DAT 510 Final Project Guidelines and Rubric

Overview

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The final project for this course is the creation of a data analytics project proposal that addresses an issue or opportunity from your choice of four scenarios
with accompanying data sets in Chapters 5, 8, 9, or 10 in your Data Mining for the Masses resource.

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Within our personal and professional lives, we are constantly creating new data. As we browse websites, interact with our mobile devices, or use various

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business applications and digitized processes (such as radio frequency identification or sensor monitoring), data is being created. These are just a few examples

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of the data that exists. Organizations have recognized the enormous benefit of combining employee intuition with the informed use of data but have not yet

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reached the full potential data analytics has to offer. In fact, many organizations collect vast amounts of data yet do not leverage data analytics to its full
potential, choosing to rely on traditional (manual) analysis. Data analytics builds upon manual analysis, integrating new techniques and tools to meet the needs

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of big data, and the savvy data professional needs to understand and plan for the use of these techniques and tools.

The final project is divided into four milestones, which will be submitted at various points throughout the course to scaffold learning and ensure quality final

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submissions. These milestones will be submitted in Modules Three, Five, Six, and Seven. The final project will be submitted in Module Nine.

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In this assignment, you will demonstrate your mastery of the following course outcomes:

 Evaluate various sources of data for their benefits and limitations to common data analytics initiatives and the resulting implications for data collection

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and security
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Apply common and repeatable data analytics life cycles to business contexts to predict solution performance, quality, and security
Assess data analytics tools for their value and applicability to business initiatives, goals, and available data
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 Determine how data analytics can be successfully applied to various types and forms of business data for deriving actionable insights based on analysis
of techniques, tools, and methodologies
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 Communicate analytics insights effectively through the appropriate application of data visualization and presentation techniques

The final project will require you to explore the world of data analytics within business contexts by proposing solutions to existing issues or identifying new
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opportunities to better leverage data analytics. You will place yourself in the role of a data analytics professional, to develop a data analytics project proposal.
Remember that your goal is not to solve all of the organization’s problems but instead to recommend a project or initiative that you feel can provide a benefit to
the organization through the use of analytics.
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Your data analytics project proposal can be submitted in the form of a report or visual presentation (such as PowerPoint) with audio or speaker notes. You will
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select the delivery method you feel will work for your audience (in this case, the upper-level management of your organization) in Module One, and you will
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integrate the communication, presentation, and visualization strategies that will best facilitate their understanding and acceptance of your proposal. Please note
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 that your instructor will need to be able to view your submission, so it is important to gain instructor approval of your final project delivery method prior to
beginning your project.

The PDF version of Data Mining for the Masses can be downloaded here. Chapters 5, 8, 9, and 10 cover some of the models covered in this course. You may use
the scenarios and data sets provided for any of those chapters for your final project. The data sets can be downloaded here. These scenarios and the

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accompanying data sets provide the starting point for the business opportunity for your proposal. In Module One, be prepared to select your scenario in

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preparation for your Milestone One submission in Module Three.

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Note: The book refers to using Data Miner, a tool not discussed in this course. You are more than welcome to download and install RapidMiner and try it on your
own. It can be downloaded from SourceForge. The data files are CSV files that can be read into RapidMiner, R, or Excel. You may use whichever tool you are the

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most comfortable with to analyze the data.

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Prompt

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Identify a problem or an opportunity from your choice of four scenarios with accompanying data sets in Chapters 5, 8, 9, or 10 in your Data Mining for the
Masses resource. Then craft a data analytics project proposal that leverages data analytics, evaluates the current use of data, and highlights recommended tools

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with the ultimate goal of improving business value. Remember your audience as you craft your proposal.

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Specifically, the following critical elements must be addressed:

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I. Introduction
A. Background: Describe the context and environment of the organization and analyze how the company is currently leveraging data analysis and
analytics tools to make decisions.
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B. Data Sources: Evaluate the data sources the organization is currently using for their benefits and limitations in meeting the goals the data is
currently being used for. In other words, is the currently used data appropriate for its current usage? Why or why not?
C. Data Needs: Analyze the various sources of data available to the organization or the data the organization could potentially begin collecting that
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could add business value. In other words, what data (existing or potential) could provide a benefit to the organization you chose to focus on, and
how?
D. Data Analytics Initiative: How can you exploit data analytics to add business value or uncover new opportunities? Identify the opportunity for a
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data analysis initiative that could provide additional business value to the organization, and explain. (You do not necessarily have to solve a
problem or fill a gap within the organization. Instead, you could identify a new initiative that improves or adds valuable insight or information to
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the organization for decision making.

