Quant teams are under pressure to extract alpha from increasingly complex and unconventional datasets. As new signals, factors, and sources emerge, how do you build models that are not only predictive—but resilient and scalable?

·      How do we quantify data quality?

·      What are the impacts of data quality on quantitative models, e.g. can models work with dirty or missing data?

·      Learn how to integrate new data sources into model pipelines without compromising stability

·      Understand the evolving role of alternative data in sustainable alpha generation

·      Assessing signal trading and landing models, assessing processes and cleaning dirty data

·      What are the different use cases of data and data quality requests from various stakeholders, including business, validation, risk, and regulators?