Programming languages are an integral part of learning data science. They are the fundamental skills in a data scientist toolbox and important to almost every data science task. Once you choose a programming language for your data science project, you’re tied to it unless you want to perform a major revamp to your data science product at some point down the line – something that a data scientist would never want to do. That’s why choosing the right programming language is important to the success of a data science project. You might have probably done some research into the right programming language for data science, but it’s difficult for someone without data science expertise to determine which one is right. Choosing between programming languages is often confusing, especially if you have to choose between the two most popular ones. Python vs R is the new furor for all those who want to learn data science.
Superman vs Batman. Coke vs Pepsi. Star Wars vs. Star Trek. Amazon vs Flipkart. The choice between Python vs. R isn’t really that kind of a rivalry – the two programming languages have diverse use cases and fan bases. From our experience at Springboard, which programming language to learn is one of, if not the first question, that someone interested in learning data science wants to be answered. Data Science is a multi-disciplinary field and it can be daunting for beginners without mentor guidance. We have found that any guidance in determining what programming languages, tools, and specific tasks, to begin with, is highly appreciated by aspirants.
We’ve written this article to give guidance to people wanting to start learning more about data science and needing help in choosing a programming language. This article is also helpful for data science professionals that wonder which language and data science libraries work best in a given scenario.
“Python vs R” or “Python and R”
Python and R programming languages have been fighting the battle for the top position on the most popular programming languages for data science out there. Battling for the award of the best data science programming language, the two hottest contenders Python and R have their own strengths and weaknesses. It often seems that Python and R are perfect for doing data science, and in fact, they are capable of doing most of the data science tasks out there. However, there are a few major differences that can help a data scientist formulate their decision –
Python vs R -The Basics
Python and R both, are open-source, powerful and highly extensible programming languages. Python was developed as a general-purpose programming language while R was for statistical analysis. The sophisticated approach of these languages makes it easy for data scientists to execute tasks with better readability, stability, and modularity. R is more of tools developed for data analysis similar to the S language while Python is a full-service object-oriented programming language.
Pros of Python Programming Language
- Python is a full-fledged object-oriented programming language and a great tool to deploy algorithms for production use.
- A flatter learning curve as it promotes easy-to-understand syntax when compared to R.
- It utilizes GPU much better, making it easier and faster to build deep learning models with Keras, TensorFlow or Theano.
- Provides better integration than R in engineering environments.
- Python scores over R programming language with its huge developer community.
Cons of Python Programming Language
- As it is a dynamically typed language, it becomes difficult to search for a few functions and detect any errors associated with the assignment of different data to the same variables.
- The lack of alternatives to many R libraries. For instance, R has many biostatistics focussed libraries which Python does not. However, it is possible to implement those in Python as well but it is much easier to use the existing ones in R for those analyses like- survival models, longitudinal data analysis, and more.
- Threading is a little tricky and problematic in Python because of the Global Interpreter Lock. This slows down multi-threaded CPU bound applications. So, as a data scientist if you are to implement a machine learning project, implement multiprocessing instead of multithreading.
Pros of R Programming Language
- R supports great data visualizations compared to Python as it does not have too many libraries to choose from when it comes to data visualization.
- It is great for statistical analysis as it requires only a few lines of code.
- If you are in the initial stages of your data science project and need to do exploratory work on statistical models- it is easier to do it in R than in Python.
Cons of R Programming Language
- Difficult to learn
- It has a large number of libraries and the documentation for the less popular ones is not complete enough for a beginner to follow.
- R stores data in RAM and thus data scientists are confined with the use of the big data as data handling in R depends on RAM capacity. However, leveraging Hadoop HDFS connectors will result in considerable performance improvement.
Python vs R -Usage
R is a good choice for exploring large datasets and ad-hoc analysis while Python is meant for data manipulation and repeated data science tasks. R has gained importance in the data science community for statistics-heavy data science projects and a great choice if you want to work with extensive research scientific data. R is also a good choice for data science projects that require a one-time dive into the dataset.
Python can be a great choice when pulling the data and automating analysis over and over, to create data visualizations such as charts or maps from the results while R works best for text analysis where you need to identify patterns by deconstructing paragraphs into phrases or individual words.
Python vs R – Data Science Libraries
R has more than 5000 tools and libraries for different domains focussed on improving the performance of machine learning projects. For example, Caret provides added value to machine learning capabilities in R by helping data scientists create efficient predictive models. The statistical modeling packages in R are extensive and powerful compared to Python. Python also has multiple data science libraries for data manipulation, data wrangling, data collection, and machine learning. For example, Scikit-learn developed over SciPy, NumPy, and Matplotlib has all the required tools for data analysis and data mining.
Python vs R -Learning Curve
People who do not have any prior programming experience may find learning R language a little overwhelming and picking up Python is easier. Python is a general-purpose programming language that reflects the thinking of a computer programmer while R reflects the thinking of a statistician. Computer programmers who transition into data science often find the design of R a little frustrating and irritating as it is totally different from what they’re used to working with. Python has become the gold standard throughout the industry as it enables easy collaboration and provides substantial R-like packages for data analysis. People who do not know R can simply learn Python and make use of RPy2 (an interface to the R language). As a data scientist, you can enjoy the power of both programming languages in one i.e. running embedded R in Python.
And The Winner Is…?
There is no clear winner! You really need to get over this! It’s not “Python vs R”, it’s Python and R.
It is impossible for anyone to know everything in the world of data science. Surround yourself with the best data science tools that can solve whatever challenges you have. When it comes to choosing a preferable language for learning data science, most of the aspirants agree that both Python and R should be talked about at the same time. It is difficult to choose one as both are equally flexible data science programming languages. Just go with the mindset that “if I use Python, it’s the right one.” and “if I use R, it’s the right one” for any given data science problem. There is no clear winner in the world of data science. Whether it is designing machine learning algorithms to analyze data or automating complex data science tasks, each of these languages can be used to successfully deploy solutions and glean valuable business insights. The best way to make a firm decision is to consider your use case and then decide which language can help you design an efficient solution.