Why Do Data Scientists Embrace Python?

For Data scientists or developers, Python is a popularly used programming language by both of them. But what are the prime reasons that make Python development services so demandable among developers? This blog will explore those causes in detail. Have a look.

Table of Content

Why Python is Used in Data Science?

 The Future of Python in Data Science

Conclusion

Why Python is Used in Data Science?

Data science is an engrossing field with varied entry points. A language named R got popular at the initial phase of data science evolution. But if a comparison is done, Python will win the race over R. Dipping your toe in the dynamic data science landscape using this programming language is beneficial for various reasons. Here are some worth mentioning them you will love to explore.

Simplicity- The most significant benefit of using Python in data science is its simplicity. Python has an easy syntax that is convenient to learn and write. Reading Python is very easy as the clean syntax of this language matches English. And when it is about learning, simple syntax and vocabulary help. So, entering into the data science sector gets easier for developers when they use Python. People with minimal tech knowledge also can learn it quickly.

Rapidly Increasing Demand- The demand for this programming language is thriving. As per the survey done by Stack OverFlow on 2020 , 65,000 developers prefer Python. And according to Tiobe’s 2020 index, Python achieved the position of the third most popular language in 2022. Google, Facebook, Instagram, and NetFlix also use Python rapidly for their data science ventures.

A Thriving Community– Flourishing community of Python also makes it a good language to use in the data science sector. Python developers available all over the world share their knowledge and thoughts and contribute to its growth. So, whatever the complexity of the development is, Python developers can sort them out using it.

Huge Set of Data Libraries- If these points are not sufficient to give you an understanding of Python in data science, take a look at the comprehensive set of Python libraries.

Pandas- It is one of the most highly-used libraries that comes with an easy using option. Quicker manipulation of the tabular data for data cleaning and data science becomes easy using this Python library. Flexibility, promptness to fast nature- all these qualities make it a demandable language among developers.

SciPy– Another highly used Python library by a python development agency also needs a mention. Data science tasks become easier by using this library. This library helps in addressing different data science needs like data structure handling, networks, algorithm analysis, and so on.

NumPy– NumPy provides the required support to different multi-dimensional languages and arrays. It supports different mathematical tasks on huge multi-dimensional metrics and arrays.

Matplotib– It offers simplified data visualization and developers may create bar charts, scatterplates using Matplotib conveniently.

Statsmodels– It is another popular statistical model library the developers use for stat tests. Usage of this popular library includes linear regression, linear models, series analysis models, and so on.

Machine Learning Libraries– Besides these common ones, the availability of different machine learning libraries in Python offer an added benefit for data scientists. Creating robust neural networks is not a big deal using these libraries. Top Machine Learning libraries Python developers use are:

TensorFlow– Written in C++, TensorFlow is a high-level library that provides users with the simplicity of Python without compromising performance. Beginners should try something else, leaving it as its complexity might create inconvenience.

Keras– It denotes a high-level API that also functions as an interface of TensorFlow. Developers use it to build an easily usable TensorFlow backend.

Scikit Learn– You may call it the ultimate solution to meet all the machine learning needs. This library gives support to all the supervised or unsupervised tasks. Machine learning algorithms used over here are k-nearest neighbors, gradient boosting, principal component analysis, logistic regression, and so on.

Easier Data Cleaning– Data cleaning is one of the crucial tasks data scientists need to handle. Numpy and Pandas, two common Python libraries make data cleaning super easy. This feature of Python makes it a great choice in this sector.

Easy Communication– Communicating your relevant findings is another task for data scientists. And data visualization makes this communication engaging. We have named two libraries already ( matplotlib, and its two children Seaborn and Pandas), that provide an easy visualization. So, you may say, using Python library means enjoying better communication with the proper visualization.

Suitable for Prototype Building– Many data science ventures fail even after using money or energy. To avoid this issue, most developers use prototypes and dry run of ideas, and stress tests to be sure of the project’s success. However, Python is an excellent option for prototype building, testing out concepts, ideas or products.

The Future of Python in Data Science

As Python developers and data scientists will grow, Python has a bright future in this sector. Advancements in the Python libraries will happen as machine learning, deep learning or other sectors in the data field sectors will be more popular.

Learning Python can be beneficial for beginners and senior data scientists as well. Readability, vibrant community or easy learning, this programming language has all the features that make it suitable for data scientists. It simplifies the data science overflow and also helps developers to develop something with ease.

Conclusion

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