EVALUATE THE ‘DESIGN SPACE’ OF THE DATASET – AS THE MODELS DEVELOPED CAN ONLY WORK IN THE ‘DESIGN SPACE’ COVERED BY THE DATA
Answer the following questions in depth with a paragraph to two paragraphs each, IN DEPTH AND WITH NO ERRORS PLEASE
May 20, 2021You are a Nurse Manager of a health care facility. You are advised to cut cost by not hiring new nurses. How would you encourage the nurses you currently have to work smarter to provide safe and efficient patient care without feeling burnt out?
May 20, 2021EVALUATE THE ‘DESIGN SPACE’ OF THE DATASET – AS THE MODELS DEVELOPED CAN ONLY WORK IN THE ‘DESIGN SPACE’ COVERED BY THE DATA
EVALUATE THE ‘DESIGN SPACE’ OF THE DATASET – AS THE MODELS DEVELOPED CAN ONLY WORK IN THE ‘DESIGN SPACE’ COVERED BY THE DATA
Due to advances in information technology and high performance computing, and an expansion in the number of sensors integrated into manufacturing systems and products in the world, very large data sets are becoming available to engineers; often in real-time form. The rate of production of such data far outstrips our ability to analyze them manually. For example, a computational simulation can generate terabytes of data within a few hours, whereas human analysts may take several weeks to analyze these data sets. Other examples include several digital sky surveys, and data sets from the fields of medical imaging, bioinformatics, and remote sensing. As a result, there is an increasing interest in various scientific communities to explore the use of emerging data mining techniques for the analysis of these large data sets.
Data mining is the semi-automatic discovery of patterns, associations, changes, anomalies, and statistically significant structures and events in data. Traditional data analysis is assumption driven as a hypothesis is formed and validated against the data. Data mining, in contrast, is discovery driven as the patterns are automatically extracted from data.
Goals
Your task in this project is to leverage existing datasets provided by Dr.s Hallinan or Reissman or datasets emerging form any other source to create data-mining models to derive useful information about the data. In the process, you will (or may):
• Gain experience in managing large or pretty large datasets
• Evaluate the ‘design space’ of the dataset – as the models developed can only work in the ‘design space’ covered by the data
• Eliminate outliers in the dataset, e.g., points that don’t well represent the normal data points however that is defined
• Identify the input factors (predictors) that contribute most strongly to the prediction of target factors (response)
• Develop data-mining based models that enable prediction of the target factors, working to minimize the error between the predicted and actual target factors.
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