Python for Data Science Vs. Python for Web Development


Python for Data Science Vs. Python for Web Development

Python is one of the top programming languages for leading big data companies and tech startups. It is a versatile language used for various purposes, including numerical computations, data science, web development, and machine learning.

That said, the blog highlights its role in data science vs. web development. It will help you understand the importance of Python in the two diverse fields and its future in data handling as well as software development:

Python for Data Science

Data science is the art of quantitative and analytical computing that companies use to optimize their strategies. Meaning, it converts meaningful, raw data into a powerful marketing and business strategies that boost a company’s return on investment.

Python is relatively easy to use in various domains, like signal processing, oil, and gas, as well as finance. So, it is considered one of the best options for a variety of jobs, including website construction, data mining, or running embedded systems in one language.

So, then what role does it play in data science? Let’s dive deeper to understand the importance of Python for Data Science:

Role of Python in Data Science

Programmers prefer Python for its simplicity and readability that makes it easy to understand and learn, which is why it’s ideal for quantitative analysis on a large scale. Mathematical optimization, probability, and statistics are all constituents of machine learning. Python makes it easy to understand, which, in turn, makes machine learning a child’s play for data scientists.

It comes with several pre-built libraries, multiple frameworks, and features that are beneficial in data visualization, data science, and evaluating comprehensive datasets. Various libraries also mean that data scientists get bespoke packages that fit their needs and are readily available for download.

Furthermore, Python offers easy integration with other programming languages and scalability. It’s also future-proof, meaning it’s not in danger of going obsolete any time soon.


Contrary to popular belief, Python is not the perfect fit for statistical analysis; and data scientists using it for the first time may find it a little unorthodox and out of place. But its immense advantages and general-purpose nature had organizations use it for data science.

So, what are these benefits that make the language invaluable for data scientists? Let’s take a look:

  • Easy to Learn and Use

As mentioned above, Python offers a smooth learning curve with a simple syntax and better readability. It makes it easy for amateurs to pick up or for new hires to join an ongoing project.

Python also comes with data mining tools that make data handling easier for veteran and beginner data scientists. Additionally, it provides better support with deep learning and machine learning, helping experts develop algorithms for the two fields.

  • Pre-Built Libraries

One significant reason for the use of Python in data science is its assortment of pre-built libraries and frameworks. It simplifies coding, decreasing development time, plus with multiple options, data scientists have the opportunity to find one library that fits their needs. It’s also why they can use Python for natural language analytics and sentiment analytics.

NumPy, SciPy, and PyBrain are some of the libraries used for scientific computation, advanced computing, and machine learning-based computation, respectively. Other frameworks used in data science include Scikit Learn, Seaborn, Pytorch, TensorFlow, Matplotlib, and more.

  • Simplicity

Python’s simplicity and readability are two of its best attributes that make it ideal for beginners. It has a short learning curve and a clear syntax that allows you to dive into the development stage, instead of spending time reading the documentation. It also contributes to speeding up development time as you can code faster than with other languages.

Plus, data computation involves complex algorithms and multi-level workflows. So, Python being easy to understand and use, allows data scientists to use it for data analysis and statistical analysis, as well as text processing.

  • Popularity and Community Support

For any programming language to be a top choice for programmers, it needs to have plenty of community support and documentation that make it easy to learn for beginners.

Thus, as an open-source language, Python is popular among other programming languages. Not only does it have multiple libraries and a short learning curve, but it also has a community of experts ready to offer help through development processes.

It’s high-quality documentation also ensures amateur data scientists can keep up with it and efficiently carry out challenging data computation analytics.

  • Not Platform Specific

We mentioned above that Python supports integration with other programming languages. That makes it platform-independent, meaning it has the flexibility to function efficiently on any platform you are targeting.

Using a pre-written code, you can make slight alterations and switch to another platform. It can not only shorten development time but also make it easy to test the codebase on multiple platforms before launch.

  • Robust, Flexible, & Scalable

Robustness is one of the significant attributes of Python that makes it ideal for data science and computational analysis. It’s a relatively short line of code, compared to other languages, make it easy to manage, fix bugs, errors, and any issues that arise.

Flexibility and scalability are other attributes that contribute to its use in data science. It allows data scientists to develop data mining models, machine learning, classification, web services, and more.

Its flexibility enables experts to resolve problems end-to-end within less time than it would take with other languages. It is also what makes Python scalable and faster.

  • Builds Better Analytics Tools

For data scientists, massive datasets are an occupational hazard that they can’t avoid. Python takes away the stress and makes their jobs a lot easier. With a quick analysis of data collected, it provides instant answers on how companies can optimize their marketing and business strategies to boost growth.

Analytics tools are integral to data science for providing information on metrics that determine the performance of a company. Python is ideal for building these tools because it offers better insights, makes it convenient to compare big datasets, understand patterns, and more.


In light of the benefits of using Python for data science, it’s future seems promising. Businesses of various domains looking to leverage the power of analytics to boost their marketing efforts, conversions, and sales, are increasing the demand for Python.

