The amount of data available to manage risk in a portfolio as well as the information needed to perform a thorough financial analysis of a company grow at an ever increasing speed. Moreover, data can no longer be gathered from one single source of information.

This practical program covers key techniques - including several aspects of supervised and unsupervised machine learning - that you can use when mining financial data.

Most exercises and case studies are illustrated in Python, allowing you to learn how to work with this flexible programming language.

• Portfolio managers
• Risk managers
• Professionals looking to introduce data-mining concepts in their day-to-day tasks
• IT developers
• Statisticians
• Quant analysts
• Financial engineers

• Basic notions of statistics
• Good working knowledge of Excel
• No prior knowledge of Python is required