Top 5 Programming Language for Data Science to Learn in 2021

Choosing a Language for Your Data Science Projects

For freshers and graduates looking to break into the world of data science, picking the right programming language can be a make or break decision. This guide to programming languages and their uses will help enhance students’ knowledge of data science-related languages and encourage them to make the right choice.

How are programming languages used in data science?

Types of Programming langauge

There are main two categories of Programming Languages

  • Low-level programming: This is considered the most understandable language that’s used by a computer to perform basic-level operations. More popular examples of this are assembly language and machine language, where assembly language is used to directly manipulate hardware or focus on performance-related issues and machine language is made up of binaries that are easily read and interpreted by a computer. 
  • High-level programming: Where low-level programming languages are detail-oriented, high-level ones are more geared towards abstractions. They are closer to human languages and are used by developers to create code which can be converted into machine language at a later stage. 

Data scientists, in particular, use high-level programming languages. These are primarily used to build analytics tools and technologies that help data scientists and other professionals extract insights from massive datasets and provide value to the business they’re affiliated to. 

The difference between programming languages in data science and regular software development is that most languages can build software but data science-oriented languages can process, scrutinise and create forecasts from a given data dump. Data-centric programming languages are the backbone of building and processing algorithms that can get as specific as required by the field of data science.

Check out the top data science roles.

Here are top 5 programming languages for data science

  1. Python
  2. R
  3. Java
  4. SQL
  5. Scala
Pros and cons of different programming language
Data science programming languages

1. Python

This high-level programming language is one of the most versatile as it contains a plethora of libraries that cater to different roles. It is considered easy to use as it is interpreter-based and has high levels of readability. The dynamic language has been around for nearly 30 years now and is used both by small businesses and industry titans like Google, Mozilla, Facebook and Netflix. Indeed also ranked it the third most profitable programming language in the world– yet another reason for it being so popular in the programming community.

Pros of Python

  • Easy to use: Since Python is fully focused on code readability, the language is versatile without being hard to read or understand.
  • Open-source: Python is free to download and you can start using it in a matter of few seconds. This is beneficial for everyone but especially for those looking to learn a programming language from scratch but doesn’t have the means to buy an expensive course or language package. 
  • An array of libraries: Whatever you need Python for, the language has a library for it. The most common ones are for machine learning, game development and web development.

Cons of Python

  • Threading problems: According to many users, Python can be tricky when it comes to threading because of the Global Interpreter Lock, which allows only a single thread to execute at a time. The hack is to carry out multiprocessing programmes instead of multiprocessing threads, but this can still be a problem for those looking for threads.
  • Not native to mobile: Developers often see Python as weak for mobile computing as it is not native to a mobile environment. It can still be leveraged for the purpose but requires an additional effort that may be beyond the purview of beginners.

2. R

When it comes to exploration of datasets and ad hoc analysis, R scores more points with data scientists. Yet another open-source programming language, R is geared towards statistical computing. It is also a key player in the process of developing numeric analysis and machine learning algorithms. It is often referred to as a ‘glue’ language, a reference to its role in connecting datasets, software packages and tools.

Pros of R

  • Reproducible analysis: R is the statistical tool of choice because it produces high-quality data analysis that can be reproduced and scaled. This flexibility allows R to be used on massive datasets and at organisational levels.
  • Strong packages: As it was built for statisticians, R has a vast array of packages that can be leveraged to nearly any end to further any statistical technique. Its charting and graphic abilities are also considered to be unmatched.

Cons of R

  • Old design: R is an old language and in that context, has not had many changes made to its design. This can be a bit problematic for those working with massive datasets, as it has not much kept up with changes in technology or use. 
  • Lack of inbuilt security: Security was not built into the R language, which means it cannot be embedded into a web browser for secure calculations. It is also difficult to use R like a back-end server for the purpose of building calculations.

3. Java

Java is another object-oriented, general-purpose language. This language tends to be highly versatile and is used in computer embedding, web applications and desktop applications. Java may seem to be disconnected to data science; however, there are many frameworks, including Hadoop, which run on JVM and constitute an integral part of the data stack. Hadoop is a software method for data processing and storage in distributed structures for large data applications. It allows large amounts of data to be processed and possesses the ability to handle virtually limitless tasks at once, thanks to its higher processing power.

