Python in Coding, Data, and FinTech

As digital devices become increasingly sophisticated, the need for skilled computer coders has followed suit. Many workplaces now include promising opportunities for those savvy in top programming languages, and Python is no exception. 

Python is a versatile coding language that continues to drive technological advancement. It has gained popularity due to its multifunctional use and readability which make it accessible to a broad array of programmers.

Python is used in many verticals, but its uses in fintech, data analysis, and coding are growing rapidly; holding promising opportunities for those with the skills to seize them. 

Python in FinTech

In 2018, Python became one of the top three languages used in fintech. This specific use has increased in recent years because Python is relatively easy to write and use in comparison to other languages. While financial services applications can be increasingly complex, using a straightforward coding language like Python has somewhat simplified this complexity in a positive, productive manner. 

However, Python’s value in this space is not confined to its simplicity; it can also help reduce error rate, which is pivotal in such a heavily regulated industry as finance. Python also enables the financial industry to rely on open-source libraries within the Python ecosystem. 

Lastly, Python represents an important unifier between economics and the emerging world of data science. When you can create algorithms and formulas relatively simply, it’s possible to integrate work more easily. As a result, employees with the ability to write and understand Python are distinguishing themselves in the field.

For example, Python makes it easier for a company to integrate the work of an economist without coding experience into a more simple interface. From there, this data is integrated into third-party platforms that can also read and understand the Python algorithms. Ultimately, this allows for the easy integration of a variety of information types without requiring heavy, complex coding.

In addition, while some professionals may be unfamiliar with fintech software, they are likely familiar with common Python applications  used in fintech. For example, the Anaconda Python ecosystem is used by businesses that sell cryptocurrency to provide market insights. Netguru notes, “ Anaconda helps developers to retrieve cryptocurrency pricing and analyze it or visualize financial data.”

Other uses of Python in fintech include stock trading strategy modeling, updates to banking software, or the aggregation of data from disparate internal systems for more complete, efficient reporting. 

Python in Data Analysis

Data analysis is arguably one of Python’s most prominent applications. In fact, the language dedicates Pandas, one of its many libraries, specifically to data analysis. This open-source data framework strives to be one of the most flexible open-source data analysis and manipulation tools available, aiming to make it possible for nearly anyone to enact powerful, layered data analysis with relative ease. In this sense, Python’s broad application in the data analysis space is comparable to its fintech capabilities — it has given a fresh layer of accessibility to an otherwise hard-to-understand branch of technology. 

Python’s usefulness in data analysis extends beyond its applicable fintech applications. The language enables the building of powerful machine learning systems which take a massive amount of data and aggregate it to fit a user’s requirements. Think of it as the world’s most advanced Excel spreadsheet that quickly analyzes millions of data points. 

Data platform developers wield incredible power, as they can take a large amount of data and reduce it to digestible bits of information. For example, Holvi is a data-processing system used for invoicing, tax prep, or tracking expenses and it is set up via a simple Python coding system. Creating useful tools like Holvi allows businesses to scale and complete a task in 20 minutes that may have previously taken hours. 

Python has an extensive collection of libraries that have revolutionized data analytics. Put simply, the language is typically seen as more convenient than its competitors because it is compatible with a wide variety of programming languages. Python is often simple enough that individuals can easily learn it at home and put it into practice writing data analysis algorithms that can be directly applied to a business.

Python is a general-purpose language — that means whether it’s fintech, data analysis, or gaming software, it can be used to create a wide variety of interfaces that are comparable or preferable to competitors’ output. 

Python in Coding 

Python has become the most preferred programming language — in no small part because of its efficiency, versatility, scalability, security, and compatibility.

There is, perhaps, no better example of this notion than the popular video streaming platform Netflix. 

“We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN (i.e., the content delivery network) that serves the final video to 148 million members,” Netflix’s engineers wrote in a 2019 blog post.

The post goes on to detail Python’s impact — how its apps govern the CDN known as Open Connect, while also impacting the app’s engineering and statistical analysis functions.

Other audio/video apps, like Spotify and YouTube, favor Python for coding-related purposes, as do apps embedded in digital cameras and smartphones, business apps, and scientific/numeric apps. Python is also crucial to web and game development, text and imaging processing, and artificial intelligence/machine learning. 

In short, Python has become an integral part of the digital world and, as a result, the world at large. The ability to write code and develop algorithms has become crucial across the fintech, coding, and data analytics fields — whether you are working in finance, writing code full-time, or developing the next powerful tool for data integration and analytics. The use of Python is just one way to garner the expertise necessary to succeed in these fields, but it might be the most simple and effective tool we have today. If creating efficiency and organizational impact is your goal, learning how to write code in Python might be the best option to level up your skills and propel your career to new heights. 

Ready to become indispensable in the fintech, coding, or data analytics field? Check out Columbia Engineering Boot Camps to learn more.