Programming Languages for Specific Data Roles
What programming language do you need for a specific data role?
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When you’re interested in getting into the world of data, it can be difficult to know which programming language you need to cater for your specific interest or skill. Many people waste a lot of time becoming proficient in a specific programming language because they have heard it's very popular or they don’t have enough knowledge.
A lot of data science roles are being used and sometimes advertised interchangeably. You might see some people referring to a Data Analyst and a Data Scientist as having the same role, or a Data Scientist and a Machine Learning Engineer.
Again, this may be due to the recruiter/employee having a lack of knowledge on the distinction between the different roles, to catch interest or be able to hire someone who can kill two birds with one stone.
This blog aims to give you a quick and simple understanding of what programming languages are required or essential for specific data roles.
Popular Data Roles
Let’s start by defining the popular data roles.
Data Analyst - look through data and provide reports and visualisations which explain the data.
Data Scientist - collects, cleans, analyses data, provides reports, visualisations and manipulates data to perform advanced data analysis.
Data Engineer - responsible for setting up and maintaining the organization's data infrastructure, whilst ensuring that the data can undergo critical analysis and can perform and produce reports.
Machine Learning Engineer - responsible for building AI systems that consume large amounts of data and be able to generate and develop algorithms capable of learning and making future predictions.
Research Scientist - in relation to data, they are responsible for researching, designing, and analysing information from investigations, experiments and trials.
Top programming languages
If you were to Google, what are the top programming languages - you will see a mixture of these, and probably a few more:
- C and C++
So after seeing this online, you’re probably thinking - where do I go from here? Which one do I actually need for the role I’m interested in?
Top Languages for Specific Data Roles
As a Data Analyst, you will be responsible for scanning through the data, finding valuable information and providing reports or visualisations. With this being said, the best programming languages for a Data Analyst would be Python and/or SQL.
- Python - will allow you to analyse, manipulate, clean, and visualise data.
- SQL - will allow you to communicate with the databases easily.
As a Data Scientist, you have a choice between various programming languages. The most popular languages used by Data Scientists are Python, and SQL, with R, C++ and Java after.
R, C++, and Java are still popular, however, Python and SQL are very popular due to their simpler coding abilities whilst producing the same results.
- Python has a larger developer community, with extensive libraries, very concise syntax, and portability. This is everything a Data Scientist wants and needs.
- SQL has the ability to store, retrieve, manage and manipulate data, as well as extract performance metrics to guide Data Scientists in their processes.
As a Data Engineer, the most popular programming languages are:
- Java - It is the oldest and most appropriate language for a Data Engineer. Data Engineers spend a lot of time working with the java-based open-source framework, Hadoop.
- Python - helps Data Engineers build efficient data pipelines, write ETL scripts, set up statistical models, and perform analysis.
- SQL - allows them to model data, extract performance metrics, and develop reusable data structures.
Machine Learning Engineer
As a Machine Learning Engineer, the most popular programming languages are:
- Python - good library ecosystem, better readability, flexibility, creates good visualisations, community support, etc. Simple syntax and construction are highly favourable in a Machine Learning Engineer's life.
- C++ - this is also a valuable programming language for machine learning engineers as it is fast and reliable, which is necessary for machine learning, as well as having a good library source.
- Java - if you want to work in web development, big data, cloud development, and app development, Java is imperative to your skillset. It also has a better performance than Python.
As a research scientist, you will not be dealing with backend issues, but more on understanding what the data and the team's findings can tell you. Similar to Data Analyst, the programming languages that will benefit you are:
- Python is a general-purpose programming language, that allows you to write fewer lines of code but perform the same operations
- R is a statistical programming language, allowing you to build statistical models and create data visualisations
To make it easy and simple, I have created the above image to give you a visual understanding of what you should be looking out for dependent on your area of interest.
Referring to the image above, it shows you what kind of programming language you need for a specific data role and to which extent. The bigger the circle, the more essential it is to that specific data role.
As data roles are forever developing, it can be overwhelming to keep up with all the changes. Learn a programming language at a proficient level before you move on to the next or learn a new skill. It’s better to take one step at a time than be overwhelmed with trying to learn 10 skills at a time.
Once you have decided on your programming language based on your area of interest, the next step is to become proficient in it.
There are readily available resources to help with your study, you just need to know the right ones. Below are a variety of links that you can benefit from
- Top Data Analyst Certification Courses for 2022
- The Complete Data Science Study Roadmap
- The Complete Machine Learning Study Roadmap
- The Complete Data Engineering Study Roadmap
Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.