Computer Science homework help. 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:  Assess the applicability and usability of fundamental data mining methods and tools for given environments  Design and develop simple data mining analysis plans for general business contexts  Analyze data for meaningful patterns in conjunction with relevant research questions  Generate well-formulated research questions by utilizing appropriate data mining and research techniques  Determine basic types of reporting for communicating the methods and results of data research
 Identify and address possible types and sources of error through analysis of data mining research
Prompt
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 wasinitially 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:
I. Background and Introduction
Critical Elements
a) What is the overall business problem you are trying to solve?
b) What is the purpose of the analytic method/approach/strategy you are using? What type of information does it yield?
II. Tools and Visualizations
a) What data mining tools will you use to perform the analysis? Why these particular ones?
b) What data visualizations will you use in your report, and why?
III. Specific Research Question
a) 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?
b) How will you determine if your research question was answered or if your hypothesis-generation was successful? How will you measure
progress?
c) What are cogent follow-up questions or explorations that should follow from your initial research?
d) 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?
Analysis:
I. To what extent does your analysis reflect an organized, stepwise approach? What aspects are beyond your control?
II. 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.
III. What meaningful patterns have you discovered? What additional research questions do these patterns indicate?
IV. 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.
V. Based on the results, what suitable alternative analytic methods exist, and why?
Final Report:
I. Display and interpret your results. This is your opportunity to follow through with your plan and actually create the reports necessary.
II. Address the validity, reliability, and limitations of your report. To what extent has the reliability and validity of the analysis been demonstrated for
varying datasets (generalizability) in order to prevent error during research and presentation?
III. 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)?
IV. Evaluate the style and visualizations you have selected now that you have seen the report output. How well were the results presented?
V. What are the next steps to address further lines of inquiry? Possible new hypotheses?
Milestone One
Milestones
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?
Introduction: Analytic Method
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
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?
Research Question
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?
Research and Support
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
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?
Sources of Error
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?
Inaccurate Depictions of Data
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.
Alternative Analytic Methods
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
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. Validity, Reliability, Limitations
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?
Resulting Decision Influence
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?
Next Steps
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).
Deliverables
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
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
articulatesrelevant business
problem
Articulatesthe 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 describesthe
purpose and information yield
of the data mining methods and
tools for the given use case
Describesthe 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 relatesthe relative
strengths and weaknesses of
data visualizationsto the
original problem
Determines and defends
visualization selectionsfor
communicating results of data
analysisin terms of strengths
and weaknesses
Determines and explains
visualization selectionsfor
communicating results of data
analysis, but not in terms of
strengths and weaknesses
Does not determine and explain
visualization selectionsfor
communicating results of data
analysis
5
Research Question Meets “Proficient” criteria and
articulations and relations are
valid, clear, and concise
Articulatesresearch question(s)
with applicability for
determining meaningful
patternsin data mining
Articulatesresearch
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 explainsthe 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)
Assessesthe research
approach, but not in terms of
successful measurability and
answerability of the research
question(s)
Does not assessthe research
approach for successful
attainment of the research
question(s)
5
Follow-Up Questions Meets “Proficient” criteria and
articulates creative but relevant
additional questionsfor further
research
Generates well-formulated
additional research questions
based on utilized data mining
and research techniques
Generates additional research
questionsfor 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
techniquesto evaluate sources
of related and supporting
research
Utilizesresearch techniquesto
examine sources of related
research, but does not evaluate
the sourcesfor ability to
support claims
Does not utilize research
techniquesto 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
Analysesthe 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
explanationsinclude context
around data mining and its
inherent dangers
Identifies possible types and
sources of error and accurately
explainsremedies
Explainssources 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
patternslends 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
resultsto research questions
8
Inaccurate
Depictions of Data
Meets “Proficient” criteria, and
methodsfor fixing are aligned
to the need in terms of
complexity of problem
Correctly resolves and
accurately explains methodsfor
fixing inaccurate depictions of
data
Correctly resolvesinaccurate
depictions of data, but
explanation of methodsfor
fixing is not accurate
Does not resolve and explain
methodsfor fixing inaccurate
depictions
5
Alternative Analytic
Methods
Meets “Proficient” criteria, and
discussion of alternative
analytic methods and tools
includesinnovative/creative
applicationsto 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 interpretsresults
in an organized, visually
pleasing, and creative or
unconventionalmanner
Creates displaysthat 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
relatesthe research and
decision-making
recommendationsto a real- world client in a maximally
clear, concise, and tangible
level
Relatesthe research and
decision-making
recommendationsto a real- world client
Inadequately relates the
research and decision-making
recommendationsto a real- world client
Does not relate the research
and decision-making
recommendationsto a real- world client
5
Visual Evaluation Meets “Proficient” criteria and
integrates audience-specific
design into evaluation
Evaluatesthe final
visualizations and style
selection for success in
communicating results and
intended message
Evaluatesthe final
visualizations and style
selection, but not in terms of
the successin communicating
results and intended message
Does not evaluate the final
visualizations and style
selection
5
Next Steps Meets “Proficient” criteria and
determinessteps that reach
beyond the initial problem to
issues of implementation and
further improvement of
business practice
Logically determines
appropriate next steps to
addressfurther 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 addressfurther 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%

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