This makes it the top programming language used by data scientists and analysts.
Today, companies are using data science as a way to get better insights about their customers as well as internal operations. But to build solutions that help in processing large amounts of data and reach such insights, a powerful tool is needed.
What Programming Language is #1 in Data Science?
As the statistics suggest, the Python programming language is the best for data science. Data is already complex, but Python is simple and easy to implement.
Did you know that the syntax of Python is similar to that of the English language? It is so beginner-friendly, developers can join at any stage in the project and start building.
Apart from the multiple data science packages, Python developers only need to know the three basics to start working with data — Conditionals, Loops, and Functions.
Python for data science has tremendous potential due to its simplicity and power. Nearly 300,000 websites use the Python ecosystem in 2021.
Why is Python Popular among Data Scientists?
Data scientists need a simple programming language that has powerful coding capabilities, data science packages, and a community that supports their work.
Python has all three.
On the other hand, it is an emerging technology that receives regular updates and aligns perfectly well with any Machine learning algorithm.
While Python is one of the top tools for data science, it is preferable over R because of the amazing libraries. Developers don’t have to work from scratch for building applications.
Python can perform basic operations like data cleaning, data munging, and pre-processing of data with ease. Once this is done, the task becomes easier for the data scientist.
For advanced operations like data analysis and identifying gaps, reaching valuable conclusions, visualizing data, and making data-based decisions are carried smoothly by the Python programming language.
Why is Python the Best Language for Data Science?
There are over 70,000 Python libraries that enable developers and data scientists to build high-performance solutions.
Along with that, the Python community is ever-expanding. As a general-purpose programming language, Python allows you to create CSV output for reading data without any hassle in a spreadsheet.
On top of that, the object-oriented and functional style of Python makes it powerful enough to process both structured and unstructured data.
Here are the top reasons that make Python the best language for data science -
Scalable Data Solutions
- Python is so powerful that even a hundred people could work on a single project, use Python, and still face no problems.
- As traffic increases, Python for data science can help to process huge amounts of collected data and reach consistent insights.
- The in-built flexibility enables a Python programmer to solve any kind of data-based problem. Python also works well with Hadoop, the popular big data platform, and it can work on any operating system.
- Developers can process tons of data and scale their solutions to maximize the insight derivation potential.
Read more: Why use Python in Healthcare Application
Data Science Packages
- Python’s powerful data science libraries are next to none. They are useful for data analytics and Machine Learning solutions.
- Some of the best Python libraries for data science include NumPy, SciPy, Scikit Learn, and Pandas, among several others.
- These libraries are highly functional, powerful, and useful for scientific computing, numerical computations, statistical analysis, probability, data plotting & visualization, and more.
- Data scientists often require functions for linear algebra, handling data structures, interpolation, signal processing, and more. Python has libraries that can easily execute all these functions.
Machine Learning Algorithms
- Python is the most popular among data scientists who work in Machine Learning. And the demand for deep learning solutions has only contributed to its growth.
- TensorFlow, Cafe, and Torch are the three highly used software libraries in Python used for writing a Machine Learning algorithm.
- Python offers complete execution with probabilities, statistics, and optimizations to help data scientists build Machine Learning models for both structured and unstructured data sets.
- On top of that, Python is a powerful programming language for working with the three most used types of Machine Learning algorithms — classification, regression, and clustering.
Data Visualization Capabilities
- To reach the best conclusions and derive valuable insights, it is important for data scientists to visualize data in a way that provides maximum analysis.
- Matplotlib is probably the most famous library amongst all the libraries for data visualization. It offers strong visualization options like histograms, scatterplots, and more.
- TabPy is an emerging library that works well with Tableau, enabling Python Machine Learning developers to build solutions in advanced analytics.
- Python is slowly going beyond the “R” programming language when it comes to data visualization capabilities.
Simplicity and Clarity
- And finally — what everyone knows about Python is that it is simple and clear. The syntax is so easy that a beginner can learn and start working on Python projects in no time.
- Many companies are using data science capabilities along with Python to augment their operations and get better results — all because of simple Python.
- Python can help build Machine Learning solutions with the fewest lines of code. There’s no complexity in developing apps from scratch.
- The Python community will always deliver and find solutions to complex problems with the simplest means possible. It’s a win-win for all.
Conclusion: Python for Data Science is Great
Every Python development company would agree that it is the best programming language for data science. There’s so much that developers, data scientists, and analysts can do with Python.
It is an incomparable programming language when it comes to working with data. Whether it’s Python’s powerful data science libraries or powerful code that scales easily — it has everything that developers need for data science based software development solutions.
Originally published at https://www.codementor.io.