- Data Science is one of the fastest-growing industries with an enormous number of tools to satiate your needs
- Let’s talk about the different data science languages and determine how to choose the best language
Python or R or SAS? Which data science language should I learn?
Raise your hands if you’ve ever asked this question or have answered it before. I’m fairly certain all of you will have come across this eternal dilemma about choosing the “perfect” programming language to start your data science career.
Here’s the thing – there is no one size fits all approach here. There is no so called “perfect” language for data science. Each language has it’s own unique features and capabilities that make it work for certain data science professionals.
And the choice isn’t limited to Python, R and SAS! We are living in the midst of a golden period in programming languages as we’ll see in this article.
Some languages may be suitable for fast prototyping while others may be good at the enterprise level. So let’s clear the confusion once and for all and see which is the best language that suits your data science career goals.
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Table of Contents
- The Data Science Language Contenders
- Points of Comparison for these Data Science Languages
- Ease of learning
- Data Handling Capabilities
- Graphical capabilities
- Job Scenario
Let’s see our Data Science Language Contenders
Python is a general-purpose, high-level interpreted language that has been growing rapidly in the applications of data science, web development, rapid application development. Its ease of use and learning has certainly made it very easy to adapt for beginners.
Python has efficient high-level data structures and effective execution of object-oriented programming. It has a comprehensive base library along with a large number of libraries for data science making it one of the most strong competitors.
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Love statistics? Make R your bestfriend!
R is a language and environment for statistical and mathematical computation along with an extensive library for plotting graphs. It is great at data-handling capability and efficient array operations R is an open-source project.
R consists of a considerable number of statistical functions and libraries for linear and non-linear modeling, time-series modeling, clustering, classification, and much more. What sets R apart from general purpose data science languages? It consists of high-quality plots which will surely help you in your analysis.
“Walks like python. Runs like C.”
This quote by Julia gives a gist about the language. Julia was developed at the prestigious MIT and its syntax is devised from other data analysis libraries like Python, R, Matlab.
It is a high-level language that has syntax as friendly as Python and performance as competitive as C. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.
You can get started with Julia today with this amazing article –
Java is the least taught language for data science but the majority of deployed machine learning projects are written in this language. It was initially developed by James Gosling at Sun Microsystems and later acquired by Oracle.
It is a general-purpose high-level language and it has grown to be one of the most popular and adopted languages for applications in the field of mobile and web development. Many of the big data applications like Hadoop, Hive have been written in Java. Also with the advent of popular machine learning libraries like Weka, Java has found popularity amongst data scientists.
C/C++ is probably one of the older languages but they are still relevant to date in the field of data science. Although you won’t find any fancy libraries for machine learning like those available within Python but these languages have strong relevance in the field of big data like the implementation of MapReduce framework for C/C++.
C/C++ is a low-level language that causes it to be less popular amongst data scientists but its computational speed is incomparable.
So, WHich Data Science Language is Right For You?
Here, we’ll use a framework to compare each data science langauge we mentioned above. The idea is to help you understand which points work for you so you can pick the language that’s suitable for your career.
Ease of Learning
There is no doubt that Python is one of the simplest and most elegant languages. Its ease of use has made it the go-to language. It doesn’t even have a variable declaration! It’s that simple. These features help you focus on what’s important and not spend your majority of time debugging your script.
R has a very specific group of users whose main focus is on statistical analysis. Therefore you must be accustomed to statistical concepts beforehand. From a programming point of view, R has a steep learning curve. It requires you to learn and understand coding. It is a low-level programming language and hence simple procedures can take longer codes.
As mentioned above, Julia inherits its syntax from some of the existing data science languages like – Python, R, and Matlab therefore if you have used these languages before then you won’t find it difficult to jump to this language.
If you come from a programming background, you must already be familiar with languages such as Java and C/C++. The former is relatively easier to learn while the latter is quite vast and takes a long to master.
For programmers, you can definitely jump to machine learning from your preferred language but for newcomers, you can begin with Python or R.
Data Handling Capabilities
R computes everything in memory (RAM) and hence the computations were limited by the amount of RAM on 32-bit machines. This is no longer the case. Python and R have good data handling capabilities and options for parallel computations. This I feel is no longer a big differentiation.
Julia has exceptional data handling capabilities and is much faster than Python runs efficiently like C language.
Most of the popular frameworks and tools used for Big Data like Fink, Hadoop, Hive, and Spark are typically written in Java. This includes Fink, Hadoop, Hive, and Spark.
C/C++ is a relatively low-level language and offers much more efficiency and speed but it is obviously a time-consuming task.
An important aspect of any data science project is the quality of its visualizations. Your first data science language must be great in its visualization capabilities.
Python comes with a great set of visualization libraries like matplotlib, plotly, seaborn. You can form visualize your data in form of bar charts, scatter charts, etc and customize the size and axis according to your needs.
R has a very stronghold in data visualization. It was built for analysts and statisticians to visualize the results. ggplot is one of the beloved libraries. You can make static and dynamic graphs that are surely going to express your data in an intuitive manner.
Julia is still at a nascent stage for data visualization and community support. It doesn’t offer the variety that Python and R offer but don’t mistake it for being a loser. JuliaPlots offers many plotting options that are simple yet powerful.
Java and C/C++ are usually used in applications that require more customization, and application-specific projects. These don’t consist of well-known data visualization libraries like Python and R.
If you look forward to a data science-based role which requires data visualization at high frequency than I’d suggest you to take up R (for statistical analysis) or Python (machine learning and deep learning)
Do you wonder why community matters? Community contribution becomes the predominant factor when you work with open-source libraries. Since these libraries are totally free of cost, it is the contributors that make any library successful. The only drawback of all these languages is that there is no customer support.
Python and R have a very strong community for data science and data analytics and that’s how we have hundreds and thousands of new libraries entering the spectrum. A lot of professionals are getting comfortable with Julia and hence the community is growing.
Java, C/C++ does not have a strong community when it comes to data science and analytics.
Python and R are the most adopted open-source data science languages, startups are looking towards hiring professionals with these skillsets. Companies hiring specifically for Julia are definitely very low. These companies usually mention Julia’s skill as an addition or organization working in the research domain.
Enterprise companies still use Java as their main language for deploying data science projects. Thereby, having Java as an essential skillset.
C/C++ for machine learning projects are either used by research organizations or by enthusiasts.
The best way to judge each language on the points of differentiation is by making your career goal clear and then going through each point one-by-one.
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I hope this article helps you in taking that first step to select amongst the languages for your data science career. Let me know if you have any other favorite languages and how has been your experience with it. 🙂