Pros of Java

  • Straightforward: Java is one of the lesser complicated languages to learn and is very adaptable to writing, compilation and debugging in the process of development. The code is also reusable and usable in creating standard programmes.
  • Distributed computing: In this method, several computers come together on a single network to develop applications simultaneously. Java can be used in such a method, which promotes collaboration over both data and application-related aspects.
  • Independent of platforms: Typically, Java code runs on any computer without the need for special software. However, it does need the Java virtual machine (JVM), which allows computers to run both Java programmes and programmes crafted in other languages. 

Cons of Java

  • Memory-consuming: Java programmes run on top of Java Virtual Machines (JVM), which makes it consume a lot more memory. This could be problematic on systems without much internal memory. 
  • No support for low-level programming: Although similar to C and C++, Java has fewer low-level facilities in comparison. It is also much slower than these low-level programming languages and cannot support unions and structures.

4. SQL (Structured Query Language)

This domain-specific language is most used for handling data within a relatable database management system. Databases are quite often the backbone of software or an application and are instrumental in determining just how well dependent technologies perform. The more commonly used databases are Oracle, MariaDB, MySQL and PostgreSQL. 

Pros of SQL

  • Function-heavy: SQL is well known for being one of the most function-heavy languages but also has a concise syntax. The simpler commands are much easier to understand; however, complex setups and mastering the database’s design take a lot more time and effort. 
  • Speedy for searching and retrieving: Due to the levels of optimisation, SQL databases are said to be the fastest in carrying out data searches over just a single table. With an optimum design, such speeds can easily be achieved even across multiple tables.

Cons of SQL

  • Predefined data model: With SQL databases, data migration becomes an issue. This is because, when entering new columns deleting existing ones, every single row in the table gets affected. The way around this is building large-scale migration scripts to adjust existing data for every change.
  • Only vertically scalable: Architecturally, SQL databases can only be expanded vertically upon one server. To be able to expand to other servers, more expensive hardware needs to be brought in to the system, to be able to cope with massive data dumps and proportionate demands. 

5. Scala

Scala has been designed to address many of Java’s problems. Again, from web applications to machine learning, this language has many different uses, but this vocabulary mostly includes the development of the front end of applications. As the term itself is an approximation of “scalable language”, a nod to the fact that the language is considered to be scalable and, hence, perfect for processing big data.

Pros of Scala

  • Easy to understand: Especially for those with some prior knowledge of Java, Scala’s syntax might seem more understandable than any other language. It is also a lot more concise than Java is, making it less complicated for beginners looking to write code.
  • Scaleable: As the name suggests, Scala is a scalable language. This means it can be easily used to build fault-resistant systems that are concurrent. The fact that it is both object-oriented and functional makes it scalable, as does its support for higher-order functions, pattern-matching and abstractions.
  • Concise: Scala is concise and thus provides better support for functions in the back end. However, complexity can be managed by raising the level of abstraction in the existing interfaces.

Cons of Scala

  • Steep learning curve: For developers not familiar with Java, some features like continuations and functional programming might be difficult to process. Though the language spec is much smaller than Java’s, the way things are combined is quite unlike Java, which is the source of the relatively steep learning curve. 
  • Limited developer pool: Scala has fewer developers than Java does, which could be a problem for organisations looking to staff up immediately. It could also be a hindrance for students who are trying to learn Scala and are looking for a mentor or guide. That said, the more the language is explored, the higher the chances of the pool growing in size.
Cons of Scala

Each of these languages have their indicative purposes, eg: Scala for front-end applications and R for statistical analysis. Thus, the final decision on which programming language to choose, depends on the student’s field of interest (front-end, statistical analysis, back-end etc), and the uses and benefits of the language in the said field. 

If you wish to upskill in the field of Data Science, check out the free courses offered by Great Learning Academy. There are a number of Data Science courses available, such as-

Data Science Foundations
Introduction to R
Basics of Exploratory data analysis
Data Visualization using Tableau
Statistical Methods for Decision Making, and more!


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