Do you enjoy statistics and programming? Are you currently studying math and statistical learning (such as machine learning)? Are you excited to learn the latest technologies and techniques in data science? If your answers are more than just one yes, your career path may take you to data analysis very soon.
Many students in programming and statistics can find a very remunerative career in data science, as it is an ever-growing field with a lot of potential. But right from the start, you have to ask yourself how you are going to approach this discipline and how you are going to tackle the programming challenges ahead of you. And here comes the question that all data scientists had to answer at the very beginning of their careers: should I learn Python or R programming to start working on data analysis?
This is a tough question since Python and R are both versatile programming languages in data statistics. They were born in the same period (the late 80s or the start of the 90s) and both have proven themselves as very useful tools in data mining. Here you can find the pros and cons of using the two languages, so you may decide which one best suits your needs.
Python was developed to offer a way to write scripts to automate some of the routine tasks encountered on a daily basis. However, as time went by, Python has evolved and become quite useful in many other fields, especially data analysis.
On the other hand, R is a programming language as well as an open source software for both graphics and data analytics. It has the advantage of running on any computer system and is used by data miners and statisticians for both presentation and analysis of their data.
Python vs R Programming for Data Analysis
It is a common challenge for a data scientist to decide whether to use Python or R for data analysis. While R was purely developed for statisticians, making it portray analysis a specific advantage for visualizing data, Python stands out with its general-purpose characteristics and the fact that it has a very regular syntax. Based on these differences, it is necessary to compare the two languages to determine which one suits them best.
Python Programming Language
- – Python programming language was inspired by Modula-3, ABC and C languages
- – Python focuses on code readability and productivity
- – It is easier to develop code and debug because of its easy-to-use and simple syntax
- – Code indentation affects its meaning
- – All pieces of functionality are often written in the same style
- – Python is very flexible and can also be used in web scripting.
- – It has a relatively gradual and low learning curve for it focuses on simplicity and readability
- – Suitable for those beginning to program
- – Its Package index is called PyPi. Its Python’s software repository with libraries. Although users have the option of contributing to Pypi. It is difficult in practice.
- – RPy2 is the library which can be used within Python to run R code. Used in providing a low level to R from Python.
- – In 2014, Dice Tech Salary Survey showed the average salary of an experienced expert was $94139
- – It is mainly applied when there is a need for integrating the data analyzed with a web application or the statistics is to be used in a database production
- – The capability to handle data was a challenge for it in the past although it has improved, this was due to its package infancy in data handling
- – You must use tools like pandas and NumPy to enable it to be used for data analysis
- – IDEs available include Spyder, IPython Notebook.
- – S programming language inspired R.
- – Emphasizes on data analysis methods, graphical models and statistics that are user-friendly.
- – It is slightly hard to use since statistical models are only written using few lines.
- – There exist R stylesheets, although they are rarely used
- – There are many ways of representing or writing the same functionality piece.
- – Offers the ease of using complex R formulas. For its many statistical models and tests.
- – Has a learning curve that is steep at the beginning when learning the basics. But it becomes very easy to learn advanced topics later on
- – Not very hard for expert programmers.
- – Comprehensive R Archive Network (CRAN). CRAN is the R repository package that is easily contributed to by the users.
- – The rpython package is used from R to run Python code.Call Python methods or functions and for getting data.
- – In 2014, Dice Tech Salary Survey showed the average salary of an experienced expert was $115 531
- – Mainly applied when the analysis requires independent computing or individual servers.
- – Easier when used for a critical task for beginners. Employs few code lines to write statistical methods.
- – Ideal for handling data from its large package number. Usable tests and the use of formulas.
- – R does not require additional packages for basic analysis. It only requires packages like dplyr for big datasets.
- – Uses R studio IDE
Analysis done by KDnuggets polls in 2014 for Python vs. R used together showed that:
R Programming = 58%
Python Programming = 42%
Python + R Programming = 23.45%
Python – Pros
- The IPython Notebook facilitates and makes it easy to work with Python and data. This is from the fact that you can share notebooks with other people without necessarily telling them to install anything. Which reduces code organizing overhead, hence allowing one to focus on doing other useful work.
- Given that it is a general-purpose language, it is intuitive and simple. It enables a data scientist with a flat learning curve which in turn allows him to increase his program writing skills. Python also has an inbuilt framework for testing which encourages improved test coverage, which in turn is a guarantee of one’s code being dependable and reusable
- It is a multi-purpose programming language bringing together people with various backgrounds, that is, statisticians and programmers.
Python – Cons
- Visualization is a crucial factor when determining the data analysis software to use. Python offers several libraries for visualization like Boken, Pygal, and Seaborn which may, in turn, be too many to pick. And unlike R, its visualizations are convoluted and not attractive to look.
- Python is just an R Challenger and doesn’t substitute the many R packages that are essential.
R – Pros
- R offers clear visualization of data, making the data efficiently designed and understood. Examples of its visualization packages are ggvis, ggplot2, rChart, and googleVis.
- R has a broad ecosystem of active community and desirable packages. The packages are available at Github, BioConductor, and CRAN.
- It was developed, for statisticians, by statisticians. Hence, they can communicate concepts and ideas through R packages and code.
R – Cons
- If you compare the speed of Python vs R, R is slow because of its code that is poorly written. Packages that can improve its performance include Renjin, PQR, FastR.
- R has a very steep non-trivial learning curve. Especially if you have a graphical user interface (GUI) background that were used for statistical analysis. Finding simple utilities and packages can be very hard.
It is clear that both the languages have their own advantages and disadvantages and it depends on your personal preferences to pick one that will solve your problems.1