Business Intelligence Advancement Utilizing Python

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Business Intelligence Advancement Utilizing Python – CTO. 13+ years in software engineering. Conference speaker, Top 50 Tech Leaders nominee, and tech-savvy professional who excels in designing intelligent software architectures that shape business growth.

One of the world’s largest code hosting platforms, Github places Python at the top of its PYPL Popularity index. This ranking is based on how often people search the internet for coding tutorials. There are of course reasons why they want to use Python application development. Other communities, such as Stack Overflow, also place Python at the top of their rankings.

Business Intelligence Advancement Utilizing Python

Finally, find the big names of world famous software companies using Python for their product development at the end of the article.

Data Analytics With Python: Use Case Demo

On average, the number of languages ​​for programming is more than 700. Python, released in the nineties, is one of the oldest. Yet today it is gaining more and more acceptance. So what makes Python stand out?

When you google a Python description, you’re bound to come across the words ‘high-level’, ‘interpreted’ and ‘general purpose’. Although the latter is easier to understand, the former terms deserve an extended explanation.

The term ‘interpreted language’ means that the code is executed immediately, without the need to compile it into the machine script. High level means it is high on the scale of abstraction and can even use the elements of human speech. In comparison, a low-level language is so close to the instruction set architecture of a computer, that it is almost similar to computer commands.

The Python script is more compact and concise than other coding languages. It has understandable syntax and is easy to master, which probably explains the immense popularity of Python tutorials on the web.

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Is Python suitable for any product at all? No programming language is universal, but the wise choice of a technology stack can certainly contribute to the product’s success.

Different products require different skills and expertise. If you intend to build an app for a specific platform, you will need specific skills. For example, you’ll need Objective-C or Swift to build native iOS apps.

However, Python is almost universal and has a wide variety of use cases. We will further examine them in more detail. Its versatility is one of the main reasons why enterprises choose Python for business project development to power their development efforts.

How large and sophisticated your end product will be can also determine your choice of technology stack. You can use PHP if you plan to build a small-sized website or web application, while medium-sized projects may require expertise in Java and C#. One way or another, larger projects will include different modules and integrations. So your technology stack will be more versatile. In this respect, Python is the best programming language for large-scale projects, although not those that require CPU-intensive tasks.

Intro To Data Analytics With Python

Your product scalability is an important aspect to consider when choosing a programming language. Essentially, scalability refers to a product’s ability to handle increasing workloads, as the business grows. If you need a highly scalable solution, choose a programming language that will scale quickly.

Python scripts must undergo interpretation to become readable by machine-level devices. It also has a slightly higher latency than most low-level languages.

As your product evolves, it will require maintenance, support and updates. So choosing a programming language with a strong support community and knowledge base is ultimately a wise decision. Python has a wide open source knowledge base and a densely populated community of professionals.

Another aspect to be aware of is the people factor. Consider the already available expertise of your internal development team, as well as the access to the development talents. If the coding skill you need is scarce in your area, you may want to reconsider your choice of coding language.

Data Analysis Projects With Python

Security considerations are extremely important to most industries, especially finance and medical services. Different technology stacks will have different security capabilities, so don’t discount this aspect when choosing a programming language.

Admittedly, Python is not completely free of security vulnerabilities. However, it has the lowest vulnerability index among a number of competitors: C, C++, Ruby, PHP and Java. In particular, it is the language most often used as a weapon for hacker attacks.

Also read: How to save money on a web project for businesses Learn how to choose the best technology stack for your web application

For many applications, Python may be an optimal language. However, the use of microservice architecture makes it possible to integrate modules written in different languages ​​into one project. Especially the one that is meant to be large-scale and complex.

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Released in 1991, Python has been around for quite some time. Decades ago, companies increasingly used it for web development.

It is understandable and intuitive to us humans. In fact, some experts prefer to call it a scripting language, since it uses scripts that are translated into machine code. Simplicity is deeply ingrained in its structure. Actually, Python coding principles seem like a Zen manifesto. Fewer lines also make it easier to debug.

In today’s competitive environment, the ability to quickly build a prototype and test a product idea can help companies gain an upper hand. Python is a good choice when you need a proof of concept.

Python is adaptable and can easily integrate with other programming languages. Such integrations lead to hybrid implementations: Jython, for example, stands for Python + Java, and CPython is a name for the integration of Python and C.

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Need to incorporate artificial intelligence (AI) and data analytics into your website? Python can help you integrate these and other technologies into a server-side backend. The top-notch data visualization capabilities enable programmers to create charts and reports for advanced data representation via a website or a mobile application.

Python supports asynchronous coding patterns, which makes it especially convenient for developers. Asynchronous patterns prevent an application from appearing blocked when executing a resource-intensive task; users can still navigate a UI while an operation is taking place.

The Python community offers a variety of resources, such as code libraries that contain pieces of pre-written code that you can easily integrate into your project. Needless to say, this helps speed up development and explains why Python is so immensely popular.

Finally, Python has a series of great frameworks that facilitate web development and are responsible for excellent usability and coding experience.

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There are more than 20 frameworks out there, but here we will focus on the most used: Django, FastAPI and Flask.

Among different development environments, Django is surely an unrivaled favorite. A full-stack framework, ideal for developing complex, feature-rich applications, Django offers many ready-made modules for basic application functionality so developers can focus on building something truly unique.

With Django, you can build websites and applications capable of scaling on demand as your business grows, and to handle heavy traffic and workloads.

Django works with most of the common databases. This allows developers to choose the most suitable one for a specific project or several databases at once. This feature is not particularly unique, but still very convenient.

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Interestingly, Django was initially built as a CMS system, but later became a full-fledged framework. Apart from web development, companies currently use Python and Django to build:

Flask is a microservices framework and differs from Django in many aspects. Flask provides Python developers with the basic feature set (routing, templating, WSGI compliance, and request handling) while offering modular design for anything else they might want to add.

As a microframework, Flask has almost no dependencies on external code libraries. It is very minimalistic and works faster.

This aspect makes it an excellent tool for beginning web developers, although they need to know Python to use Flask.

Top 30 Companies That Use Python For Success And Profit

Typically, companies use Flask to build web applications, from small to large. Websites, blogs and directories, large e-commerce sites and social networks can all be created using Flask.

FastAPI is a relatively new Python framework that is currently expanding its pool of users and becoming mainstream. It is an asynchronous framework, which means that it is meant to write concurrent code. FastAPI is also the most recent of Python frameworks, inspired by Django, Flask, and another asynchronous development environment, Sanic.

The use case for FastAPI includes high-performance applications, machine learning modules, or adding extra modules to existing Python applications.

As you can see, the list of use cases for Python-powered applications is quite extensive. If you haven’t had the chance to encounter the results of Python development, it can be confusing to learn exactly what products Python can contribute. Below we’re going to shed some light on the subject, so read on.

Explore Possibilities With Python App Development

Also read: How much does it cost to create an app? Check out the comprehensive guide on app development costs

The industry use cases are equally revealing. The business applications for Python are constantly expanding as software development companies embrace digital transformation. Today, the industries that use Python for product development include:

Python has been recognized as the main programming language for fintech applications and platforms. However, it is not just the fintech startups. Market incumbents, for example, Bank of America, have taken serious steps to transition from their legacy software and create applications with Python.

Healthcare providers are using Python-powered data analytics and machine learning in predictive diagnostics, medical image reading, patient care management, and personalized medicine.

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