What Is Python for Data Science?

Python for data science refers to the extensive libraries and tools the programming language offers for data and numerical analysis, as well as its capacity for machine learning tools that can improve analytics in general. In recent years Python has become an increasingly popular programming language due to its versatility and multi-purpose design.

As opposed to domain-specific languages and those that are designed for unique purposes, Python provides a range of tools that make it valuable not just as a data science platform, but also as a foundation to build more expansive applications that include data science tools.

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Indeed, by 2013, almost half of all data scientists already used Python daily for Big Data, and its widespread use has also lead to a massive trove of libraries and resources that make it doubly valuable for analytics and even used as a data science suite in any modern BI tool.

Libraries like NumPy, for instance, add significant calculation and numerical analysis tools to the standard Python language, while Scikit -earn and Pandas offer in-depth tools for data modeling and manipulation, machine learning algorithms, and more complex analyses. Taken together, these tools (and thousands of more libraries) present data scientists with a fertile ground to develop tools and analytical models that process massive data sets faster.

How Can I use Python for Data Science?

There are multiple ways you can integrate Python and data science tools in your existing analytics and business intelligence platforms, ranging from small connections to major additions. One of the most common uses of Python for big data (Python Big Data) is to build specific tools that can answer questions within a broader infrastructure. For instance, you can build a Python application that can take data from your existing data warehouse and perform a specific analysis on each new batch that flows in.  

This allows you to cut down on time spent parsing and offers a ready-made solution that can plug into any BI platform you’re using, without having to switch. On the other hand, you could theoretically use Python to build out a complete analytics suite that includes other functionalities.

Because Python is designed to be completely multi-purpose, it can include functions that go beyond statistical analysis, including visualizations, machine learning, and data storage without having to branch out into a variety of other languages.

In general, Python is a great addition to any data science tool due to a large number of libraries and resources that have been built specifically for statistical analysis, numerical calculations, and Big Data. It can function both as an addition or a base language for creating new data science tools.

While it’s become a highly popular language, it is not a requirement to use Python if you’re building data science tools. However, it’s a great addition and can deliver great value with minimal effort due to its accessible and user-friendly design.

The answer largely depends on what you need. If you’re seeking a domain-specific language built explicitly for statistical and data analysis, R is likely to have the tools you need, with over 12,000 packages already available and most of them built by academics and data scientists. However, Python has a slight advantage in its versatility, which allows for broader functions that also include data science applications.

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