Data Science Project end-to-end: use-case in banking & collection

How can a real-world business apply analytics and machine learning and benefit from it? Generating value from data is one of the most exciting and hottest challenges out there.

The potential of data science in banking and debt collection has not been explored thoroughly. Hence, there is huge room for establishing the best practices in machine learning and data engineering.

During this talk, Igor Smirnov will guide you through the main stages of a data-related project. Starting from an initiation phase where business struggles with a particular problem. Up to an implementation phase where a cross-functional team can deliver direct value to the company.

In addition, we will touch on some important challenges, like data quality and security, validation of results, and collaboration among different teams. This talk will encourage you to use an analytical approach in the financial domain by showing a comprehensive picture of a data science project.

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