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 II. Proposal
A. Goals: What are the goals of this initiative? How do they align with the organizational mission? And how do you plan to measure success? Be sure
to consider the progress and pathway for data analytics projects of the type you chose to propose.
B. Data Analytics Life Cycle: Apply the data analytics life cycle to your proposed initiative, and walk your audience (management) through the life
cycle as it applies to the initiative.

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C. Value of life Cycle: Based on your application of the life cycle to the initiative, analyze how the life cycle will help you infer predictability,

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performance, quality, and security of your initiative and its results.
D. Data: Evaluate the existing or desired data for its applicability to your proposed data analytics initiative. In other words, what are the benefits and

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limitations of the current data for the use you have in mind, including potential collection and security implications?
E. Tool Applicability to Initiative: Assess the current data analytic tools for their applicability to your initiative. In other words, how well will the

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existing tools and technology in place work with your initiative?

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F. Tool Applicability to Data: Assess the applicability of the existing tools for the data you have or will have, based on your analysis of the
characteristics of that data. In other words, how fitting are the existing tools for the data, considering the various forms the data may take?

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G. Tool Recommendations: This course covers many analytic tools and technologies, including their benefits and limitations for various uses and
data. Recommend two tools that are not already used and could reasonably be applied to your initiative. Assess the applicability and value of

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these tools as they relate to your available and planned data and the goals you have established for the initiative.

III. Conclusion

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A. Value: Determine the value of applying data analytics to this company or business based on your analysis of the value of the initiative you
proposed. In other words, describe the benefit of using data analytics to meet the goals, needs, or opportunities of your company, and derive

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actionable insight.

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B. Insights: Communicate the insights you gained from your analysis of the initiative, the data, and the data analytic tools and technology you
explored with management. How are these insights potentially beneficial to the company, the industry, and the company’s future? How are they
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beneficial to your future as an analytics professional?
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IV. Communication
Your submission will be assessed according to the content, the logic of your explanations and analysis, and the evidence of your gained knowledge. In the
professional realm, however, the communication of ideas is also important. Therefore, your submission will also be assessed on the way your ideas are
presented to the audience (in this case, the management of the selected company). Remember that management may not have the same level of data
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knowledge that you do, particularly with the use of specialized language.
A. Visualization: Effectively communicate your insights and conclusions using appropriate visualizations and depictions of data possibilities.
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B. Presentation: Present your proposal (either in a report or in presentation format) to ensure your audience understands the value of your initiative.
Remember that a proposal is meant to be accepted, so you will need to employ language and communication techniques that are likely to meet
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your audience’s needs.

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 Milestones
Milestone One: Introduction
In Module Three, you will submit the introduction portion of the final project (Part I). This milestone will be graded with the Milestone One Rubric.

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Milestone Two: Proposal, Sections A–D

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In Module Five, you will submit Sections A–D of the proposal portion of the final project (Part II). This milestone will be graded with the Milestone Two Rubric.

Milestone Three: Proposal, Sections E–G

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In Module Six, you will submit Sections E–G of the proposal portion of the final project (Part II). Although you have used IBM’s BlueMix and R, you are not

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limited to these tools. This milestone will be graded with the Milestone Three Rubric.

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Milestone Four: Conclusion

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In Module Seven, you will submit the conclusion portion of the final project (Part III). Be sure that you are addressing how the proposal will benefit the
organization. This milestone will be graded with the Milestone Four Rubric.

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Final Project Submission: Data Analytics Project Proposal
In Module Nine, you will submit your final data analytics project proposal. Remember that your final submission should include Part IV, which was not addressed

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in any of the milestones. The submission is expected to meet the highest professional standards and should reflect the feedback gained throughout the course.
This submission will be graded with the Final Project Rubric.

