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Top 5 Programming Languages You Need for Data Science Jobs

Whatever the specific field of focus, if you want to build your data analytics skill set or get a data science job, you’ll need a good knowledge of a range of data science programming languages.

We take a look at the top 5 programming languages used in data science in 2021 and examine how you can get the best of each one.  

1) Python

Level

Beginner

Why’s it so great?

Still, while it’s strength lies in its ease-of-use and flexibility, the area where Python really shines is in artificial intelligence and machine learning. And, with so many libraries and packages available and a massive community, using Python is a great way to collaborate. 

What can you do with it?

Pretty much anything! There’s a reason Python is the most widely used data science programming language in the world right now. Not only is it extremely flexible, but it’s very easy to learn Python – it’s one of the most beginner-friendly programming languages out there. In terms of sheer pick-up-and-play factor, Python leads the pack.

 

2) SQL

Level

Beginner

Why’s it so great?

One of the best data science programming languages for beginners, SQL is a ‘non-procedural language.’ This means its semantics are very simple and the coder only has to specify “what to do”, not “how to do it.” Simple, solid and loved by a huge community, SQL is – despite being almost 50 years old – one of the most relevant programming languages today.

What can you do with it?

If you’re dealing with databases, you’re going to need to know SQL. Creating and adapting database structure, querying, reorganizing, controlling data access – SQL is essential for these areas. It’s also possible to write SQL code in other programming languages, including Python and R.  

 

3) Julia

Level

Beginner

Why’s it so great?

Created specifically for data scientists in 2009 as a faster alternative to Python, Julia is another programming language that is becoming increasingly popular. It’s versatility, speed and simplicity have meant that downloads of Julia have grown 78% since 2018.

What can you do with it?

It’s strength lies in data visualization, data mining, deep learning and anything that uses multi-dimensional datasets. For any kind of machine learning or data science projects, this versatile programming language is perfect. It’s designed with performance and productivity in mind, which is why it can be used for low-level programming, as well as high-end operations. 

While it’s growing fast, the Julia community is still relatively small. This makes collaboration difficult for now, although hopefully not for long. For any data scientists looking to stay ahead of the curve, getting to grips with this emerging programming language looks to be a very smart move.

 

4) Java

Level

Intermediate

Why’s it so great?

It’s lightning fast, highly portable and, unlike most programming languages, has true garbage collection.

What can you do with it?

You can build an entire application from scratch. Java is a key programming language when it comes to developing robust data science applications, covering data analysis, data mining and machine learning.  

It’s a lot harder to get to grips with than many other programming languages, particularly Python with its easily readable code. Still, with backwards compatibility and ability to build high-grade applications, Java is definitely relevant to anyone entering the field of data science. 

 

5) R

Level 

Intermediate

Why’s it so great?

In terms of popularity and uptake, R is one of the fastest growing programming languages on the planet. Extensible and relatively easy to learn, R is also great for open source projects.

What can you do with it?

R is a very powerful programming language, making it ideal for handling large amounts of complex data sets. Big data, machine learning and statistical analysis are all ideal, which is why 36% of data scientists regularly use R.

It also provides great extensibility, with impressive functions that include specialised techniques, enabling data scientists to develop very powerful customizable tools. Adaptability is another strong point – R can be used in tandem with other data science programming languages, including Python, C++ and Java. 

 

Learn programming languages with CodeOp’s Data Science Course

Are you ready to kick-start your data science career? At CodeOp, you’ll learn to master the key programming languages necessary for a range of careers in data science.

We also cover all the aspects of data science needed to thrive in the industry, from technical skills to soft skills and everything in between, while supporting you through every step of your journey towards landing your dream job.   

Here’s what makes CodeOp’s data science bootcamp different:

  • You’ll learn the fundamental concepts of Data Science, including  Statistics and Machine Learning and create datasets from Relational Databases using SQL.
  • You’ll get hands-on training using libraries in Python.
  • You’ll be exposed to a hands-on, collaborative, experiential method of learning, with real-life application cases.
  • You’ll learn from a diverse range of experienced experts from both academia and the data science field
  • You’ll have the choice to study online, as well as at our fantastic campuses in Barcelona and Malaysia
  • Our role in your learning journey extends beyond the classroom, with tailored career advice and support to help you showcase your new skills and land your dream data science job!    

Why not download our Data Science Course Guide and begin your data science journey with CodeOp!