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Python is the preferred programming language for Data Scientists. They need easy-to-use language, with decent library availability and a large community.

Projects with inactive communities are generally less likely to update their platforms.

So why is Python popular in Data Science?

Why is python the best choice?

Python has long been known as an easy-to-learn programming language, syntactically speaking. Python also has an active community and a huge selection of libraries and resources.

The result? You have a programming platform that makes sense to use with emerging technologies such as machine learning and data science.

Professionals working with data science applications don’t want to get bogged down with complicated programming requirements. They want to use programming languages like Python to perform hassle-free tasks.

Python also allows developers to deploy programs and run prototypes, significantly speeding up the development process.

This is why Python is so popular that 48% of Data Scientists have rated Python as their preferred programming language.

Why are data science and python closely related?

Data science is about extrapolating useful information from vast stocks of statistics, records, and data. These data are generally unsorted and difficult to correlate with significant precision. Machine learning can make connections between disparate data sets but requires computer sophistication and power.

Python meets this need by being a versatile programming language. It allows you to create CSV output for easy reading of data in spreadsheets. You can also use more complex file outputs that can be ingested by machine learning clusters for computational purposes.

Example:

The weather forecast is based on past readings from weather records dating back a century. Machine learning can help create more accurate predictive models based on past weather events.

Python can do this because of its lightweight and efficient at running code, but it is also multifunctional. Additionally, Python can support object-oriented, structured, and functional programming styles, which means it can find an application anywhere.

One of the main reasons why Python is popular is the number of available libraries that approach 70,000 libraries. As mentioned earlier, Python offers many libraries geared toward data science. A simple Google search reveals many bookstores in the Top 10 Data Science Packages.

The most popular data analysis library is an open-source library called Pandas. It is a high-performance set of applications that greatly simplifies data analysis in Python.

Python has the tools to perform a variety of powerful functions. It’s no wonder that computer scientists have embraced Python.

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Try an interactive course

Interactive learning is the new trend, and it is seen by many as the best way to learn Python. Personally, I greatly appreciate the unique features that interactive courses bring to online learning. Among these characteristics:

Interactive programming lessons let you write code directly in your browser, following clearly defined instructions.
When you take an interactive course, you receive constant feedback on your code and its writing quality.
Interactive lessons usually start simple, but move quickly to more advanced concepts, while developing your knowledge logically and rationally.

I’m sure you can see why interactive online Python courses are one of my favorite ways to learn to program in Python.

If you would like to learn the basics of Python through an interactive course, check out the LearnPython.org Interactive Python tutorials.

This Python training teaches the basic concepts and basics of the Python language that you need to get started. It also covers more advanced elements such as functions and loops, while allowing you to practice what you learn in your browser.

Final thoughts

Python is still in development, which means it receives regular updates and releases. So you can rest assured that learning Python for data science is a good use of your time.

As big data and machine learning become mainstream in businesses and governments, the demand for more skilled Python practitioners will increase and it’s a good skill to have in your arsenal.

First appeared here