Data Science Experience (DSX)
Data Science Experience (DSX) is a platform that hosts a number of open source tools including Jupyter Notebooks (to Create and collaborate on Python, R, and Scala notebooks); R Studio; and Machine Learning.
DSX contains code, data and visualizations.
Within the portal: https://apsportal.ibm.com/analytics several notebooks, tutorials and test data sets apply to Decision Optimization use cases. Search for “decision optimization” Examples where decision optimization modeling can help include automating complex decisions and trade-offs to better manage limited resources; Taking advantage of a future opportunity or mitigating a future risk; Proactively updating recommendations based on changing events and Meeting operational goals, increasing customer loyalty, preventing threats and fraud, and optimizing business processes
a) Model a Golomb Ruler: This Python notebook shows you how to set up a decision optimization engine and create a constraint programming model that calculates and outputs a Golomb ruler. In decision optimization, actions are recommended based on the outcomes you desire, taking into account specific scenarios, resources, and knowledge of past and current events. Analyzing data to predict future outcomes and suggesting the optimal way to handle future outcomes. Decision support.
b) Store Location Decision Optimization: Tutorial includes everything you need to set up IBM Decision Optimization CPLEX Modeling for Python (DOcplex), build a Mathematical Programming model, and get its solution by solving the model on the cloud with IBM ILOG CPLEX Optimizer. When you finish this tutorial, you'll have a foundational knowledge of Prescriptive Analytics.
c) House Builder Worker Scores: Tutorial includes everything you need to set up decision optimization engines and build constraint programming models to help solve the problem of building five houses in different locations.
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Created: July 25, 2014Englishfrançais