DAT 520 Final Project Guidelines and Rubric.
Overview You must complete a decision analysis research project as your final project for this course. Your research project will focus on a real-world topic of your choice, as approved by your instructor. You will pick a topic from the list provided or with approval from your instructor, and create a data analysis plan and decision tree model based on a real-world scenario. This assessment will provide you with the opportunity to employ highly valued decision support skills and concepts for data within a real-world context. You can use the Final Project Notes document, found in the Assignment Guidelines and Rubrics section of the course.
The project is divided into three milestones, which will be submitted at various points throughout the course to scaffold learning and ensure quality final submissions. These milestones will be submitted in Modules Two, Five, and Seven. The final submission will occur in Module Nine.
This project will address the following course outcomes:
Appraise data in context according to industry-standard methods and techniques for its utility in supporting decision making Determine suitable data manipulation and modeling methods for decision support Articulate data frameworks for organizational decision support by applying data manipulation, modeling, and management concepts Evaluate the ethical issues surrounding organizational use of decision-oriented data based on industry standards and one’s personal ethical criteria Create and assess the agility of solutions through application of data-mining procedures for decision support in various industries
Prompt Your decision analysis model and report should answer the following prompt: How does your model and evaluation resolve uncertainty in making a decision? In order to produce your analytic report, you will need to choose and investigate a data set using the decision analysis techniques you learned in class. Then you will formulate a research question, write an analytic plan, and implement it. Your report should not solely consist of descriptions of what you did. It should also contain detailed explorations into the meaning behind your model and the implications of its results. You will also be testing your model’s fitness and evaluating its strengths and weaknesses.
The project in a nutshell: 1. Choose a data set (get ideas from the source list in the spreadsheet Final Project Topics and Sources.xls) 2. Formulate your decision analysis research question 3. Write an analytic plan 4. Perform the top-down or bottom-up modeling 5. Perform model diagnostics 6. Evaluate
These activities are broken up into milestones so that the work is spread throughout the term and you can get early assistance with any obstacles.
A decision analysis report is similar to any other analytic report. These reports introduce a problem, state a line of inquiry, explain a model that the author developed, discuss results and limitations, and then make conclusions and recommendations. Some decision models seek the best expected value among a discrete set of choices. Other decision analyses might seek the threshold values at which the model changes from one recommendation to another, describe the implications, and leave it to the reader to decide what to do. Still other decision models might look for the likeliest path to explain patterns that are already present in a data set. In all cases, they have something in common: They are trying to help resolve uncertainty. Your job is to bring clarity to the decision being made.
Decision analysis seeks less to produce a definitive result, and more to accurately explain the combinations of possibilities that can lead decision makers to clearer choices. This is the modeling aspect. If you model the weather but never take into account barometric pressure, your model would fail if trying to determine the worst hurricane trajectories. These are the kinds of things you will be looking at in your decision models: searching for ways to explain the conditions that produce outcomes and to evaluate the strengths and weaknesses of the models you produce.
The three main ideas that your report should encompass are your ability to formulate a decision analysis research question based on an appropriate data set, develop your model, and finally evaluate the model’s utilities, results, strengths, and weaknesses. In short, if your report fully encompasses these three concepts, you will produce an authentic document that would stand on its own in a professional setting.
Data sources to choose from: The included spreadsheet lists data sets used in previous sessions of DAT 520. Students found these data sets, prepared them for modeling on their own, and wrote excellent papers on the topics. Remember that your data set needs to be appropriate for modeling a discrete set of choices. Either those choices are built into the model as categorical variables, or you will need to do some legwork by converting continuous variables into rational categorical groups. This activity would be part of the data preparation and documented in your data appraisal section.
Your final project must include the following sections:
Limitations: Up to two pages Conclusion: Up to two pages
Sources: Note that the core elements add up to about 15–20 pages, double-spaced. The overall target for the core elements is still 15–20 pages, so that you have room to adjust each section according to the needs of the project. Everything you need to say in the report should fit within 15–20 double-spaced, 12-point font pages with one-inch margins.
To see some good final projects, consult the exemplars. Not all of them are 100% perfect papers, but they do embody the level of complex thinking that characterizes an interesting project. The idea behind the page limit is to explore the concept of “less is more.” If you add up the text, graphics, sources, and supporting material from all the milestones, you end up with 15 to 20 pages. For the final, that means some compression needs to occur. This means finding the most important information from what you have previously written and leaving room for the new parts that you need to write. Follow the list of required elements for the final to guide how to structure your research paper.
Specifically, the following critical elements must be met in your final submission:
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 indepth 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?
5. 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.
