Difference Between Data Science Coding and Machine Learning

by Madhurjya Chowdhury

October 27, 2021

Any computer-related job requires the use of coding. Machine learning and artificial intelligence are two aspects of computing, and anyone working with them must know how to program. If you’re just a regular user, you generally won’t need any programming skills.

If all you want to do is use other people’s libraries, you don’t need to be a serious coder. You only need semantic and syntactic understanding in this scenario, which is more than enough.

Data science coding

Low level and high level coding languages ​​are the two categories of coding languages. Low-level languages ​​are the most intelligible and least complex languages ​​used by computers to perform various functions.

A machine language is basically binary read and executed by a computer, while assembly language tackles direct hardware control and performance issues. Assembly language is converted into machine code using assembly software. Compared to their high-level counterparts, low-level coding languages ​​are faster and use less memory.

The second category of programming languages ​​more effectively encapsulates programming details and ideas. These high level languages ​​can generate code that is not affected by the type of computer. Plus, they’re portable, more human-looking, and extremely valuable for troubleshooting instructions.

However, many data scientists choose to use high-level coding languages ​​to process their data. Those interested in getting into the subject might consider focusing on a data science language as a starting point.

Coding in machine learning

Machine learning is applied through coding, and coders who know how to write this code will have a better understanding of how algorithms work and can monitor and improve them more effectively.

C++, Java and Python are the most commonly mentioned programming languages, although they can be much more detailed. When it comes to machine learning, languages ​​such as Lisp, R Programming and Prolog are becoming essential.

That said, prior knowledge of other languages ​​such as HTML and JavaScript is not required. Instead, start with more applicable languages ​​like Python, which are considered relatively easy to learn due to features like using English terms instead of punctuation.

Some machine learning engineers advise anyone interested in the field to start with these fundamental ideas rather than coding. Understanding the basic ideas that make artificial intelligence work is crucial.

Best Programming Languages


Python is currently the most widely used data science coding language on the planet. This flexible and versatile language is naturally object oriented. It also supports a variety of programming paradigms including functional, structured, and procedural programming.

It is also one of the most used languages ​​in data science. It is a faster and superior alternative for data transformations with less than 1000 iterations. With Python’s modules, natural processing and data exploration becomes child’s play. Python also generates a CSV file, making it easier for coders to read data from a spreadsheet.


Hundreds of Java libraries now exist, solving any problem a programmer may encounter. When it comes to generating dashboards and displaying data, a few languages ​​stand out.

This flexible language can handle many jobs at the same time. Everything from electronics to desktop and online programs can be integrated into it. Java is used by popular processing systems like Hadoop. It is also one of those data science languages ​​that can be scaled up quickly and easily for massive applications.


This attractive and sophisticated programming language was born only a few years ago, in 2003. Scala was created in order to solve problems with Java. It has a wide range of applications, from web development to machine learning. It is also a scalable and efficient language for processing large amounts of data. Scala enables object-oriented and functional programming, as well as concurrent and synchronized processing, in today’s enterprises.


R is a statistical computer language developed by statisticians for statisticians. Open source language and tools are frequently used for statistical calculation and visualization. It has many data science applications, however, and R includes a number of useful data science libraries. R can be used to explore collections of data and perform ad hoc analysis. Loops, on the other hand, contain more than 1000 iterations, which makes them more difficult to master than Python.


SQL, or Structured Query Language, has become a leading computer language for data management over the years. Although SQL tables and queries are not primarily used for data science activities, they can help data scientists when interacting with database systems. For storing, manipulating, and retrieving databases from relational database management systems, this domain-specific language is particularly useful.


Julia is a data science coding language designed specifically for high performance numerical methods and computational research. He has the ability to quickly apply mathematical principles such as linear algebra. It’s also a fantastic language for working with matrices. Julia’s API can be integrated into applications that can be used for various back-end and front-end development.


In the current era, there are more than 250 programming languages. Python emerges as an undisputed leader in this huge sector, with more than 70,000 libraries and approximately 8.2 million users worldwide. Python supports TensorFlow, SQL, and a variety of additional data science and machine learning frameworks. A rudimentary knowledge of Python can also help you learn about computing frameworks like Apache Spark, which is known for its data engineering and huge data analysis applications.

Learning a computer language is a prerequisite for becoming an expert in data science. Before making a selection, data scientists should weigh the pros and cons of several types of computer languages ​​for data discovery.

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