Python and Machine Learning for Financial Analysis

Machine learning and AI have multiple largescale applications in finance & banking, and the industry has been quick to take advantage of any new technology. Utilizing Python has been a particularly fruitful approach for many finance professionals, due to the programming language’s vast capabilities.

With Python, financial analysts can process a huge amount of data for their organization. They can make accurate calculations for risk and trade that improve business operations to a level of sophistication that would not have been possible even five years ago. Major banks like Credit Suisse use Python to: detect fraud, better align their services to their customers’ needs, set up management systems, and
model smart investments.

These developments have all made a huge difference to the fundamental pillars of finance & banking.

The course all the fundamentals of Python programming. This makes it a useful refresher for Python users who want to work in finance. But it is also useful for anyone who is already in the finance industry and wants to learn Python.

Suitable even for complete beginners, Ryan covers the various data types, variable functions, and file operations that any Python user should expect to see when using the program. Once you’ve grasped the fundamentals, you’ll get a deeper understanding of key Python libraries such as NumPy and Pandas, as well as the visualization tools that you’ll use at the end of your projects, such as Matplotlib, Seaborn, Plotly and Bokeh.

By the end of the course, you’ll have six completed projects to put into your portfolio when you go on the job hunt!