Data Science
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Many traders use brokerage statements to create a verifiable track record. Unfortunately they are not credible for this purpose. A better alternative exists.
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Some analysts spend months building backtests that no-one is willing to trust. Learn why, and what to do about it.
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The data marketplace suffers from severe and costly information asymmetries. These reduce the quality and value of available data.
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Financial analysis will often yield incorrect results if the underlying data is not point-in-time.
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Ensuring integrity and trust: learn how blockchains can and should be used for data validation, data integrity and data provenance systems.
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Knowing where data comes from is critical to understanding the data. This is particularly true for financial data due to its time sensitive nature.
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