To guide you in writing your final paper, follow the Final Project Rubric. The rubric is less about format and more about thought. Specifically, you should write sections that detail the limitations and justification for your analysis. You should also take the time to address any ethical or legal issues that connect with your results or decisions being analyzed. You should annotate and caption your graphics. You could include a table that characterizes the data set. You should address what your model does to assist decision makers. You should defend your choices of variables and groupings. Lastly, you should address the agility of your analysis and how it might be applied to future uses.
Milestones Milestone One: Choose a Data Set and Formulate Decision Analysis Research Question In Module Two, you will choose a data set from the curated list of sources (Final Project Topics and Sources.xls), or you may submit your proposal for a different data source than those listed. Then you will write a decision analysis research question, which should be two to three pages in length and framed as a discrete set of choices to be analyzed. This milestone is graded with the Milestone One Rubric.
Milestone Two: Develop Decision Analysis Model In Module Five, you will draft your decision tree. This task presupposes a data set, a viable decision analysis research question, and the necessary data prep. To complete this milestone, you may have to experiment with different modeling styles. The main objective is to draft your model, explain what you did, and explain why it is the best model for your research question. This milestone is graded with the Milestone Two Rubric.
Milestone Three: Revise and Evaluate Decision Analysis Model In Module Seven, you will revise and evaluate your decision model based on the feedback you received from the instructor for the previous milestone. Evaluation in this case could mean a few different things. If you are performing a bottom-up style recursive partitioning analysis, you should report on the error rate and variable selection. You might also consider alternative variable categorizations to improve your model. If you are performing a top-down decision tree modeling exercise, what are the threshold values that cause the tree to flip? You should perform sensitivity analysis on the critical variables in your tree and report what those sensitivity analyses are telling you. For either style of modeling, what makes your tree stronger? What breaks the model? This milestone is graded with the Milestone Three Rubric.
Final Submission: Decision Analysis Model and Report In Module Nine, you will submit your decision analysis model and report, compiling all the components used to develop the model and produce the report, as well as a leading abstract, table of contents, and in a format that addresses all of the critical elements in the instructions. The project should include sections that detail the limitations and justification for your analysis. You will probably be compressing what you wrote for your introduction to make it fit within the eight-page limit. You should also take the time to address any ethical or legal issues that connect with your results or decisions being analyzed. Lastly, you should address the agility of your analysis and how it might be applied to future uses. This assignment is graded with the Final Project Rubric.
Milestone Deliverables Module Due Grading
One Research Question Two Graded separately; Milestone One Rubric Two Develop Decision Analysis Model Five Graded separately; Milestone Two Rubric Three Revise and Evaluate Model Seven Graded separately; Milestone Three Rubric Decision Analysis Model and Report Nine Graded separately; Final Project Rubric.
Final Project Rubric Guidelines for Submission: The final report will be a 15–20 page research paper, double-spaced, in 12-point Times New Roman font with one-inch margins all around and APA citations. Title page, abstract, appendices and bibliography of sources are extra beyond the 15–20 pages of the report. You may include one page or less of annotated/captioned graphics as part of the report. The purpose of the limits is to keep the discussions compact and to maintain the integrity of publication-quality research.
Critical Elements Exemplary (100%) Proficient (90%) Needs Improvement (70%) Not Evident (0%) Value Introduction Meets “Proficient” criteria and cites specific, relevant examples to establish a robust context for the data-mining analysis plan The purpose, type, intended populations, and uses of the analysis report are analyzed to establish an appropriate context for the data-mining analysis plan The purpose, type, intended populations, and uses of the analysis report are not sufficiently analyzed to establish an appropriate context for the data-mining analysis plan Either the purpose, type, intended populations, or uses of the analysis report are not analyzed 6.25 Data Appraisal: Characterize Meets “Proficient” criteria and claims are qualified with source evidence or examples Makes accurate claims about the general use of the dataset(s) and the intended purpose of the data Not all claims about the general use of the dataset(s) and the intended purpose of the data is accurate given the available evidence Does not make claims about the general use of the dataset(s) and the intended purpose of the data 6.25 Data Appraisal: Context Meets “Proficient” criteria and qualifies claims specific to discrete needs of the organization Makes accurate claims about the data within industry standards and the context of the problem to be solved Not all claims about the data are accurate based on industry standards and the context of the problem to be solved Does not make claims about the data based on the context of the problem to be solved and industry standards 6.25 Data Appraisal: Measurable Utilities Meets “Proficient” criteria and supporting explanation is qualified with examples or research evidence Makes accurate determination and thoroughly explains the measurable utilities and how the data supports that choice Determination of unit of analysis is not entirely accurate or explanation does not thoroughly explain how the data supports measurable utilities determination Does not determine a measurable utilities 6.25 Select Appropriate Techniques: Preparation Meets “Proficient” criteria and quality of explanation allows for a seamless delivery of the initial molding process Makes appropriate analysis step selections and explains the process for preparing the raw data Not all analysis step selections are appropriate for preparing the raw data, or not all step processes are sufficiently explained Does not select and explain analysis steps for preparing raw data into a useable form 6.25
