This presentation will dive into machine learning, which is very applicable to data scientists in all industries. There will be a focus on the life cycle of a typical data scientist project: from scoping the project, to exploratory data analysis, model building, model evaluation and delivery of the project. We will dive into the challenges faced in each component and provide examples of common occurrences when working in this space. (Presentation is attached.)
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