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How much coding is needed in a data science career?

  • Aileen Scott 
How much coding is needed in a data science career?

The most common question in people’s minds that are not from a technical background is how much coding is required to ace a data science career path. If you also have the same question, you are not alone. But, the surprising answer is “it depends”. Unarguably, coding is a crucial aspect and vital tool for data scientists. But, depending on the data science professional role or the company, coding may not require. In contrast to popular belief, coding is not a prerequisite for data science.

If you are curious whether a data scientist needs coding skills, continue reading this blog until the end. This blog will explore the answer to this question and discover its significance.

Does data science require coding?

Data science roles traditionally require programming skills. Most experienced data scientists still code today. Data scientists are still growing, but the data landscape is changing. Now, technologies enable people to work on data projects without typing code.

These technologies are not intended to replace data science skills in coding but rather to make data analysis more accessible to those with less technical skills. When used in the planned way, these technologies allow data scientists to continue using code for complex and bespoke solutions.

What are the programming languages used in data science?

It is crucial to have a basic understanding of each data science language. So, take a closer look at each language:

1. Python

Python is a highly used programming language in data science. Scientists do not use Python for scientific research. Python is a powerful tool for machine learning, data analytics, and data visualization. Also, it is widely used in other software engineering applications. It can be almost as easy to learn Python as reading and writing in English. Python’s open-source nature and this characteristic make it a popular coding for data scientists. Data scientists and other tech professionals use it extensively.

Data scientists are often in need of saving time through automation. Python language is a fantastic tool for automating tasks.

2. R

R is a scripting language that is:

· Open-source

· Widely supported

R can be an excellent tool for data scientists managing large, complex data sets. R is the best language for data scientists who combine statistical computing with mathematics and graphics. This language provides its programmers with extensive packages, libraries, and other tools suitable for quantitative applications.

3. SQL

According to Zdnet.com, SQL is second in importance for data scientists after Python. Being familiar with this language is essential because the industry uses it for interfacing with relational database systems. Data science professionals need to be able to query databases. For aspiring data scientists, it’s essential to have a basic understanding of SQL. Data scientists are often required to use this language when dealing with structured data. Data scientists can write SQL queries or scripts to automate tasks like:

· aggregating data

· Calculating averages

· Find the maximum and minimum value in a dataset

SQL can also be used to store data in databases or extract data from databases.

4. Java

Data scientists may choose to use Java to perform tasks related to:

· Machine learning

· Data analysis

· Data mining

This is a good choice for cases when these applications are to be integrated into larger development projects. Java offers many libraries for data mining, machine learning and other applications. Scala is an extension of the Java programming language. It increases the ability of data scientists to manipulate large datasets. Scala offers an extensive set of valuable and well-supported libraries.

5. C/C++

C/C++ is codebasing for many of today’s programming languages. This means that a data scientist should have a solid foundation in C. C/C++ also offers advantages such as the ability to compile data quickly and efficiently. C/C++ is a good choice for data scientists on projects needing high performance or massive scalability.

Benefits of coding in data science

Data scientists should have a solid understanding of coding, an essential part of the data science process. Data science programming requires in-depth knowledge of computer science, mathematics and statistics concepts and techniques. Data science coding enables you to build robust algorithms to automate and solve complex problems.

So, the main benefits of learning to code for the data scientist role are:

· Data science coding allows faster and more accurate analysis of large datasets than manual methods.

· Data science allows you to automate repetitive tasks and free up time for other aspects of your job.

· Data science coding helps you to obtain better insight into data and offers more access to the manipulation and analysis of data.

This guide will provide data scientists some time-saving tips, tricks, and hacks : An Ultimate Guide on 21 Powerful Tips, Tricks, And Hacks for Data Scientists 


You must have the right attitude and skills to be a successful data scientist. Data scientists must be experts in data-science programming, have a strong understanding of maths and statistics, and maintain a consistent workflow.

They must also be able to visualize their data to make their insights easier to understand. Coding is only one aspect of becoming a data scientist. These challenges may seem overwhelming, but with the right help and resources, anyone can become a successful Data Scientist.