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Top 10 Programming Languages to Learn for Data Science

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28th Jun, 2021
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Top 10 Programming Languages to Learn for Data Science

Data science is one of the hottest fields in the tech domain today. Although an emerging field, data science has given birth to numerous unique job profiles with exciting job descriptions. What’s even more exciting is that aspirants from multiple disciplines – statistics, programming, behavioural science, computer science, etc. – can upskill to enter the data science domain. However, for beginners, the initial journey might get a little daunting if one doesn’t know where to start. 

At upGrad, we’ve guided students from different educational and professional backgrounds across the world and helped them enter the world of data science. So, trust us when we say it’s always best to start your data science journey by learning about the tools of the trade. When looking to master data science, we recommend you begin with programming languages. 

Now the important question arises – which programming language to choose? 

Let’s find out!

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Best programming languages for Data Science

The role of programming in Data Science generally comes when you need to do some number crunching or create statistical or mathematical models. However, not all programming languages are treated alike – some languages are often preferred over others when it comes to solving Data Science challenges. 

Keeping that in mind, here’s a list of 10 programming languages. Read it till the end, and you’ll have some clarity in terms of what programming language would best suit your data science goals. 

1. Python

Python is one of the more popular programming languages in the Data Science circles. This is because Python can cater to a wide array of data science use cases. It is the go-to programming language for tasks related to data analysis, machine learning, artificial intelligence, and many other fields under the data science umbrella.

Python comes with powerful, specialized libraries for specific tasks, making it easier to work with. Using these libraries, you can perform important tasks like data mining, collecting, analyzing, visualizing, modelling, etc. 

Another great thing about Python is the strong developers’ community that will guide you through any possible challenging situations and tasks. You’ll never be left without an answer when it comes to Python programming – someone from the community will always be there to help solve your problems. 

Mostly used for: While Python has specialized libraries for different tasks, its primary use case is automation. You can use Python to automate various tasks and save a lot of time. 

The good and bad: The active developers’ community is one of the biggest reasons why aspiring programmers and experienced professionals love Python and steer towards it. Also,  you get many open-source tools related to visualization, machine learning, and more to help you with different data science tasks. There are not many cons to this language, except that it is relatively slower than many other languages present on this list – especially in terms of computational times. 

2. R

In terms of popularity, R is second only to Python for working with data science challenges. This is an easy-to-learn language that fosters the perfect computational environment for statistics and graphical programming. 

Things like mathematical modelling, statistical analysis, and visualization are a breeze with the R programming language. All of this has made the language a priority for data scientists across the world. Further, R can seamlessly handle large and complex datasets, making it a suitable language for dealing with the problems arising from the ever-increasing heaps of data. An active community of developers backs R, and you’ll find yourself learning a lot from your peers once you embark on the R journey!  

Mostly used for: R is hands-down the most famous language for statistical and mathematical modelling. 

The good and bad: R is an open-sourced programming language that comes with a solid support system, diverse packages, quality data visualization, as well as machine learning operations. However, in terms of cons, the security factor is a concern with the R programming language. 

3. Java

Java is a programming language that needs no introduction. It has been used by top businesses for software development, and today, it finds use in the world of data science. Java helps with analysis, mining, visualization, and machine learning. 

Java brings with it the power to build complex web and desktop applications from ground zero. It’s a common myth that Java is a language for beginners. Truth be told, Java is suitable for every stage of your career. In the field of Data Science, it can be used for deep learning, machine learning, natural language processing, data analysis, and data mining. 

Mostly used for: Java has been mostly used for creating end-to-end enterprise applications for both mobiles and desktops. 

The good and bad: Java is much faster than its competitors because of its garbage collector abilities. Thus, it is an ideal choice for building high-quality, scalable software. The language is extremely portable, and offers the write once, run anywhere (WORA) approach. On the downside, Java is a very structured and disciplined language. It isn’t as flexible as Python or Scala. So, getting the hang of the syntax and basics is pretty challenging. 

4. C/C++

C++ and C are both very important languages in terms of understanding the fundamentals of programming and computer science. In the context of data science, too, these languages are extremely useful. This is because most new languages, frameworks, and tools use either C or C++ as their codebase. 

C and C++ are preferred for data science owing to their quick data compilation abilities. In this sense, they offer much more command to developers. Being low-level languages, they allow developers to fine-tune different aspects of their programming per their needs.

Mostly used for: C and C++ are used for high-functioning projects with scalability requirements. 

The good and bad: These two languages are really fast and are the only languages that can compile GBs of data in less than a second. On the downside, they come with a steep learning curve. However, if you’re able to get control of C or C++, you’ll find all other languages relatively easy, and it’ll take you less time to master them! 

5. SQL

Short for Structured Query Language, SQL is a vital role if you’re dealing with structured databases. SQL gives you access to various statistics and data, which is excellent for data science projects. 

Databases are crucial for data science, and so is SQL for querying the database to add, remove, or manipulate items. SQL is generally used for relational databases. It is supported by a large pool of developers working on it. 

Mostly used for: SQL is the go-to language for working with structured, relational databases and querying them. 

The good and bad: SQL, being non-procedural, doesn’t require traditional programming constructs. It has a syntax of its own, making it a lot easier to learn than most other programming languages. You don’t need to be a programmer to master SQL. As for cons, SQL features a complex interface that might seem daunting to beginners initially.

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MATLAB has for long been one of the go-to tools when it comes to statistical or mathematical computing. You can use MATLAB to create user interfaces and implement your algorithms. Its built-in graphics are varied enough and extremely useful for designing user interfaces. You can use the in-built graphics for creating visualizations and data plots. 

