ricklentz · October 30, 2017 15:36
diff --git a/actionable_analytics.txt b/actionable_analytics.txt
 How does the concept of “actionable analytics” fit with what has been presented so far in this course? 

 According to Mike Wu, actionable analytics requires knowing the predictive power of the model, the input data, and the accuracy requirement of the prediction. Once these are model attributes are identified, data scientists can form and present meaningful comparisons between courses of action.  Using the model's prediction capability, the analyst generates and offers multiple forward-looking action paths for leadership.  

 This process fits perfectly with what we have presented in this course so far.  We have used linear programming and constraint programming to translate business problems into models.  We have dealt with uncertainty through the use of probabilistic models with input distributions.  During the next part of our course, we will deal with increasingly real-world issues (dynamic programming, game theory, metaheuristics, and simulation).
	How does the concept of “actionable analytics” fit with what has been presented so far in this course?

	According to Mike Wu, actionable analytics requires knowing the predictive power of the model, the input data, and the accuracy requirement of the prediction. Once these are model attributes are identified, data scientists can form and present meaningful comparisons between courses of action. Using the model's prediction capability, the analyst generates and offers multiple forward-looking action paths for leadership.

	This process fits perfectly with what we have presented in this course so far. We have used linear programming and constraint programming to translate business problems into models. We have dealt with uncertainty through the use of probabilistic models with input distributions. During the next part of our course, we will deal with increasingly real-world issues (dynamic programming, game theory, metaheuristics, and simulation).