to Python, R comprises of multiple packages which help to improve the performance of the machine learning projects. For instance — Caret boosts the machine learning capabilities of the R with its special set of functions which helps to create predictive models efficiently. R developers gain advantage from the advanced data analysis packages which cover the pre- and post-modeling stages which are directed at specific tasks like model validation or data visualization.
● Suitable for exploratory work — If you require any exploratory work in statistical models at the beginning stages of your project then R makes it easier to write them as the developers just need to add a few lines of code.
Disadvantages of R
● Steep learning curve — It is tough to deny that R is a challenging language and therefore you can find very rare experts for building your project team.● Inconsistent — As the algorithms of R come from third parties, it happens that you might end up with inconsistencies. Every time your development team makes use of a new algorithm, all the connected resources need to learn different ways to model data and make predictions. Similar to this, every new package requires learning and there is no detailed documentation of R as it leads to a negative impact on the development speed.
Advantages of Python
● General-purpose language — Python is regarded as a better choice if your project demands more than just statistics. For instance — designing a functional website● Smooth Learning Curve — Python is easy to learn and easily accessible which enables you to find the skilled developers on a faster basis.
● The bulk of Important libraries — Python basts countless libraries for munging, gathering, and controlling the information. Take an occasion of Scikit-realize which comprises tools for information mining and investigation to support the incredible AI convenience utilizing Python. Another bundle called Pandas gives engineers superior structures and data examination devices that help to diminish the improvement time. If your development team demands one of the major functionalities of R then RPy2 is the one to go for.
● Better Integration — Generally, in any engineering environment, the Python integrates better than R. Thus, regardless of whether the designers attempt to exploit a lower-level language like C, C++ or Java, it generally gives better joining different components with Python wrapper. Additionally, a python-based stack is anything but difficult to incorporate the remaining task at hand of data researchers by bringing it easily into creation.
● Boosts Productivity — The syntax of Python is exceptionally decipherable and like other programming languages, however unique in relation to R. In this way, it guarantees high profitability of the development groups.
Disadvantages of Python
● Includes a very few statistical model packages.● Due to the presence of the Global Interpreter Lock (GIL), threading in Python becomes tricky and quite problematic. Subsequently, multi-threaded CPU-bound applications act slower than single-thread ones. An AI undertaking is more valuable for executing multiprocessing instead of utilizing multithreaded programming.
When compared to R, Python is . . .
General purpose: Python is a general purpose programming language. It’s great for statistical analysis, but Python will be the more flexible, capable choice if you want to build a website for sharing your results or a web service to integrate easily with your production systems.
Increasingly popular: In the September 2019 Tiobe index of the most popular programming languages, Python is the third most popular programming language (and has grown by over 2% in the last year), whereas R has dropped over the last year from 18th to 19th place.
Better for deep learning: Most serious deep learning projects use either TensorFlow or PyTorch. Both work really well with Python, and while there is now an R interface for TensorFlow, much more deep learning work is being done with Python than with R. As deep learning becomes applicable to an increasingly wide range of domains (it started off with computer vision, now it’s becoming the default approach for most Natural Language Processing tasks as well) that’s increasingly important.
Similarity to other languages: While someone with a background in Lisp might be able to learn R fairly quickly, if someone has a background programming in a more popular general purpose programming language — like Java, C#, JavaScript or Ruby — they are going to find it easier to come up to speed with and contribute to a project written in Python.
There are still plenty of jobs where R is required, so if you have the time it doesn’t hurt to learn both, but I’d suggest that these days, Python is becoming the dominant programming language for data scientists and the better first choice to focus on.
R vs. Python: Which One to Go for?
When it comes to machine learning projects, both R and Python have their own advantages. Still, Python seems to perform better in data manipulation and repetitive tasks. Hence, it is the right choice if you plan to build a digital product based on machine learning. Moreover, if you need to develop a tool for ad-hoc analysis at an early stage of your project then go for R. The ultimate choice depends on which programming language you want to go. Till then — keep learning!
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