Python is way more widespread than you may realize – it’s practically everywhere now!

Python is also well known to be the core foundation of creating famous software services such as:

  • Google
  • YouTube
  • Instagram
  • Spotify
  • Reddit

Aside from being one of the leading programming languages available, Python is also very easy to learn. It’s also clear to read, and very simple to write in. This gives a huge boost to the speed of development without having to sacrifice reliability.

And, thanks to the high demand for it, Python is also very supported in the ever-growing community. This helps the newcomers have faster understanding.

But how does one answer the question “What is Python used for?”.

It’s really a very broad question that requires tremendous research. And we’ve done that. We’ve listed the top 4 industries where Python is used for mostly:

  • General Web development
  • Data science
  • Machine Learning
  • Artificial Intelligence
Advantages and disadvantages of Python
Advantages and disadvantages of Python

General Web development

Python may not always be the primary choice for building the server-side of the website. The language is multipurpose, which means that it’s usable in the majority of programming fields.

Take for instance machine learning. Experts always promote Python as the ideal language when it comes to machine learning. It’s perfect for data science as well. But we’ll talk about that in the next section.

Be that as it may, Python is a very solid option for web development too. It has a simple syntax, a broad variety of frameworks, and a strong ecosystem. As mentioned above, Instagram – which receives millions of visitors per day – runs on Python.

Building a web with Python means that you produce code responsible for running in the server rather than use the browser.

Here are 7 very persuasive reasons why you should use Python when it comes to web development:

  1. The code you’ll write will be easy to read, and you’ll understand most of it just by looking at it. (No more curly braces and code blocks
  2. Its standard library support allows you to execute a lot of complex code very easily
  3. Multiple systems and platforms support
  4. It’s OOP-driven – good support for objects, modules, and other reusable mechanisms
  5. The Django framework is extra smooth – it’s not a surprise it’s currently the most famous framework for web development
  6. Python lets you develop quickly with much lesser code
  7. It’s scalable – you can expand even complex applications with ease

Ultimately, whether you choose to adopt Python for web development or not is purely up to you – and it does depend on many factors.

Data science

Python is becoming more and more popular because, well, plenty of reasons. It’s even considered mandatory to master Python syntax before doing anything in data science.

There are countless reasons why you should learn Python, and one of them is to analyze data. Or, just get into the field of data and analysis.

Data analysis, in essence, is the methodology of gathering data and processing them in such an order that you’ll get useful insights. It’s all about the utilization of the major techniques related to the visualization and manipulation of data. Through these, you’ll learn to get very valuable insights. These specific insights are what help companies form better strategies and decisions.

Python is perfect for data science. Period. It’s an object-oriented, high-level and pretty much extremely interpreted language. It’s known for having dynamic semantics.

Exploratory Data Analysis in Python
Exploratory Data Analysis in Python

Python is known for its immense capabilities when it comes to Rapid Application Development – especially because of its dynamic bindings and typing. It’s extensively used for scripting and used as a “glue language” to link the present existing components together. It jibes pretty well with data analysis as well, which is probably the reason why it’s touted as the perfect language for it.

It’s pretty quick when it comes to speed, which is a must in the data analysis world. It also has a lot of fast libraries, such as Matplotlib.

Python is always under development, which means that it receives regular updates and releases. This can ensure you that learning Python for data science is always a time well-spent. And as long as big data and machine learning become more common, the demand for Python developers will always rise further.

Machine Learning

To put it simply, machine learning is making a computer perform a task without explicitly programming it. Simple, right?

In today’s world, pretty much every advanced system has a machine learning algorithm at its heart. The best examples are Google Search engine, Amazon Product recommendations, LinkedIn, Facebook, Twitter. All of these big companies have machine learning algorithms embedded in their system – in one form or another.

Here’s why Python is widely used in the machine learning field

  1. It’s simple to learn, compare to C, C++, and Java. Its syntax is way simpler and easier
  2. It has amazing data handling capacity
  3. It’s open-source – Python, along with R, are gaining momentum in the Analytics domain for the sole purpose of being open-source
  4. The capability to interact with pretty much all the third party languages and platforms

Consider the following example:

Weather forecasts rely on past readings from a century’s worth of weather records. Machine learning can help make more accurate predictive models based on past weather events. Python can do this because it is lightweight and efficient at executing code, but it is also multi-functional. Also, Python can support object-oriented, structured and functional programming styles, meaning it can find an application anywhere.

The ways of Machine Learning in different industries
The ways of Machine Learning in different industries

Artificial Intelligence

Artificial Intelligence, along with Machine Learning are the new trend of the IT industry. Today’s Artificial Intelligence is way beyond science fiction idea – it has become a necessity! It’s being widely used to process and analyze tremendously huge volumes of data. And, not surprisingly, Artificial Intelligence handles the work that other-wise can’t be done manually. This is all because of its significantly increase volumes and intensity.

Artificial Intelligence is applied in order to predict things that help people create strong strategies and find more effective solutions. FinTech, for example, uses it to do market research and predict the best way to invest to get bigger profits. The traveling industry, on the other hand, uses it to deliver personalized suggestions. These are just two out of countless places where Artificial Intelligence is taking place.

Python plays a huge role in all this. It offers many features that are helpful in the usage of Artificial Intelligence. And that makes it the best language for these kinds of purposes. It’s really no surprise that industries are using Python for predictions and other machine learning tasks.

Artificial Intelligence is being used in financial services a lot in order to “connect the dots” of risk management. It’s predicted that Artificial Intelligence in Fintech could reduce operating costs by 22% by 2030 – resulting in an impressive $1 trillion.

Artificial Intelligence in FinTech could reduce operating cost by 22$ by 2030
Artificial Intelligence in FinTech could reduce operating cost by 22$ by 2030