Why is Python Used for Data Science and Machine Learning?
In the last few years, Data Science has been a domain that has found many takers, mainly because of how interested learners can begin learning it at multiple junctures. However, this flexibility has just made it one of the most popular subject areas in recent times.
If we talk about Data Science as a skill, there’s a lot of promise in it as a career prospect, providing a lot of security, too, with the kind of demand it has created. In terms of market size, the global Big Data and Analytics market is worth a whopping $274 million, and it’s only expected to grow.
And it’s the popularity of Data Science that has been pivotal in bringing Python for Data Science to the fore for developers all over the world.
A Brief Overview of Python Language
Python is an object-oriented, interpreted, high-level programming language with a versatile set of semantics. It is built predominantly in data structures, making it an absolute gem for a software development company to ensure rapid app development, as well as using it as a scripting language to connect components.
Thanks to its super-simple syntax, Python is a highly readable language, and this ease makes it very easy to maintain. In addition, the modular nature of this language allows the code to be re-used by developers, further highlighting its flexibility as a programming language.
Read more: The Top Python Development Skills for Developers in 2022
Why is Python a Great Option for Data Science and Machine Learning?
Data Science experts have increasingly taken to this programming language for Python application development. And this preference is backed by multiple reasons, making it easy for developer teams at any enterprise software development company to go ahead with developing great apps and products for their clients.
Let’s have a look.
- Python is Simple and Easy to Learn:
- Because of its intuitive nature, Python is a very simple programming language to learn, even for beginners. Given how intimidating coding can be, Python is a welcome exception due to its easy syntax and overall nomenclature.
- It’s no surprise that learners can pick Python more quickly than other programming languages like C++, C, and Java. This ease makes Python for Data Science the perfect combination for this domain in so many ways. As an aspiring data scientist, Python is just about the language option you need.
2. Access to Multiple Tools and Libraries:
- Data Science and Machine Learning are all about churning vast amounts of data, drawing measurable inferences around them, and taking the right kind of actions based on the collected information. However, the data collected initially is raw, and it isn’t easy to comprehend any analytics with it.
- Cleaning and organizing this data set to model it into anything actionable is a very complex task. This is where Python for Machine Learning is just the perfect option. The language has a lot of open-source libraries to help developers complete all those complex tasks with relative ease.
- All these libraries get regular updates so scientists can use them in their Python scripts. Pandas, NumPy, TensorFlow, and more are a few such libraries.
3. Extended Community Support:
- Community support is another big factor when we talk about the popularity of Python as an object-oriented programming language. As the language has been around for more than three decades, Python has found a lot of Data Science experts, which has now become a strong community that developers can visit whenever they get stuck with issues.
- Its relevance with people and companies has helped the ones associated with the language to always be eager to share their tips to help the interested ones write better code, correct their errors, and more.
4. The Virtue of Scalability:
- Another positive point of Python as a programming language is that it is very easy to scale, opening doors to more development opportunities. In case of any issues, the same can always be cleared out with the next set of updates. With multiple ways to address a single issue, Python offers a lot on everyone’s plate.
- Additionally, Python is more than adequate for developers with basic programming languages like C++.
5. Seamless Web Development:
- Finding a Python development company is easy, but finding one good at their job is tough. As a company, Python can take a lot of effort away from your team. That’s because Python makes the web development process simpler and more sorted than ever before.
- Some multiple frameworks and libraries are dedicated to Django and Flask that can make your coding highly productive with minimal errors and provide a lot of speed to the overall development process. For instance, comparing the development speed with PHP, the development that takes place in a few hours gets done in just a few minutes.
6. Automation in its Truest Sense:
- Automation is at the crux of any enterprise software development company today, and Python allows teams to embrace automation with its automation frameworks like PYunit. The benefits of automation with Python include:
- There’s no need for the installation of any additional modules. They are part of the initial bundle.
- With the right naming titles for terminals, it becomes easier to conduct singular experiments in a more unified and sophisticated manner.
- The reports of experimentation tests get generated within a few milliseconds.
Read more: Python AI: Why Python is Better for Machine Learning and AI
Conclusion
When it comes to domains like Machine Learning and Data Science, it is not an understatement that there will always be a demand for dedicated Python developers simply because of how much the programming language offers.
Taking a leaf out of this trend, BoTree Technologies has a team of Data Science experts and Python developers to take your Python application development to the next level. As an aspirant, too, if you want to get into development, then Python is a good stepping stone.
Contact us today for a FREE CONSULTATION.
Originally published at https://www.tech-exclusive.com on December 27, 2022.