It’s versatility, and fast performance makes it invaluable for correlating datasets. Plus, it’s easy to learn and use, which makes it ideal for both beginner and veteran programmers.

In present-day, Python has all the tools needed for transforming, processing, cleaning, and crunching data at large-scale. It can be useful when you have AI or machine learning projects that involve natural language analysis, sentiment processing, or predictive analysis.

It is a prerequisite for the role of a data scientist, and a competitive advantage over others in a similar line of profession.

Python for Web Development

Web development is a term that defines creating and maintaining websites. A typical web development Bootcamp will teach you everything from front-end development to backend coding.

Front-end is the interface that users interact with to perform their desired command, whereas backend is the business logic that deals with comprehensive datasets.

What makes python ideal for web development is its numerous advantages, including multiple libraries, readability, swift processes, and more. Over the years, it has become a flexible, dynamic, and immensely versatile language, gaining the preference of many developers over other options, including Java and C++.

Additionally, it is compatible with a variety of web projects, whether they are simple or complex. Developers can work with Python in diverse domains, including transportation, travel, healthcare, finance, and more.

However, the web developer community is divided over the use of Python for web development. So, let’s look into its importance as well as pros and cons to determine how it fares against data science:

Role of Python in Web Development

Python frameworks play a significant role in developing large-scale web development projects that cannot be built using languages like .net or PHP. It has several options that are beneficial in different areas of web development.  

Let’s look into each one individually to understand the role of Python in web development:

  • Django

Django is an open-source platform built on Python that offers fast and scalable development. It also has extensive community support and documentation that make it one of the most popular frameworks.

It is dynamic and versatile, providing an admin panel, directory structure, and database interface during development, as well as multiple libraries and dependencies. It is ideal for use in developing database-driven applications but not for projects that don’t involve sophisticated features.

  • Flask

Flask supports beginners looking to get started with single-page web development. It is lightweight and minimalistic, commonly referred to as a microframework.

It also means, with Flask, you need to manually add libraries or extensions as you code because the framework doesn’t provide them automatically. But unlike its counterparts, Flask offers complete control and flexibility in your web development project.

The drawback is that Flask doesn’t offer support for validation, data abstraction, and several other constituents of other frameworks.

  • CherryPy

CherryPy works as a base for other full-stack web development frameworks like TurboBears and Web2Py. It supports python conventions, allowing developers to work on their projects as they would with OOP programming.

Pros and Cons

While some developers recognize the perks of using Python for web development, others are against it. So, let’s see what has the community divided:


  • Easy and Readability

As mentioned above, Python offers simplicity and readability, which makes it easy to learn and understand. It is also why it’s ideal for web development. Python provides low barrier entry and useful visualizations that make it easy to use with complex systems. Plus, its readability allows developers to understand the code written by fellow professionals in the team.

  • Prototyping

Python allows a swift and agile development of prototypes. Its flexibility and readability contribute to making and testing prototypes much faster than with any other programming language. Plus, it not only saves development time but also helps limit expenses during development.

  • Scripting and Software Testing

Integration with C++ and Java allows developers to use Python for scripting. It enables them to design an entirely customized application with extensions.

Additionally, it’s handy in QA testing for its simple and clear syntax. It offers an easy-to-use testing framework that QA experts with limited tech knowledge can easily use for test automation.

  • Enterprise Application Integration

Aside from Java and C++, Python also supports integration with other languages, including PHP and .net. They are used for enterprise development, which allows developers to use Python as well.

  • Server-Side Scripting

Much like scripting software, Python also supports server-side scripting and graphical user interface that is integral to web development. It’s simple syntax not only speeds up the process, but its code features functional modules and connection that allows developers to execute the algorithm, following user input.


  • Design Restrictions

One drawback of using Python for web development is that it puts restrictions on the design. It’s because the language is dynamically-typed. Meaning, a few tasks that are completed with a statically-typed style, Python executes them during software runtime.

So, if you integrate heavy design elements on your website, it will stall the software or increase load time, preventing smooth user experience.

  • Limitations for Memory-Intensive Tasks

Python’s flexibility is one of its greatest attributes for web development or data analysis. But it leads to high memory consumption, which limits its use for memory-intensive projects.

  • Speed

If speed is integral to your web development, Python may not be the option for you. It is an interpreted language that uses different techniques to translate the code, which makes it slower than other programming languages.

  • Threading Issues

Global Interpreter Lock creates limitations for Python. It is a mutex that prevents the execution of multiple threads at a time. So, a multi-thread program will be slower than a single-thread one.

Conclusion: Data Science Vs. Web Development

In a nutshell, one of the best assets of Python is its simplicity and readability that makes it ideal for beginner or seasoned developers to use for data science and web development. For startups, in particular, the language offers flexibility, scalability, supports prototypes and MVP development, and more.

Additionally, the adoption of Python for machine learning and AI is increasing its worth in data science, whereas new emerging languages are replacing Python in web development.

That’s not to say the language isn’t worth your attention, no matter your expertise. Some of the top companies using Python for their web development include Pinterest, Dropbox, Instagram, and Bitbucket. For more information visit QuickStart and get in touch with our experts today.

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