The Data Science Forum is a not for profit educational organization with a simple but important mission: to help people gain understanding, acquire knowledge, and develop skills in computational methods, methodology for data analysis, and applications in statistics and mathematical finance. To fulfill that mission, the Forum provides both education experiences and career development opportunities for students and alumni of the University of London group.
Why Data Science?
We live in a world where the way industries compete is increasingly defined by the proliferation of data and increasing technological complexities. Organisations of all kinds are awash with data. All sorts of data, often coming from disparate sources, including financial data, customer data, audio data, social media connections, and images, are collected by banks, insurance companies, and retailers. But for many in the industry, extracting insights from a data warehouse that contains structured and unstructured data is a daunting task.
Financial services, in particular, have adopted data analytics to inform better investment decisions, often deploying machine learning as a core technology. In conjunction with stateof-the-art deep learning models, algorithmic trading uses sophisticated mathematical models to optimize portfolio returns. The adoption of big data has transformed the landscape of financial services. Investment banks and asset management firms use voluminous data to make sound investment decisions. Insurance and retirement firms can access past policy and claims information for active risk management.
Along with its apparent benefits, significant challenges remain with regards to our current ability to capture the mounting volume of data. The increasing volume of market data poses a big challenge for financial, and other, institutions.
Most of these sessions are designed for advanced undergraduate and early graduate students who are comfortable with mathematical notation and formality. Some may need to review their knowledge of mathematical concepts alongside the algorithms under consideration.
Some sessions will be aimed at a more advanced audience, where are expected to have some background in the field. If you find the learning curve too steep, then do not worry. Advanced topics will be introduced gradually but rigorously while introducing numerical methods alongside mathematical background and motivating examples from modern computer science. It is hoped that the practical nature of this approach will help develop the intuition and comfort needed to understand more extensive literature in each subtopic.