If you’re interested in pursuing a career in fintech, you might be wondering what technologies and tools you’ll need to use on the job.
Besides learning about and evaluating the user experience of mobile payment apps like Zelle, Venmo, Cash App, or Paypal, if you’re considering a fintech career, you should also take the time to learn programming languages like Python.
Python is a core programming language taught in UW Online Collaboratives’ two graduate certificates, Emerging Technologies in Fintech and Leading Fintech Transformations. In these programs, students use Python for data visualization projects. They also engage in statistical analysis, using historical data, statistical methods and Python to predict price changes over time. Fintech professionals may utilize many apps with prediction tools to help users plan for their retirement or other financial goals.
How Python is Used in FinTech
Python is one of the most popular programming languages used in fintech development because of its broad range of applications including:
- Analytics tools. Python is commonly used in quantitative finance to process and analyze massive datasets, such as financial data. Pandas, a Python library, simplifies the process of data visualization and allows complex statistical analyses. Python-based solutions are equipped with powerful machine learning algorithms that enable predictive analytics, which is extremely important to all financial services providers, thanks to libraries like Scikit or PyBrain. Examples of this include Holvi, a digital banking provider for small businesses, and Iwoca, which offers credit financing to small businesses.
- Stock trading platforms. The stock market generates enormous amounts of financial data, which necessitates extensive research for which Python comes in handy. It can be used by developers to build solutions that discover the best stock trading methods and provide practical, predictive analytical insights into the state of specific markets. Use case scenarios include algorithmic trading in fintech products. Examples include: Quantopian, QuantConnect, Zipline, and Backtrader.
- Cryptocurrency. Every business that sells cryptocurrencies requires tools to analyze cryptocurrency market data and make predictions. Anaconda, a Python data science platform, aids developers in obtaining cryptocurrency prices, analyzing them, and visualizing financial data. As a result, Python is used by the vast majority of web applications that deal with Bitcoin analysis. Examples include: Dash, Enigma, ZeroNet, koine, and crypto-signal.
Why Python is so Popular in Fintech
One of the many reasons Python is used frequently in fintech is because it’s versatile and scalable, with more than 137,000 libraries and countless frameworks. It’s also easy to read, learn, and use.
If you’re new to programming languages, know that anyone with basic computer knowledge can learn the basics of Python in a single month. Python’s syntax is simple, and a Python coder with basic knowledge can easily understand other programs written in the same language just to get a context of what’s actually happening inside the code.
For example, in FNT705, Fintech Analytics, students will use Python for time-series forecasting. Many organizations use time-series data in their operations to track things like energy consumption, daily stock prices, and social media engagement metrics. By looking at this data, organizations can understand trends and forecast future events to maximize sales.
In addition to Python, students will learn other technologies when completing a certificate:
- Students in FNT735, Blockchain Technologies, will see how blockchains work, the details of the cryptographic techniques used to implement them and the architecture used in Bitcoin and Ethereum systems. Students will look inside actual blockchain implementations and set up mining codes, and see how these systems enable decentralized finance and how smart contracts are programmed.
- In FNT730, Technologies in Fintech, students will get an introduction to HTML, JavaScript, information security and web security concepts and tools. They will work with AWS tools, create and deploy simple applications and learn about databases and SQL. They will also study RPA concepts and use cases and address user interface and UX concepts.
- In FNT740, Artificial Intelligence and Machine Learning in Fintech, students will use Google CoLab and Python to implement the Machine Learning Process. CoLab provides free access to graphics processing units (GPUs) and tensor processing units (TPUs), which can be beneficial for training machine learning models and other computationally intensive tasks. Students will apply concepts they learned with Google CoLab to other platforms like AWS Sagemaker and Azure ML Studio.
Are you interested in learning more about fintech? UW Online Collaboratives’ Fintech graduate certificate programs are 100% online and each will provide you with the technical competencies that you need to advance your career in fintech, while empowering you to complete your education when it’s convenient for you. To contact an enrollment adviser, click here.