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If you’re interested in the world of data, it can be difficult to know which programming language you need to meet your particular interests and skills. Many people either hear that a particular programming language is very popular, or because they don’t know enough about it, they waste a lot of time mastering it.
Many data science roles are used and sometimes promoted interchangeably. Some might call data analyst and data scientist the same role, or data scientist and machine learning engineer.
Again, this can be due to a lack of knowledge on the part of recruiters and employees about distinguishing between different roles to attract interest or to hire two birds with one stone. There is a nature.
This blog is intended to help you quickly and easily understand which programming languages are necessary or essential for a particular data role.
Let’s start by defining common data roles.
data analyst – Explore your data and provide reports and visualizations that explain your data.
data scientist – Data collection, cleaning, analysis, reporting, visualization, and manipulation of data to perform advanced data analysis.
data engineer – Responsible for setting up and maintaining the organization’s data infrastructure while ensuring that data undergoes critical analysis and reports can be run and generated.
machine learning engineer – Responsible for building AI systems that can consume large amounts of data and generate and develop algorithms that can learn and make predictions for the future.
Researcher – In terms of data, we are responsible for researching, designing and analyzing information from surveys, experiments and clinical trials.
If you look up the top programming languages on Google, you’ll see a mix of these languages, and possibly a few more.
- JavaScript
- python
- go
- Java
- Kotlin
- PHP
- C#
- Quick
- R.
- Ruby
- C and C++
- mat lab
- SQL
After looking at this article online, you’re probably thinking – where do I go from here? Which do I actually need for the role I’m interested in?
data analyst
As a data analyst, you are responsible for scanning data, finding valuable information, and providing reports and visualizations. That said, the programming languages of choice for data analysts are Python and SQL.
- Python – Allows you to analyze, manipulate, cleanup, and visualize data.
- SQL – Allows you to easily communicate with your database.
data scientist
Data scientists can choose from a variety of programming languages. The most common languages used by data scientists are Python and SQL, followed by R, C++, and Java.
R, C++ and Java are still popular, but Python and SQL are very popular due to their easier coding ability while producing the same results.
- Python has a large developer community, rich libraries, very clean syntax, and portability. This is everything a data scientist wants and needs.
- SQL provides the ability to store, retrieve, manage, and manipulate data, as well as extract performance metrics to guide the data scientist’s process.
data engineer
The most popular programming languages for data engineers are:
- Java – Java is the oldest and best suited language for data engineers. Data engineers spend a lot of time working with Hadoop, a Java-based open source framework.
- Python – Helps data engineers build efficient data pipelines, write ETL scripts, set up statistical models, and perform analytics.
- SQL – Enables you to model data, extract performance metrics, and develop reusable data structures.
machine learning engineer
For machine learning engineers, the most popular programming languages are:
- Python – Great library ecosystem, readability, flexibility, creating great visualizations, community support, and more. Simple syntax and structure are very advantageous in the life of machine learning engineers.
- C++ – This is also a valuable programming language for machine learning engineers as it is fast and reliable for machine learning and has excellent library sources.
- Java – If you want to work in web development, big data, cloud development or app development, Java is essential for your skill set. It also has better performance than Python.
Researcher
Researchers focus on understanding what the data and team findings tell us, rather than dealing with back-end issues. As with data analysts, useful programming languages are:
- Python is a general-purpose programming language that allows you to perform the same operations with fewer lines of code.
- R is a statistical programming language that allows you to build statistical models and create data visualizations.
To make it easier and simpler, I created the image above to give you a visual idea of what to look out for depending on your area of interest.
Looking at the image above, you can see what kind of programming language is required for a particular data role, and to what extent. The larger the circle, the more important it is for that particular data role.
According to Stack Overflow’s 2022 Developer Survey, JavaScript is the most used programming language and has been for a decade. But when it comes to programming languages used to learn how to code, HTML/CSS, JavaScript, and Python are at the top, and they’re all pretty close to a tie.
The role of data is constantly evolving and keeping up with all the changes can be a daunting task. Learn a programming language at a proficient level before moving on to the next language or learning a new skill. It’s better to take one step at a time than to be overwhelmed trying to master ten skills at once.
Once you’ve decided on a programming language based on your area of interest, the next step is to become proficient in that language.
With resources readily available to help you learn, all you need to know is what’s right for you. There are various links below, so please take advantage of them.
Nisha Aria Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing data advice, tutorials, and theory-based knowledge on data science for her science career. She also wants to explore whether artificial intelligence can contribute to the length of human lifespan in different ways. She is an avid learner and seeks to broaden her knowledge of technology and writing skills while helping mentor others.
