Fill This Form To Receive Instant Help
Fill This Form To Receive Instant Help
Homework answers / question archive / DAT 220 Final Project Guidelines and Rubric Overview The assessment for this course is the creation of a recommendation report that is specifically catered to a given audience
DAT 220 Final Project Guidelines and Rubric
Overview
The assessment for this course is the creation of a recommendation report that is specifically catered to a given audience. The purpose of this project is to showcase your understanding of foundational data concepts.
The project includes four milestones, submitted in Modules Two, Three, Six, and Seven. The final project is submitted in Module Eight.
In this assignment, you will demonstrate your mastery of the following course outcomes:
The scenario: Bubba Gump Shrimp Company is a successful retailer of regional food, both in its restaurants and through other retail channels. Bubba Gump began as a small, privately owned restaurant. Thanks to unexpected exposure from a blockbuster movie, Bubba Gump grew rapidly from its humble beginnings and now operates several restaurants, sells branded merchandise through an online retail site, and wholesales its branded merchandise to other retail outlets. Bubba Gump’s growth was initially very rapid in response to a strong demand and high name recognition that followed from its movie exposure. After its first few years of rapid growth, sales increased at slower rates and finally leveled off. Sales have declined in each of the last two years.
Bubba Gump Shrimp Company has collected a large amount of data about its business, including restaurant point-of-sale (POS) data, web channel sales performance, customer information through restaurant loyalty programs, and customer and sales transaction data through its website and retail partners. Bubba Gump’s leadership has decided to commission an analysis of the company’s vast data assets to better understand its customers and look for ways to create new revenue growth.
You have been assigned to plan, conduct, and report on this data mining initiative for Bubba Gump Shrimp Company. The company data that is available to you includes Bubba Gump’s restaurant point-of-sale (cash register, credit card) data, its customer database (collected from its restaurant loyalty program and online sales channel), its web store sales transaction data, and customer and sales data from third-party retailers.
All of Bubba Gump’s data has recently been integrated in a data warehouse. That enterprise data warehouse was built specifically to support data mining initiatives like the one you have been assigned to conduct, by consolidating data from multiple operations and channels in one place and integrating the data across sources for a complete view of the customer experience. For the first time, Bubba Gump analysts can link sales transactions to specific customers at specific restaurants, for example. It also means that you can link customer transactions across channels; that is, for any given customer, you can link to both their restaurant purchases, their online purchases, and (in some cases) their purchases from third-party retail partners.
You have been selected to develop and execute the data mining analysis plan for Bubba Gump’s customer analysis project. Your project will be the first major data mining project conducted against the new Bubba Gump data warehouse. Because Bubba Gump’s data was not previously integrated in a single data warehouse, company leadership has never been able to analyze its customers across their complete experience. In other words, customer restaurant purchases, online purchases, and third-party retailer purchases could not be analyzed together previously; each channel had to be analyzed separately.
As a first step, a sample of 500 customers has been selected from the analytics data warehouse and given a survey in exchange for purchase credits at one of
Bubba Gump’s sales channels. The survey sample was selected from the universe of customers who have made purchases from at least one Bubba Gump outlet (restaurant, web store, etc.). Responses to various customer satisfaction questions were recorded, and historical purchase information has been extracted from the data warehouse for each customer in the sample.
Your task is to analyze the survey responses to understand whether there are natural “clusters” within Bubba Gump’s customer population. You are then to create a visualization of this survey data that describes Bubba Gump’s customers across any dimensions that define those subgroups.
Final Written Report (Due in Module Eight): Given the above scenario, develop a plan for data analysis that will allow you to address the question and report your results. Your final submission will include three sections: your plan for analysis, your actual analysis with annotations explaining your steps, and your final report serving as your interpretation of results. The following critical elements highlight the necessary performances and questions you will need to address for each section of your final deliverable:
Plan for Analysis:
Analysis:
Final Report:
Milestone One will be completed in Module Two of the course and will serve as an introduction to the final project. Based on the overview provided, articulate a concise business question from the scenario presented above for which data mining can be used to provide insights. The following critical elements are addressed:
Introduction: Business Problem
What is the overall business problem you are trying to solve?
What is the purpose of the analytic method/approach/strategy you are using? What type of information does it yield?
This milestone is graded with the Final Project Milestone One Rubric.
Milestone Two will be completed in Module Three of the course and is centered on tools and visualizations. Describe a simple sampling strategy that might be used to address the business question from a subset of the customer population. What type of information do you need to address the question? In your response, address the following critical elements:
Analysis Tools
What data mining tools will you use to perform the analysis? Why these particular ones?
Data Visualizations
What data visualizations will you use in your report, and why?
What is the specific research question that needs to be addressed? What research question will you work from in order to analyze the given data for meaningful patterns?
Research Measurement
How will you determine if your research question was answered or if your hypothesis-generation was successful? How will you measure progress?
Follow-Up Questions
What are cogent follow-up questions or explorations that should follow from your initial research?
