Why Is Python So Important For Data Science And Machine Learning?

By NIIT Editorial

Published on 20/11/2021

8 minutes

Before beginning their machine learning and data science careers, prospective data scientists have one question: ‘What is the most commonly used language by data scientists?’. Python, R, and C++ are just a few of the programming languages used by data scientists. However, Python holds a special place in the future of AI and data analysis.

Open-source, adaptable, and simple to learn, Python is an object-oriented, open-source programming language. With an array of libraries and tools, data scientists can quickly complete their assignments.

Python has an expansive community base where engineers and data scientists can ask queries and answer questions. Data scientists have used Python as a service for a long time, and it will continue to be the top choice for developers and data scientists.

Understanding Python

In the beginning, Python was used to automate repetitive tasks, prototype applications, and use these applications in various languages. Because of the spotless and straightforward code and extensive documentation, it is comparatively easier to learn and understand. A more interesting aspect of Python is that it can be used in non-technical fields, like business and advertising, to help experts analyze data.


Object-oriented programming is easy with Python, an open-source, interpreted, high-level language. Data scientists frequently use this language for a variety of data science projects/applications. Python is well suited to mathematical and statistical functions. It provides several libraries that are well-suited to data science. 

Because Python is easy to use and has a simple syntax, scientific and research communities widely use it because it makes it easy to adapt for people with no engineering background. It makes this particularly useful for rapid prototyping. It is best to learn about them from the best data science courses online and data analyst courses online. 

Here Are Some Reasons Python Is Important

Consulting companies offering data science services encourage their developers and data scientists to use Python as a programming language. Python has quickly gained popularity among developers and data scientists. A prominent data scientist must manage a large amount of data. 


Python has gained popularity for dealing with big data because of its ease of use and enormous python libraries. It is best to learn from the best data science courses online and data analyst courses online. 

Let's look at Python's role in Machine Learning and Data Science in more detail:


The learning curve for Python is short and easy. Python's simple syntax and better comprehension make it an easy language for new data scientists to learn. You can use several data mining tools available in Python to handle the data better. You can use Rapid Miner, Weka, Orange, etc. Data scientists rely on Python since it has many valuable and easy-to-use libraries, like Pandas, Numpy, Scipy, Tensorflow, and many others.

Programming In Python Is Flexible:

Besides creating software, one can use Python to analyze, compute numeric and logical data, and develop websites. Python has primarily become a Web development framework, controlling many prominent websites, including TurboGears, Django, and Tornado in the future of AI. Python is perfect for developers who have an aptitude for developing applications and websites. Most Data Scientists prefer this programming alternative over the next one available on the market.

With Python, You Can Build Better Analytics Tools:

A key component of data science is data analytics. A data analytics tool provides information about several frameworks that can evaluate any business's performance. It is possible to build Data analytics tools with the Python programming language.

Big datasets can be easily correlated, enriched with examples, and better understood with Python. Python is helpful for self-service analytics and data analysis. In addition to this, Python has made it much easier for data mining companies to handle data analysis to their advantage.


Python is a highly fast-scaled programming language in machine learning and data science. It has the highest scaling rate of all available languages. Therefore, Python has a growing number of possibilities. 

With new updates coming, it is easy to resolve any problem quickly. According to popular belief, Python offers the best options for newbies from the myriad available options. Whether you have non-Python programmers who know C+ design patterns, Python will be a better choice for the time spent developing and checking code.

The process is fast since you don't have to look for memory leaks, compilation errors, or segmentation faults.

Wrapping It Up

Python is a highly adaptable language that is very easy to understand and is exceptionally unique as well. A data scientist can accomplish more with Python in less time, thanks to the power of data science in Python. It is best to learn from the best data science courses online and data analyst courses online.

Python works well in a wide range of environments and also fits in perfectly in the future of AI. As a result, it can be used on virtually any operating system and integrated with any programming language with barely any changes. Python has emerged as the top choice for developers and data scientists due to these qualities. You can learn more about using Python as a component of AI through NIIT’s Advanced Post Graduate Program in Data Science & Machine Learning. It is an 18-week online certification that validates learner expertise in the following areas: 

  • Data analysis 
  • Data  visualization 
  • Predictive modeling 
  • Machine learning 
  • Deep learning 
  • Natural language processing

The program is placement-backed and ensures that incumbents receive job offers with Rs. 5LPA minimum CTC threshold. Program graduates can become certified data scientists with the opportunity to work for major employers. 

Apply now.

Advanced PGP in Data Science and Machine Learning (Full-Time)

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