About Financial Modeling Data Science
Data science can be incorporated into financial modeling to help make the assumptions more robust, more accurate, and hopefully closer to an expected reality. Since data science is the study of how to extract information and insights from data (especially using statistics in this case), it can be very helpful for determining assumptions in models and performing sensitivity analysis to measure the range of outcomes as they change.
Data plays a significant role in the finance industry. Many financial services firms struggle to find professionals with expertise in data science, so they increase their spending on sourcing and recruiting top candidates. Professionals who are into data science are proficient in certain tasks such as data mining and analysis. They typically have an educational background in Mathematics, Statistics or Calculus.
As financial institutions lean into the use of technology, professionals with data science expertise need to have at least a basic understanding of Java, C++, Python, and other programming languages used in financial modeling.
To learn more, check out CFI’s Introduction to Python for Finance Course.
Image: CFI’s Python Fundamentals Course.