Are there any published sources or other resources that address your line of inquiry? Where do they fall short? How will they help guide your analysis?
This milestone is graded with the Final Project Milestone Two Rubric.
Milestone Three will be completed in Module Six of the course. Building off of the work in Milestone Two and the exercises in the course, we will now work on preparing the analytics report.
For each data mining activity (cluster analysis, linear regression, logistic regression), provide a description of the use of the data mining technique against the sample survey data. In that description, include an explanation of the applicability of each technique toward solving the business problem, results generated by the technique, applicability of those results to the problem, and potential limitations of the method with regard to solving the business problem. Be sure to address the following critical elements in your response:
Analysis Organization
To what extent does your analysis reflect an organized, stepwise approach? What aspects are beyond your control?
In performing the analysis, what sources of error in the raw data did you need to resolve before a successful run? Describe what happened and how you fixed things.
Meaningful Patterns
What meaningful patterns have you discovered? What additional research questions do these patterns indicate?
In using the data mining tools, did you encounter inaccurate depictions of data that you needed to resolve? Explain these depictions and how you addressed the issues.
Based on the results, what suitable alternative analytic methods exist, and why?
This milestone is graded with the Final Project Milestone Three Rubric.
Milestone Four will be completed in Module Seven of the course. Prepare a report that describes the customer survey exercise, the results, and the implications for Bubba Gump’s interest in increasing sales through its web channel. Include the following critical elements:
Display and Interpretation
Display and interpret your results. This is your opportunity to follow through with your plan and actually create the reports necessary.
Address the validity, reliability, and limitations of your report. To what extent have the reliability and validity of the analysis been demonstrated for varying data sets (generalizability) in order to prevent error during research and presentation?
How would you facilitate a potential client or superior in your organization to make decisions resulting from this assessment (e.g., pairing results with other kinds of information)?
Visual Evaluation
Evaluate the style and visualizations you have selected now that you have seen the report output. How well were the results presented?
What are the next steps to address further lines of inquiry? Possible new hypotheses?
This milestone is graded with the Final Project Milestone Four Rubric.
Final Submission:
Submit your final report in Module Eight. Be sure to write your report in a way that clearly communicates the intent to the given audience (the company’s partners) and affectively portrays your recommendations.
The final project is graded with the Final Project Rubric (see below for the Final Project Rubric).
|
Milestone |
Deliverables |
Module Due |
Grading |
|
1 |
Introduction |
Two |
Graded separately; Final Project Milestone One Rubric |
|
2 |
Tools and Visualizations |
Three |
Graded separately; Final Project Milestone Two Rubric |
|
3 |
Analytics Report |
Six |
Graded separately; Final Project Milestone Three Rubric |
|
4 |
Customer Survey Exercise Report |
Seven |
Graded separately; Final Project Milestone Four Rubric |
|
|
Final Report |
Eight |
Graded separately; Final Project Rubric |
|
Critical Elements |
Exemplary (100%) |
Proficient (85%) |
Needs Improvement (55%) |
Not Evident (0%) |
Value |
|
Introduction: Business Problem |
Meets “Proficient” criteria with relevant context into the connection to the overall business goals |
Accurately and clearly articulates relevant business problem |
Articulates the business problem, but may be inaccurate or unclear |
Does not articulate the business problem |
4 |
|
Introduction: Analytic Method |
Meets “Proficient” criteria, and explanation includes cogent, relevant details to illustrate the relevance of the method to the business problem |
Accurately describes the purpose and information yield of the data mining methods and tools for the given use case |
Describes the purpose and information yield of the chosen analytic method, but explanation is not entirely accurate or explanation is lacking necessary detail |
Does not describe the purpose and information yield of the chosen analytic method |
5 |
|
Analysis Tools |
Meets “Proficient” criteria, and defense shows detailed analysis of the available tools and resources |
Selects and defends choice of data mining tools in terms of the relative strengths and weaknesses of the tools |
Selects and explains choices, but does not defend in terms of the relative strengths and weaknesses of the tools |
Does not select and explain data mining tool choices |
5 |
|
Data Visualizations |
Meets “Proficient” criteria and accurately relates the relative strengths and weaknesses of data visualizations to the original problem |
Determines and defends visualization selections for communicating results of data analysis in terms of strengths and weaknesses |
Determines and explains visualization selections for communicating results of data analysis, but not in terms of strengths and weaknesses |
Does not determine and explain visualization selections for communicating results of data analysis |
5 |
|
Research Question |
Meets “Proficient” criteria and articulations and relations are valid, clear, and concise |
Articulates research question(s) with applicability for determining meaningful patterns in data mining |
Articulates research question(s), but questions are not applicable to how data will be analyzed for meaningful patterns |
Does not articulate research question(s) |
7 |
|
Research Measurement |
Meets “Proficient” criteria and thoroughly explains the criteria needed for answering the research question at a deeper level |
Assesses the research approach in terms of the measurability of the research question(s) |
Assesses the research approach, but not in terms of successful measurability and answerability of the research question(s) |
