The emerging field of Data Science is wildly calling and is not easy to give up on, just like that! If you are a non-coder, a beginner, or someone from a non-CS/ non-IT background, you probably might think- after all, how much coding is required for data science?

Well, the absolute answer to “Is coding required for Data Science” is YES! While a very high level of coding like the software developers is not essential for Data Science, however, if you ask me- “How much coding is needed for data science?” the answer will deviate from one job role to another.

Well, the golden thumb rule is that coding is not required to get started in Data Science. However, you can learn it in the process of mastering Data Science for specific tasks alone. But coding is always a plus!

Btw, Why is coding required in Data Science?

Data Science requires coding when it comes to

DATA

Basically one needs to have good programming knowledge when it comes to the following implementations:

1. Machine Learning Libraries
2. Data Transformation
3. Statistical Packages
4. Greater control over data
5. Version Control

Machine Learning Libraries

Machine Learning techniques go hand-in-hand with Data Science. And when talking about machine learning, it does involve a lot of coding.

Find out: Top 11 Python Libraries For Machine Learning in 2021

Without at least some knowledge of coding languages, working with machine learning libraries or even replacing them would be too hard! Therefore, we have to get our hands dirty with coding to integrate machine learning libraries in data science.

Data Transformation

Transforming data from one form to another, converting data types, removing duplicate data, or even enriching data to enhance its usability are all Data transformation techniques that can be well-managed with better coding skills. Data transformation tools and packages like Pandas and Tidyverse assist in cleaning the raw data and delivering clear insights.

Statistical Packages

Statistical Analysis and calculations help Data Scientists with more accurate results. Tools ease these Data Science calculations and produce better results in less time. However, these tools incorporate at least a basic amount of coding. So, that is what makes coding know-how a requisite skill for Data Scientists.

Greater control over data

Coding languages are versatile. It gives more flexibility and ease in reading and managing data. It improves your control over data and making sense of the available data becomes simpler. Specific conditions and the logic applied with coding, increase the readability.

Moreover, you can go ahead with script automation and save time while augmenting interest.

Version Control

The importance of version control cannot be ignored when it comes to big projects. Version control records the changes and allows better control over the multiple programmer modifications made to the project. This process demands your coding proficiency! Another reason that makes coding requisite to be a Data Scientist.

Find out: Is Data Science A Good Career Choice In 2024?

Before we move into the next section, ensure you have a good grip on data science essentials like Python, MongoDB, Pandas, NumPy, Tableau & PowerBI Data Methods. If you are looking for a detailed course on Data Science, you can join GUVI’s Data Science Career Program with placement assistance. You’ll also learn about the trending tools and technologies and work on some real-time projects. 

Instead, if you would like to explore Python through a Self-paced course, try GUVI’s Self-Paced Data Engineering and Big Data certification course.

So, what programming languages are used in Data Science?

All that said, which are the programming languages that you should be focusing on?

• Python
• R
• Structured Query Language
• JavaScript
• GIT

Let’s dig deep and understand, why and how these programming languages become vital while you accelerate your career in Data Science.

Do you want to go through a detailed Data Science Syllabus: What Is The Syllabus For Data Science? [2021 Course Syllabus]

Python

Python’s ease of use and simple syntax make this popularly used programming language a warm and easy-to-learn coding base for even beginners. Python has a powerful collection of libraries and is most widely used in machine learning, data analysis, and data visualization.

Find out: Best Python Libraries for Data Science Career in 2024

Some of the use cases of Python:

• Getting data from an API
• Creating a dataset
• Querying non-relational data from MongoDB
• Performing data analysis
• Executing data visualization

R

R is an ideal tool when it comes to data wrangling. And, it allows the usage of several preprocessed packages making data wrangling a lot easier. Most importantly, R presents a wide variety of statistics-related libraries. So, it delivers a favorable environment for statistical computing and design.

Above all, R is a programming language created by statisticians for statistics, specifically for working with data. 

Do you want to learn more about Data Visualization: Data Visualization: Get Ready For Your Next 6’er Analysis!

Some use cases of R in Data Science:

• Analytics: to identify patterns and build practical models.
• Great support & libraries
• Visualizing Data
• Transforming Data
• Machine learning

Structured Query Language

SQL is one of the most powerful programming languages used for extracting and communicating various data types from databases. Relational Database Management or SQL is an integral part of Data Science. While multiple industries are now driving their product management with NoSQL, SQL still remains a perfect choice for numerous CRMs, business intelligence tools and in-office operations.

What is the importance of SQL in Data Science? What do we use SQL for?

• For data wrangling and preparation
• To handle structured data
• Most importantly, for querying databases
•  To carry out data analytics with the data that is stored in relational databases like Oracle, Microsoft SQL, MySQL

Read more about SQL: MongoDB vs. MySQL: Which is the best to learn?

If you wish to test your SQL learning, try this: Top SQL Interview Questions One Needs On Their Fingertips!!!

JavaScript is undoubtedly the most popular programming language which has grown in scope by leaps and bounds. It is rightly known as the web’s #1 language. But does it have tools and functionalities to do Data Science too?

Definitely! Tools and libraries like TensorFlow, D3.js, etc., are changing the landscape for JavaScript and making it all the more popular in the world of Data Science, as well.

Is Javascript a good programming language for Data Science? Read here: Data Science Syllabus

And, in Data Science, what is JavaScript used for?

• Creating Machine Learning models
• Creating data visualization and diagrams

GIT

Git is a free and open-source distributed version control system, for tracking changes in any set of files. That said, Git is the most important tool for all software developers. And owing to its ability to track every change, Git is becoming an indispensable part of data scientists’ careers too. Data Scientists and analysts can employ its Version Control System (VCS). With the help of Git, they can work on the same data project together.

What is Git used for in data science?

• To track code and file changes
• Version control

Learn more about Git: How to Upload a Folder on GitHub? | Beginner’s Guide

So, is coding required for Data Science?

Do you aspire to become a Data Scientist? Are you worried that coding might come your way? Then, no worries! You can step into our comprehensive Data Science Program with zero coding knowledge and kickstart a propelling Data Science career from there.

Kickstart your Data Science journey by enrolling in GUVI’s Data Science Career Program where you will master technologies like MongoDB, Tableau, PowerBi, Pandas, etc., and build interesting real-life projects.

Alternatively, if you would like to explore Data Engineering and Big Data through a Self-paced course, try GUVI’s Self-Paced Data Engineering and Big Data certification course.