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Data Science Methodology From Deployment to Feedback Deployment Case Study : Understand the results Assimilate knowledge for business: (assimilate : 완전히 이해하다, 동화되다, 흡수하다) - Practical understanding of the meaning of model results - Implications of model results for designing intervention actions (implication : 영향, 함축, 암시) Case Study : Gathering application requirements Application requirements: -..

Data Science Methodology From Modeling to Evaluation Modeling - Concepts From Modeling to Evaluation - Modeling : In what way can the data be visualized to get to the answer that is required? - Evaluation : Does the model used really answer the initial question or does it need to be adjusted? Data Modeling : Using Predictive or Descriptive? Data Modeling : Using training/test sets Understand the..

Data Science Methodology From Requirements to Collection Data Requirements From Requirements to Collection - Data Requirements : What are data requirements? - Data Collection : What occurs during data collection? Case Study : Selecting the cohort Define and select cohort: (cohot : 집단) - inpatient within health insurance provider's service area - primary diagnosis of CHF (Congestive Heart Failure..

Data Science Methodology From Problem to Approach Data Science Methodology Overview Addressing data science challenges - data science combines statistics, techonology, and domain expertise to extract insights from vast data - adopting a methodology can help address these issues Challenges: - resolve the problems of misunderstanding of the business questions - not knowing how to apply the data to..