Financial ModellingPython

7 reasons why you should learn Python, even as a finance professional

Python, believe it or not, has become one of the most popular topics in programming in recent years, and it is now widely used by big tech companies and developers. 

A growing number of fintech companies are now using Python for data analysis. Consequently, it is imperative that finance students should learn Python for finance to further their careers.

However, the question persists, what is it that distinguishes Python from other programming languages? and what makes it superior to traditional software in terms of data analysis? 

The answer is that it is compatible with a wide range of programming paradigms, including object-oriented functional programming.

Python is a dynamic and interpreted programming language that prioritizes code readability.  Most importantly, due to how similar it is to English, and due to its modularity, it is one of the easiest programming languages to learn.

In this article, we’ll look at some more reasons why Python is dominating the industry right now, and why finance professionals should start learning Python and its applications.

Why should a finance professional learn Python?

Here are some of the most apparent answers,

Easy for beginners. 

To begin with, Python is one of the simplest programming languages to learn. To begin performing data analysis in Python, you do not need any programming experience.

Python, unlike R and MATLAB, two other popular science and engineering languages, has a very simple syntax and coding rules, making it ideal for beginners. It’s also very simple to set up and get started.

Because of its ability to integrate with other languages, Python is also referred to as a “glue language.” Python runs on a variety of platforms, including Windows, Linux, and macOS, and can be used for any project, from gaming to data visualization.

Powerful community support

Currently, learning Python is as easy as typing a simple query on google search, ‘Python course’.

As soon as you do this, you’ll be flooded with ads on numerous Python courses, boot camps, workshops, and whatnot.

One more thing that makes it easy for financial professionals is that there are numerous online sources for learning it. The most important one is, the official Python documentation. It packs everything you could want to know about the language—and because Python itself is simple enough, learning the language is a breeze.

Of course, an easier way to learn it would be to enroll in this course on Python for finance, which has modules for training individuals from the very basic of python to advanced concepts like financial modeling.

Open-source library 

Python has a plethora of open-source libraries that extend the functionality of the core language. Installing these libraries is pretty easy too and doesn’t require much knowledge in terms of ‘technical skills’.

The Python libraries cover a wide range of topics, from simple GUI application development to machine learning, networking, and powerful data analysis.

Python is an excellent programming language for the financial sector. Python is widely used in the investment banking and hedge fund industries to solve quantitative problems for pricing, trade management, and risk management platforms.

Its open-source library or modules is of high importance, and there are several such modules, some of which are given below. 

  • NumPy: a complete scientific computing library in Python for performing linear algebra and high-level math, including the ability to work with matrices and other data structures. If you’re familiar with MATLAB, NumPy will feel right at home.
  • SciPy: SciPy is an excellent library for data scientists and engineers that builds on NumPy and allows you to work with N-dimensional arrays and perform a variety of optimization and linear algebra operations.
  • Scikit-learn: This is the library to use if you’re interested in machine learning. Scikit-learn implements a number of well-known machine learning algorithms. If you’ve already installed NumPy and SciPy, you’ll be pleased to know that Scikit-learn is compatible with both of these packages!
  • Pandas: Not the animals. Pandas is a fantastic open-source data manipulation and analysis library. If you’re familiar with R data frames and SQL syntax, you’ll find that pandas combine the best of both worlds in a small Python library.

Also, read the blog, best python modules to learn for financial modeling.

Analytical tools are available.

Python is widely used in complex quantitative financial solutions that process and analyze large datasets. In this regard, open-source libraries are extremely useful because they simplify the process and aid in data visualization. 

As a finance professional, you can make use of these tools to analyze stock market data, and build models to make analysis-based predictions.

Furthermore, these analytics-based libraries provided by Python easily resolve the most complex calculations. Most Python-based solutions include ML algorithms that aid in predictive analysis. Furthermore, this greatly assists financial service providers in providing better service to their customers.

Banking Applications

Python is widely used by financial solutions companies that deal with payments and online banking platforms. Furthermore, it is adaptable and simple to use, this is why most financial institutions and banks prefer to use this technology.

Python is widely used in quantitative finance to process and analyze large datasets, such as big financial data. Pandas libraries, for example, make data visualization easier and allow for sophisticated statistical calculations.

Python skills are in high demand among banks such as Credit Suisse and Barclays. C++ is no longer as popular, but it is still used.

When it comes to accounting, Python is most useful when working with data. It can read any type of data, both structured and unstructured.

Rapid application development

Python is preferred over other languages in the fintech and traditional finance sectors due to its quick software development time.

Because of the abundance of expansive data analysis libraries, developing fintech application forms in Python takes a fraction of the time it would take with data analysis tools like Microsoft Excel and R because you wouldn’t have to waste time writing code from scratch.

It’s interesting to note that Python has been used in the development of platforms such as Bank of America’s Quartz and J.P. Morgan’s ATHENA, as well as by large corporations such as Google, Facebook, Instagram, and Spotify. Many other companies, including Citigroup, now require their data analysts to be fluent in Python and to attend Python training classes.

Now the last but on the least, there among many reasons to learn python the last one on the list is 

Python’s applications in Blockchain

If you are a finance professional, you might be interested in tapping into the vast potential of Blockchain and cryptocurrency.

If that’s the case, then there is no better technology than Python for you to achieve your goals.

Python provides some super cool libraries like Dash, Anaconda, Cryptosignal, etc. using which you can extract cryptocurrency prices and make analysis-based predictions on their movements.

To cut a long story short, it can be said that Python’s use in finance and related activities demonstrates how important it is to the financial industry. 

Many areas of the financial industry require resources to generate and manage complex data. As far as today’s world is concerned, Python is the best tool for the above purposes.

Furthermore, to gain practical knowledge of this language, you can always enroll in this hands-on information-packed Python for finance program.

Python is a programming language that is simple, flexible, and powerful, with applications in data science and beyond. It’s also a great time to start if you’re new to programming. Hope it was helpful for you. 

Thanks for Reading!

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