This language is particularly useful for data science because it is instrumental in solving Deep Learning problems. 

Mostly used for: MATLAB finds its way most commonly in linear algebra, numerical analysis, and statistical modelling, to name a few. 

The good and bad: MATLAB offers complete platform independence with a huge library of in-built functions for working on many mathematical modelling problems. You can create seamless user interfaces, visualizations, and plots to help explain your data. However, being an interpreted language, it will tend to be slower than many other (compiled) languages on the list. Further, it’s not a free programming language. 

7. Scala

This is a very powerful general-purpose programming language that has libraries specifically for data science. Since it is easy to learn, Scala is the ideal choice of many data science aspirants who’ve just started their journey. 

Scala is convenient for working with large data sets. It works by compiling its code into bytecode and then runs it on a VM (Virtual Machine). Because of this compilation process, Scala allows for seamless interoperability with Java – opening endless possibilities for data science professionals. 

You can use Scala with Spark and handle siloed data without any hassles. Further, owing to the concurrency support, Scala is the go-to tool for building Hadoop-like high-performance data science applications and frameworks. Scala comes with more than 175k libraries offering endless functionalities. You can run it on any of your preferred IDEs such as VS Code, Sublime Text, Atom, IntelliJ, or even your browser. 

Mostly used for: Scala finds its use for projects involving large-scale datasets and for building high-functionality frameworks. 

The good and bad: Scala is definitely an easy-to-learn language – especially if you’ve had any experience with programming earlier. It is functional, scalable, and helps in solving many Data Science problems. The con is that Scala is supported by a limited number of developers. While you can find Java developers in abundance, finding Scala developers to help you might be difficult. 

8. JavaScript

Although JavaScript is most commonly used for full-stack web development, it also finds application in data science. If you’re familiar with JavaScript, you can utilize the language for creating insightful visualizations from your data – which is an excellent way to present your data in the form of a story. 

JavaScript is easier to learn than many other languages on the list, but you should remember that JS is more of an aid than a primary language for data science. It can serve as a commendable data science tool because it is versatile and effective. So, while you can go ahead with mastering JavaScript, try to have at least one more programming language in your arsenal – one that you can use primarily for data science operations. 

Mostly used for: In Data Science, JavaScript is used for data visualizations. Otherwise, it finds use in web app development. 

The good and bad: JavaScript helps you create extremely insightful visualizations that convey data insights – this is an extremely pivotal component of the data analysis process. However, the language doesn’t have as many data science-specific packages as other languages on the list. 

In Conclusion

Learning a programming language is like learning how to cook. There’s just so much to do, so many dishes to learn, and so many flavors to add. So, just reading the recipe will be no good. You need to go ahead and make that first dish – no matter how bad or good it turns out to be. Likewise, no matter which programming language you decide to go ahead with, the idea should be to keep practicing the concepts you learn. Keep working on a small project while learning the language. This will help you see the results in real-time. 

If you’re in need of professional help, we’re here for you. upGrad’s Professional Certificate Programme in Data Science for Business Decision Making is designed to push you up the ladder in your Data Science Journey. We also offer the Executive PG Program in Data Science , for those interested in working with mathematical models for replicating human behaviour using neural networks and other advanced technologies. 

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If you’re looking for a career change and are seeking professional help – upGrad is just for you. We have a solid 85+ countries learner base, 40,000+ paid learners globally, and 500,000+ happy working professionals. Our 360-degree career assistance, combined with the exposure of studying and brainstorming with global students, allows you to make the most of your learning experience. Reach out to us today for a curated list of courses around Data Science, Machine Learning, Management, Technology, and a lot more! 


Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.
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Frequently Asked Questions (FAQs)

1Which among all these languages is best for data science?

Although all of these languages are apt for data science, Python is considered to be the best data science language. The following are some of the reasons why Python is best among the best:
1. Python is much more scalable than other languages like Scala and R. Its scalability lies in the flexibility that it provides to the programmers.
2. It has a vast variety of data science libraries such as NumPy, Pandas, and Scikit-learn which gives it an upper hand over other languages.
3. The large community of Python programmers constantly contributes to the language and helps the newbies to grow with Python.
4. The inbuilt functions make it easier to learn as compared to other languages. In addition, data visualization modules like Matplotlib provide you a better understanding of things.

2Is one programming language sufficient to become a Data Scientist?

It is often said that learning Python alone can serve all your requirements as a Data Scientist. However, when you work in an industry, you have to use some other languages as well to efficiently handle real-life use cases.
Python has a rich and powerful library and when you combine it with other programming languages such as R (which has an extensive set of computational tools for statistical analysis), can enhance the performance and increase scalability.
As Data Science primarily deals with data, along with programming languages, having the knowledge of databases is also essential for a Data Scientist.

3 What are the other skills to be learned along with a programming language to be a data scientist?

Just a programming language is not sufficient to be a successful Data Scientist. It takes a lot more than that to be called a Data Scientist. The following skills are necessary to be a full-fledged Data Scientist:
1. Mathematical concepts like Probability and Statistics.
2. Deep understanding of Linear Algebra and Multivariate Calculus.
3. Database Management System (DBMS) like MySQL and MongoDB.
4.Cloud Computing platforms like Power BI and Tableau.
5. Data Visualization.
6. Subdomains of Data Science like Deep Learning and Machine Learning.
7. Advanced concepts of Data analysis and manipulation.
8. Model deployment and data wrangling.
9. Soft skills like communication and storytelling skills.

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