Simplifying Python Vs R
Hi folks, welcome back, thank you so much for your love and support, I hope you all are doing well. In today’s edition we will try to understand Python Vs R, and the use of individual in the respective applications.
“Data Scientist- The Sexiest Job of 21st Century.”- As mentioned by Harvard Business Review, is one of the hype that everyone around the globe is talking about. In learning data science everyone will come across of learning Python and R alongside. Often we usually come across creating confusions about learning Python or learning R, well the mystery is a mystery. So in this blog we will try to understand and crack the mystery of PYTHON Vs R and look forward in knowing what a good approach is.
The context is divided into stages which will take you in knowing about individual both Python and R, the Applications in which they play vital role as an individual followed by the Pros & Cons, the important individual qualities list of books to kick-start and finally the tabular summarization.
Both R and Python are open sources and free to use high-level programming languages. R is specifically developed for statistical computing. It has plenty of add-on packages / tools to support machine learning and data analysis. On the other hand, Python is a general purpose and powerful programming language with special applications in data preparation, data munging, and data analysis.
Millions of data scientists and statisticians use R programming to get away with challenging problems related to statistical computing and quantitative marketing. R language has become an essential tool for finance and business analytics-driven organizations like LinkedIn, Twitter and Bank of America. R language has an innovative package system that allows developers to extend the functionality to new heights by providing cross-platform distribution and testing of data and code. With more than 5K publicly released packages available for download, it is just a great programming language for exploratory data analysis language can easily be integrated with other object oriented programming languages like C, C++ and Java. R language has array-oriented syntax. R language is designed particularly for data analysis with a flexibility to mix and match various statistical and predictive models for best possible outcomes. R programming scripts can further be automated with ease to promote production deployments and reproducible research.
Applications of R:
· Ford — Ford uses open source tools like R programming and Hadoop for data driven decision support and statistical data analysis.
· Lloyd — The popular insurance giant Lloyd’s uses R language to create motion charts that provide analysis reports to investors.
· Facebook — Facebook uses R language to analyse the status updates and create the social network graph.
· Zillow- Zillow makes use of R programming to promote the housing prices.
Python is often preferred by computer programmers trying to develop skills in number crunching and analysis. While R is preferred more by mathematicians and statisticians. Python can help programmers play with data by allowing them to do anything they need with data — data munging, data wrangling, website scraping, web application building, data engineering and more. Python language makes it easy for programmers to write maintainable, large scale robust code.
“Python programming has been an important part of Google since the beginning, and remains so as the system grows and evolves. Today dozens of Google engineers use Python language, and we’re looking for more people with skills in this language.” — said Peter Norvig, Director at Google.
Programming in Python is similar to pseudo code and makes sense like one speaking English language, thus it makes it easier to learn and understand immediately. Python does not have in-built packages like R but it has support for libraries i.e. Scikit, Numpy, Pandas, Scipy & Seaborn which are very useful for learning data science, machine learning and can also be used for statistical analysis tasks.
Brian Curtin, Member of Python Software Foundation said that, “In Python programming, everything is an object. It’s possible to write applications in Python language using several programming paradigms, but it does make for writing very clear and understandable object-oriented code.” The Broadness (Public index 40k add-ons listed under 300 different categories), Efficient (dealing with large datasets transformations), ease of understanding and mastering python makes it the king of data science learning.
Applications of Python:
· Mozilla — Python programming is used by Mozilla for exploring their broad code base. Mozilla releases several open source packages built using Python.
· Dropbox — Dropbox, a popular file hosting service founded by Drew Houston as he kept forgetting his USB. The project was started to fulfil his personal needs but it turned out to be so good that even others started using it. Dropbox is completely written in Python language which now has close to 150 million registered users.
· Walt Disney — Walt Disney uses Python language to enhance the supremacy of their creative processes.
· Reddit — Entertainment and Social News website.
· Bit Torrent- File sharing software etc.
Pros & Cons
•R is great for prototyping and for statistical analysis.
•It has a huge set of libraries available for different statistical type analysis. Check The Comprehensive R Archive.
•RStudio IDE is a definitely a big plus. It eases most of the tedious tasks and fastens your workflow.
•The syntax could be obscure sometimes.
•It is harder to integrate to a production workflow.
•In my opinion, it is better suited for consultancy-type tasks.
•The libraries documentation isn’t always user friendly.
•Python is great for scripting and automating your different data mining pipelines. It is the de facto scripting language nowadays.
•It also integrates easily in a production workflow. Besides, it can be used across different parts of your software engineering team (back-end, cloud architecture etc.).
•The scikit-learn library is awesome for machine-learning tasks.
•Ipython (and its notebook) is also a powerful tool for exploratory analysis and presentations.
•It isn’t as thorough for statistical analysis as R, but it has come a long way these recent years
•In my opinion, the learning curve is steeper than R, since you can do much more with Python.
Books to kick-start
To learn Python- Click HERE
Python book Github Repository-
To learn R — Click HERE
R book Github Repository:
In the end, the choice between R or Python depends on:
The objectives of your mission: Statistical analysis or deployment, the amount of time you can invest, your existing company/industry most-used tool, if fresher then speak to the experts in the industry as to how they work on the hands on part and also approach as to how they accomplish the task, understanding the knowledge on basis of challenges the expert face. And after all that still you have doubt, use the one that is available and that gets the work done quickly.
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