The growing role of information and data within our society has had a strong influence on many industries that need to make predictions and automate complex tasks. Within the world of business and finance, we see data analytics most commonly used for the purpose of making decisions, assessing risk, and detecting patterns within a system. Through developing knowledge of programming languages like Python, Financial Analysts will not only further develop their data science skills but also learn more about the burgeoning FinTech (Financial Technology) industry.

The Role of Data Science in Financial Analytics

Data science is a fast-growing field that focuses on the analysis of information and data, with a variety of applications across industries and institutions. For professionals in business and finance, data science plays an important role in the realm of financial analytics. Based on the analysis of financial data, Financial Analytics applies the skills that data scientists use to the world of business and finance. Acting as the data analysts of the finance world, Financial Analysts are commonly seen within institutions, such as insurance companies, banks, and investment firms. Within insurance companies and banks, Financial Analysts are able to audit individuals and assess models of risk for those that are looking for an insurance plan or a loan. Within investment firms, Financial Analysts can also develop models and forecasts about what stocks to buy and sell, as well as timelines for making investments.

While the world of business and finance is known as an industry which deals with levels of risk and uncertainty, the role of Financial Analysts is reducing that risk through the analysis of information, data, and the patterns and trends which emerge from them. As more institutions collect and store large repositories of data, the accuracy of statistical models that are used to predict the outcomes of decisions through forecasting trends in the market and economics will only continue to improve. Like other areas of data science, the necessity of Financial Analysts is also projected to increase over time. In addition, many Financial Analysts can combine their data science skills with software engineering to break into the world of financial technology.

Python and the Development of Financial Technology

Financial Technology, or FinTech, is a fast-growing space within the business and finance industries that focuses on the development of software and platforms which are geared towards banking and investing. Prioritizing financial exchange through web-based platforms and mobile applications, the development of financial technology has facilitated the popularity of using QR Codes to exchange cash (such as using Venmo or CashApp) and even investing on the stock market from the comfort of your cell phone (as seen with investment apps like Robinhood). In addition, the popularity of cryptocurrencies, such as Bitcoin, also demonstrates the ever-growing importance of FinTech in the exchange of money moving into the future.

In addition, FinTech has popularized the use of artificial intelligence (AI) and machine learning through financial advising algorithms which are embedded into banking and investing apps. For example, Bank of America has recently introduced its version of a virtual financial assistant: an AI named Erica which interacts with customers and offers insights and predictions based on data collected from the platform and users’ financial habits. FinTech is not only focused on the creation of platforms and applications, but robots and algorithms which can stand in for more traditional methods of financial decision making.

For data scientists and Financial Analysts interested in transitioning into the FinTech industry, knowledge of programming languages can be used to work on the development of applications and algorithms. Developing skills in a coding language like Python is essential to developing a career in financial technology because Python is commonly used in the development of platforms and predictive models. Some of the most popular skills for Financial Analysts that specialize in Python are focused on working with data science libraries and statistical models.

Popular Python Skills for Financial Analysts

Through the development of financial technology and data analytics, the field and industry of data science has played a very important role in many of the changes that have been made in the world of business and finance over the past few decades. Financial analysts who want to stay up to date on these important shifts in the industry should further develop their data science skills through more advanced knowledge of programming languages.

Current and prospective Financial Analysts can build a set of highly desirable skills which are essential to the future of business and finance by learning more about financial technology and Python. Financial analysts require some background in statistics and other quantitative methods of data analysis. By using statistical models such as linear regression, you can do the work of calculating and anticipating interest rates, as well as using predictive analytics to create risk management models and deploying market trading strategies.

Want to expand your Python and Financial Analytics Skills?

Programming is an essential skill for any career in data science and analytics, and learning Python is especially useful within the world of business and finance. With multiple data science classes to choose from, Noble Desktop offers a series of options for learning programming languages and analytical skills. Noble Desktop’s Python classes not only introduce the fundamentals of Python, but students and professionals will also learn more about how they can apply their knowledge of Python to pursuing a career in finance.

Through the Python for Finance Bootcamp you can learn more about how to create risk assessments and other financial models which are useful for making investments on the stock market. The FinTech Bootcamp is an excellent introductory course for learning how to apply Python to financial analytics and developing machine learning models. Noble Desktop also offers more advanced bootcamps in financial modeling for students that are looking to expand their skills outside of the world of data science!