The popularity of data science techniques such as data mining and machine learning has grown enormously in recent years. They present effective solutions to process and analyze the huge amount of data available to risk managers and financial analysts.
With the advances in computing power and distributed processing, it is now possible to process - and make sense of - the vast array of information that can be gathered from several different data sources.
This hands-on program covers key techniques - including several aspects of supervised and unsupervised machine learning - that can be used when mining financial data. The program also focuses on advanced data science techniques that are becoming widely used in financial markets for text analysis and artificial intelligence (AI): Natural Language Processing (NLP) and Deep Learning (DL).
The program is delivered entirely through workshops and case studies. Participants will learn how to implement natural language processing techniques by building a sentiment analysis model to analyze text. In the deep learning section, participants will focus on the different neural networks that can be put at work for data classification, time-series forecasting, and pattern recognition.
All exercises and case studies are illustrated in Python, allowing you to learn how to work with this flexible, open-source programming language.
