Applying data science techniques to production trading desks typically involves moving data sets around for what-if trial and error simulations - frequently without audit controls or change management mechanisms in place to tie insights gleaned to data sources.
Moreover, there’s direct value created if you can streamline your workflow and get your insights fast and correct. You may learn something valuable….from last week’s batch run. Is the insight still valid? Hope so. Did you learn that insight from the Hadoop cluster data lake or from the Snowflake data dump? Where did those files originate from again? Bob’s out this week.
Key Learning Topics:
Data science applications to trading systems
Risks commonly encountered in nascent applications of these data science initiatives without tighter controls
How notebooks can be embedded in existing applications, trading or risk management, to better control access, report on results and satisfy risk / audit teams
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Applying data science techniques to production trading desks typically involves moving data sets around for what-if trial and error simulations - frequently without audit controls or change management mechanisms in place to tie insights gleaned to data sources.
Moreover, there’s direct value created if you can streamline your workflow and get your insights fast and correct. You may learn something valuable….from last week’s batch run. Is the insight still valid? Hope so. Did you learn that insight from the Hadoop cluster data lake or from the Snowflake data dump? Where did those files originate from again? Bob’s out this week.
Key Learning Topics:
Data science applications to trading systems
Risks commonly encountered in nascent applications of these data science initiatives without tighter controls
How notebooks can be embedded in existing applications, trading or risk management, to better control access, report on results and satisfy risk / audit teams