Does not assess the research approach for successful attainment of the research question(s) |
5 |
|
Follow-Up Questions |
Meets “Proficient” criteria and articulates creative but relevant additional questions for further research |
Generates well-formulated additional research questions based on utilized data mining and research techniques |
Generates additional research questions for further inquiry, but research questions are not well-formulated given the utilized mining and research techniques |
Does not generate additional research questions for further inquiry |
6 |
|
Research and Support |
Meets “Proficient” criteria, and use of research is valid for the given environment |
Utilizes appropriate research techniques to evaluate sources of related and supporting research |
Utilizes research techniques to examine sources of related research, but does not evaluate the sources for ability to support claims |
Does not utilize research techniques to examine sources of related research |
5 |
|
Analysis Organization |
Meets “Proficient” criteria, and assessment evidences a high degree of specificity around the limitations of the approach |
Assesses the organization of the analysis plan for stepwise approach and uncontrollable aspects |
Analyses the organization of the analysis plan, but does not assess in terms of adherence to a stepwise approach and possible uncontrollable aspects |
Does not analyze the organization of the analysis plan |
5 |
|
Sources of Error |
Meets “Proficient” criteria, and explanations include context around data mining and its inherent dangers |
Identifies possible types and sources of error and accurately explains remedies |
Explains sources of error and their remedies, but explanations have gaps in accuracy or logical identification |
Does not identify and explain possible types and sources of error and remedies |
5 |
|
Meaningful Patterns |
Meets “Proficient” criteria, and identification of meaningful patterns lends to further research questions/accurately indicates possible new directions |
Analyzes data for meaningful patterns and accurately relates the results to the original research questions |
Analyzes data for meaningful patterns, but there are gaps in accuracy or the relationships between the results and the original research questions are inaccurate |
Does not analyze data for meaningful patterns and relate results to research questions |
8 |
|
Inaccurate Depictions of Data |
Meets “Proficient” criteria, and methods for fixing are aligned to the need in terms of complexity of problem |
Correctly resolves and accurately explains methods for fixing inaccurate depictions of data |
Correctly resolves inaccurate depictions of data, but explanation of methods for fixing is not accurate |
Does not resolve and explain methods for fixing inaccurate depictions |
5 |
|
Alternative Analytic Methods |
Meets “Proficient” criteria, and discussion of alternative analytic methods and tools includes innovative/creative applications to the business environment |
Assesses the applicability and usability of alternative data mining methods and tools for the results and the business environment |
Discusses alternative data mining methods and tools, but not in terms of their applicability and usability within the business environment |
Does not discuss alternative data mining methods and tools |
5 |
|
Display and Interpretation |
Meets “Proficient” criteria and displays and interprets results in an organized, visually pleasing, and creative or unconventionalmanner |
Creates displays that accurately interpret data and adhere to plan for presentation |
Displays adhere to plan for presentation, but do not accurately interpret data, OR displays accurately interpret data, but do not adhere to plan for presentation |
Does not display and interpret results |
5 |
|
Validity, Reliability, Limitations |
Meets “Proficient” criteria, and explanation is detailed and uncovers the complexity of determining validity, reliability, generalizability, and limitation issues in reporting |
Explicates validity, reliability, generalizability, and limitations of the report in terms of possible issues during data research and presentation |
Explicates validity, reliability, generalizability, and limitations of the report, but not in terms of possible issues during data research and presentation |
Does not explicate validity, reliability, generalizability and limitations of the report |
5 |
|
Resulting Decision Influence |
Meets “Proficient” criteria and relates the research and decision-making recommendationsto a realworld client in a maximally clear, concise, and tangible level |
Relates the research and decision-making recommendationsto a realworld client |
Inadequately relates the research and decision-making recommendationsto a realworld client |
Does not relate the research and decision-making recommendationsto a realworld client |
5 |
|
Visual Evaluation |
Meets “Proficient” criteria and integrates audience-specific design into evaluation |
Evaluates the final visualizations and style selection for success in communicating results and intended message |
Evaluates the final visualizations and style selection, but not in terms of the success in communicating results and intended message |
Does not evaluate the final visualizations and style selection |
5 |
|
Next Steps |
Meets “Proficient” criteria and determines steps that reach beyond the initial problem to issues of implementation and further improvement of business practice |
Logically determines appropriate next steps to address further lines of inquiry or decision needs within the business context |
Determines next steps for further lines of inquiry and decision needs, but not in logical terms of the business context |
Does not determine next steps to address further lines of inquiry and decision needs |
5 |
|
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 has no major errors related to citations, grammar, spelling, syntax, or organization |
Submission has major errors related to citations, grammar, spelling, syntax, or organization that negatively impact readability and articulation of main ideas |
Submission has critical errors related to citations, grammar, spelling, syntax, or organization that prevent understanding of ideas |
5 |
|
|
|
|
|
Earned Total |
100% |
Already member? Sign In
