There are only a handful of Programming Languages that have changed the way we approach programming today. And Python is one of them, which brought in a fun factor and ease of use inside programming to make it more appealing and versatile at the same time for various applications.
Python is not only changing the game of Data Science but several domains, that include Web Development, Machine Learning, A.I., etc. There are a lot of benefits of using Python, which is game-changing even today.
Let’s dive into the topic to find out what Python is faring in Data Science?
A peek at questions we will answer in this article:
- What is Python?
- What is Data Science?
- Which Programming Languages are popular in Data Science and why?
- Why is Python popular in Data Science?
What is Python?
Python, as we know, is an open-source, general-purpose high-level interpreted Object-Oriented Programming (OOP) Language that emphasizes more on code readability, making scripts easy to read and comprehend. A Dutch programmer, Guido van Rossum launched Python in 1989 to make coding more maintainable and easy to understand. With the Internet, the Python community grew at a very rapid pace. Python chartered into several domains like Web Development, Software Development, Data Science, Machine Learning (ML), Gaming, Artificial Intelligence (A.I.), Business Applications, etc.
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What is Data Science?
Data Science is an interdisciplinary domain of science that uses mathematical or scientific algorithms, techniques, and methods, to process aggregated data and extract insights for wide-ranging applications. In layman’s terms, Data Science is the science of exploring data to find meaningful information. Data Science consists of fields like Math, Statistics, Programming, and various other Data Visualization tools, to feature extracted information. And lately, ML is also enhancing the Data Science throughput. Wish to break into the Data Science sector Which Programming Languages are popular in Data Science and why? Check out these industry-grade Data Science Online Courses
Among all the Programming Languages used for Data Science, Python and R stand out. And they both have equally garnered the praise of their users. Let’s discuss some of their differences to understand these tools quickly.
- Python is a general-purpose language, unlike R, which is for statistical analysis and computing.
- Both are open-source tools, with Python releasing in 1989 and R in 1992.
- Python is an OOP, so it will convert everything into objects, thus helping in executing tasks with better code readability, stability, and modularity. Whereas R is a procedural language that breaks down tasks into a series of steps and procedures for easy comprehension. The downside of R is code readability and performance are low compared to Python.
- Since Data Science is just an application of Python, it hosts some popular libraries like Keras, TensorFlow, scikit-learn that Data Scientists use to develop data models efficiently and with better integration. Thanks to a large community of R, it has several open-source packages that help in building various complex data models, avoiding the pain of developing them from scratch.
- Python is versatile in terms of aggregating data from several sources, which R is not capable of.
- In terms of Data Exploration, R leads Python, as it’s built from the ground-up for performing statistical analysis on data sets and offers more options in exploring data. For some heavy tasks, R has to rely on third-party libraries. Whereas Python has Pandas, the data analysis library of Python. You can perform tasks through this library without any complaints about its performance.
- When it comes to Data Modelling, Python has NumPy for numerical analysis, SciPy for scientific computing, and scikit-learn code library for ML. R on the other hand can do Data Modelling with just a few core functionalities, and for building a specific model, you need to rely on outside packages.
Why is Python popular in Data Science?
It is one of the most popular languages across domains, for a variety of reasons, like:
- enhanced code readability
- open-source
- versatile
- can perform complex tasks very easily
- Easy to learn and easy to use
- has a huge community
- Has many packages and libraries to get the job done effectively
As is evident from the comparison between R and Python, Python is ahead of R in many segments and offers much more functionalities which its counterparts cannot offer. It has many popular libraries and packages, which changes the game in Data Science. R is a Programming Language focused on statistical computing and analysis, whereas, Python is a general-purpose language. Earlier, R was the choice of every statistician and Data Scientist, but with the rising popularity of Python lately, many users are shifting from R to Python, citing better opportunities and functionalities.
According to StackOverflow snapshots of trends in Data Science, Python is closing the gap between R and itself at a ferocious pace, thanks to its ever-growing community.
Conclusion:
Python is continuing its growth in the Data Science sector, and if you wish to become part of this domain, learn Python for Data Science to get a better head start.
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