Select the three correct statements about the Evaluation stage of the data science methodology. 1 pointModel Evaluation includes validating that the model is designed as intendedEvaluating the data model includes ensuring that the model processes the data s as intendedModel Evaluation always includes statistical significance testingModel Evaluation ensures the data are correctly handled and interpret

Which statement best describes the Modeling Stage of the data science methodology?1 pointModeling always uses training and test setsThe Modeling stage is followed by the Analytic Approach stageModeling always requires testing multiple algorithms and parametersModeling is always based on predictive model

What is the primary purpose of model evaluation in the data science methodology?1 pointTo refine the data collection processTo determine the optimal parameter values for model buildingTo deploy the model and monitor its performanceTo assess the quality of the model and ensure it meets the initial request

Question 2Which of the following statements represents the essential characteristics of the data science methodology?1 pointData Science Methodology is a highly iterative process. At any point in the methodology, data scientists can decide to repeat a stage or revisit a prior stage and work forward from that previous stage.Data Science Methodology stages and work ends immediately ends when the data scientist deploys the model.Data Science Methodology has no endpoint because data collection occurs before identifying the data requirements.Data Science Methodology stages immediately end after data scientists deploy the code because no feedback is required

Question 1Fill in the blank: The first stage of the data science methodology is ___________________.1 pointModelingData CollectionBusiness UnderstandingData Understandin

Select the correct statements that describe what data scientists do during the Data Preparation stage.1 pointDuring the Data Preparation stage, data scientists determine the timing of eventsDuring the Data Preparation stage, data scientists define the variables to be used in the model.During the Data Preparation stage, data scientists separate data and data sources.During the Data Preparation stage, data scientists ignore missing data