Select Appropriate Techniques: Manipulation
Meets “Proficient” criteria and step selection and explanations are seamlessly integrated into a clear process
Makes appropriate step selections and explains the process of steps for in-depth analysis and manipulation of the data to support organizational decision making
Not all steps are appropriate for in-depth analysis and manipulation in support of organizational decision making or not all steps are explained in terms of process
Does not select and explain indepth analysis and manipulation steps for decision support
Select Appropriate Techniques: Checkpoints
Meets “Proficient” criteria and the explanations of the selections provide clear and seamless integration of steps into the overall manipulation process
Makes appropriate algorithm selections, and explains the process of the selections, for the optimization, risk assessment, and built-in check points to ensure the success of data analysis and manipulation
Not all algorithm selections and explanations of process for optimization, risk assessment, and built-in check points are appropriate to ensure successful data analysis and manipulation, or key valuable methods are missed
Does not select and explain the process of algorithm selections for optimization, risk assessment, and built-in checkpoints
Select Appropriate Techniques: Defend
Meets “Proficient” criteria and substantiates claims with scholarly research evidencing considerations of social responsibility
Makes and justifies claims about the ethical and legal issues related to the use, interpretation, and manipulation of the data for the decisions being made, based on industry standards, laws, and organizational policies
Not all claims about the ethical and legal issues related to the use, interpretation, and manipulation of the data for the decisions being made are justifiable based on industry standards, laws, and organizational policies
Does not make claims about the ethical and legal issues related to the use, interpretation, and manipulation of the data for the decisions being made
Defend and Evaluate Choices: Best
Meets “Proficient” criteria and substantiates claims with research in specific support of the decisions/problem at hand
Makes and justifies claims about the appropriateness of the methods for manipulation and algorithm selections made for decision support based on analysis of industry standards and valid research
Not all claims about the appropriateness of the methods for manipulation and algorithm selections made are justifiable based on analysis of industry standards and valid research
Does not make and justify claims about the appropriateness of the methods for manipulation and algorithm selections made
Defend and Evaluate Choices: Agility
Meets “Proficient” criteria and substantiates claims with scholarly research and real world examples
Makes and justifies claims about the agility of the choices made for decision support in various industries, projects, and organizations with research and relevant examples
Not all claims about the agility of the choices made for decision support in various industries, projects, and organizations are justifiable based on the provided research and examples
Does not make claims about the agility of the choices made for decision support in various industries, projects, and organizations
Defend and Evaluate Choices: Address Issues
Meets “Proficient” criteria and the details of the explanation expound upon social responsibility and industry standards
Details the ethical considerations that should be made about use of the results of the solution and how ethical use can be ensured
Explains ethical considerations for use of the results of the solution, but lacks detail or does not explain how ethical use can be ensured
Does not explain ethical considerations for use of solution results
Decision Tree Model: Implement
Meets “Proficient” criteria and performance of mining process and accuracy of decision solution evidence appropriate planning and implementation of plan within the context of the selected topic
Correctly performs the data mining process and creates an accurate decision support solution
Performs the data mining process and creates a decision support solution, but solution is not accurate
Does not perform the data mining process and create a decision support solution
Decision Tree Model: Structure
Meets “Proficient” criteria and substantiates claims with scholarly evidence and real world examples
Makes and justifies claims about the overall structure and purpose of model for organizational decision support based on specific examples and research
Not all claims about the overall structure and purpose of model for decisions support are justifiable
Does not make claims about the overall structure and purpose of model for organizational decision support
Decision Tree Model: Documentation
Meets “Proficient” criteria and model is of quality to allow others to develop further, more detailed models to address possible issues
Outline effectively acts as process documentation for addressing potential complications during implementation of the analysis plan
Not all aspects of outline would be effective in addressing the potential complications of implementation, or common major issues are not addressed
Does not include an outline for addressing potential complications during implementation
Decision Tree Model: Results
Meets “Proficient” criteria and comprehensively evaluates against criteria above the given criteria and specifically relevant to the context of the selected topic
Accurately evaluates the results of the decision tree model against the given criteria
Evaluates the results against the given criteria, but with gaps in accuracy
Does not evaluate the results against the given criteria
Articulation of Response
Submission is free of errors related to citations, grammar, spelling, syntax, and organization and is presented in a professional and easy to read format
Submission utilizes visualization options that effectively address the needs of the audience and has no major errors related to citations, grammar, spelling, syntax, or organization
Submission utilizes various visualization options that don’t effectively address the needs of the audience or has major errors related to citations, grammar, spelling, syntax, or organization that negatively impact readability and articulation of main ideas
Submission does not utilize visualization options for the audience or has critical errors related to citations, grammar, spelling, syntax, or organization that prevent understanding of ideas
Earned Total 100%