Data science is one of the most trending technological innovations of the 21st century. Deriving its core from basic statistics, mathematics, and programming, this vast domain has become pivotal in the working of millions of companies, across sectors, around the globe. Because of the value it brings to the table, it has also become one of the most popular career choices for IT professionals. To top it all off, data scientists are some of the most handsomely paid professionals in the world.
As one of the major foundations of a data scientist’s skillset is programming, it often becomes difficult for a budding professional to choose a programming language. This is not just because of the abundance of programming languages available in the market today, but also the features that comes in tow with each of these.
To help ease your learning curve for data science, here’s a curated list of the pros and cons of the popular three programming languages used in this domain.
Python is one of the most popular general-purpose programming languages in the world. It is also one of the few programming languages that is versatile and can be used in almost any kind of application. Let’s now look at a few pros and cons of Python when we add data science to the mix.
- Python has an extensive library of purpose-built modules that can be used to build robust algorithms and models.
- The vast number of libraries and packages available for Python including Pandas, TensorFlow etc. make Python ideal for working on machine learning and deep learning.
- Python also has you covered in terms of data visualization with libraries like Matplotlib, Seaborn etc.
- Python has a very gentle learning curve and mastering the syntax of this language is not that difficult.
- Coupling Python with other tools like Apache Hadoop, Apache Spark etc. for big data analytics is quite straightforward.
- Python is open-source and free to use.
- The Python community is one of the most active and resourceful communities in the world of programming.
- When it comes to statistical and analytical purposes, there are other languages like R that offer more packages.
- Certain other languages are faster and safer than Python.
- Python is dynamically typed. This means that type errors are quite common and could become frustrating at times.
R is a programming language that has become almost synonymous to data analytics and statistics. Because of this, a large portion of companies hiring for data science professionals today make it mandatory for potential interviewees to know R. But, as it goes, even R has its advantages and disadvantages when it comes to applications in data science.
- R’s library of packages for statistical and analytical applications cannot be rivalled by any other programming language. This makes it perfect for data science and analytics.
- The basic installation of R is powerful enough to handle most analytics and statistics without any add-ons.
- R is one of the few languages that tackles multi-dimensional calculations efficiently and quickly.
- R also has a bunch of data visualization libraries like ggplot2.
- Just like Python, R is also an open-source language.
- R is in no way a versatile language. It handles data science and analytics with grace but that’s about it. It will not be your first choice for any other application.
- There are a few unconventional trends that R follows that might need some getting used to for programmers who’ve worked on other languages.
- R is not one of the quicker languages out there.
Scala is a programming language that goes hand-in-hand with big data analytics. Primarily used in the cluster computing framework, Apache Spark, Scala has gained a lot of popularity over the years in the big data, data engineering, and data science domains. Similar to the other two languages in this list, Scala too has its own benefits and drawbacks.
- Scala is ideal for dealing with huge volumes of data and big data.
- Scala is a multi-paradigmatic language which means programmers have the choice to either work with the object-oriented paradigm or the functional paradigm.
- As Scala is run on JVM, it can be interoperable with Java. This makes it a very powerful general-purpose programming language.
- Just like Python and R, Scala is also open-source.
- Scala is not the easiest language for a novice programmer.
- Writing programs in Scala is quite challenging because of its complex syntax and type system.
The aforementioned were the three most popular programming languages in the data science and analytics field right now. As mentioned earlier, each of these has its own set of pros and cons which makes them ideal in particular scenarios and applications, and less so in others. You may also be interested in reading an article on the history of programming.
We hope this post helped you differentiate between three of the most popular programming languages in the field of data science and decide which one of these is the best choice for you. So, what are you waiting for? Choose a language and master data science today to secure your career.