Determine the appropriate steps (including: risk assessment, probability calculations, and modeling techniques) for data manipulation and in depth analysis to support organizational decision-making.

I. Introduction: Analyze the purpose, type, intended populations, and uses of the analysis to establish an appropriate context for the data-mining and analysis plan. II. Data Appraisal

A. Characterize the data set. For example, what is the purpose such data are generally used for? B. Appraise the data within the context of the problem to be solved and industry standards. How will you use the data? For example, expound upon the limitations of the data set in the context of your needs.

C. Explain the utilities that you will be using and how the data supports that choice. III. Select Appropriate Techniques

A. Determine and explain the appropriate steps for preparation of the data sets into a usable form: what steps were taken to make data descriptions clear, how extreme or missing values were addressed, and how data quality was improved.

B. Determine the appropriate steps (including: risk assessment, probability calculations, and modeling techniques) for data manipulation and in depth analysis to support organizational decision-making.

C. Models and checkpoints: How will you optimize the models, what will you test for, and how will you build in checks to determine a successful analysis?

D. Defend the ethicality and legality of the analytic selections made for use, interpretation, and manipulation of the data based on industry standards for legal compliance, policies, and social responsibility. If there are no potential ethical and legal compliance issues, explain how your prep and use of this data are both ethical and legal. IV. Defend and Evaluate Choices

A. Why are these choices the best for the data and problem at hand? What research or industry standards are supportive of your choices of methods? Explain how the methods chosen will support organizational decision-making.
B. Determine the agility of these choices for decision support based on research and relevant examples: how can they be adapted to alternative needs or reapplied to future analysis?

C. Address ethical and legal issues that might arise from the use and interpretation of the data, based on industry standards, policies, and social responsibility. How can you ensure that your selected procedures, use of data, and results will be socially responsible and in line with your own ethical standards?

D. Implement your plan: Perform data preparation, mining and modeling procedures, and create your decision support solution. V. Decision Tree Model (bottom-up, top-down): Include the detailed process and programming steps necessary to complete the analysis. Be sure to:

A. Defend the overall structure and purpose of the tree model in organizational decision support.

B. Develop process-documentation that addresses potential complications. This piece should resemble a recipe/outline that provides enough information for addressing potential implementation issues.

C. Evaluate the results of your decision tree model. At minimum, attend to the following: 1. Are the results reasonable? 2. How accurate is your model?

3. Are there missing or extraneous elements that could have influenced your results?

4. What common errors are made during creation of the model you chose? How did you ensure that you did not make these errors? VI. Articulation of Response/Final Report: Utilizes visualization options that effectively address the needs of the audience. Options may include annotated shell tables, visualizations, and a compositional structure.