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Milestone co rc
Deliverable
Deliverables
Module Due Grading
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One Introduction Three Graded separately; Milestone One Rubric
Two Proposal, Sections A–D Five Graded separately; Milestone Two Rubric
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Three Proposal, Sections E–G Six Graded separately; Milestone Three Rubric
Four Conclusion Seven Graded separately; Milestone Four Rubric
Final Project Submission: Data Analytics Project Proposal Nine Graded separately; Final Project Rubric
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 Final Project Rubric
Guidelines for Submission: Your final project should be submitted as a formal report or presentation, as chosen in Module One. Make sure to address all of the
critical elements in the prompt.

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Remember, if you chose to submit a formal report, the document should use double spacing, 12-point Times New Roman font, and one-inch margins. If you

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chose to submit a presentation, make sure you are able to submit a video file, audio file, or accompanying speaker notes in addition to your slides. Also note that
your instructor plays the role of upper management in this final project scenario. No matter what type of submission you select, you must cite your sources in
APA format.

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Instructor Feedback: This activity uses an integrated rubric in Blackboard. Students can view instructor feedback in the Grade Center. For more information,

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review these instructions.

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Critical Elements Exemplary (100%) Proficient (90%) Needs Improvement (70%) Not Evident (0%) Value
Introduction: Meets “Proficient” criteria and Analyzes how data analysis and Analyzes how data analysis and Does not analyze how data 6.33
Background shows particular insight analytics are currently being analytics are currently leveraged, analysis and analytics are

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regarding the organization’s use leveraged within the described but with gaps in accuracy or leveraged
of data analysis context and environment of the specificity to the context and

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chosen organization environment of the organization
Introduction: Data Meets “Proficient” criteria and Comprehensively evaluates the Evaluates the data sources the Does not evaluate the data 6.33
Sources evidences keen foresight into data sources the organization is organization is currently using, sources the organization is

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potential data sources for currently using for their benefits but not for their benefits and currently using
meeting organizational goals
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goals
limitations in meeting the goals,
or omits key details
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Introduction: Data Meets “Proficient” criteria and Analyzes in detail the various Analyzes various existing or Does not analyze various existing 6.33
Needs evidences keen insight in existing or potential options for potential options for data or potential options for data
connecting data to strategic data sources to determine the sources, but not to determine the sources
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benefits for the organization potential for adding business potential for adding business
value value, or response lacks
necessary detail or key sources
Introduction: Data Meets “Proficient” criteria and Articulates a data analytics Articulates a data analytics Does not articulate a data 6.33
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Analytics Initiative evidences keen insight into the initiative that highlights the initiative that highlights the analytics initiative that highlights
details or potential of leveraging alignment between alignment between the alignment between
data analytics for business organizational opportunity and organizational opportunity and organizational opportunity and
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opportunity and value application of analytics to create application of analytics to create application of analytics to create
value or uncover new value but lacks key detail or value
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opportunities aspects regarding the value or

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opportunity
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 Proposal: Goals Meets “Proficient” criteria and Articulates the goals of the Articulates the goals of the Does not articulate the goals of 6.33
demonstrates keen insight into initiative, alignment with the initiative, alignment with the initiative, alignment with
aligning and measuring data organization, and how success organization, and success organization and measures of
initiatives within organizational will be measured measures, but response contains success
contexts inaccuracies or is missing key

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detail

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Proposal: Data Meets “Proficient” criteria and Accurately applies the data Applies the data analytics life Does not apply the data analytics 6.33
Analytics Life Cycle evidences a nuanced insight into analytics life cycle to the initiative cycle to the initiative, but with life cycle to the initiative
the practical considerations in gaps in detail or accuracy

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applying the analytics life cycle

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within real-world contexts
Proposal: Value of Life Meets “Proficient” criteria and Analyzes the value of life cycle Analyzes the value of the life Does not analyze the value of the 6.33

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Cycle evidences keen insight into the application to the initiative in cycle application to the initiative, life cycle application to the
value of the life cycle approach terms of inferring predictability,
but not in terms of inferring initiative

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for maximizing organizational performance, quality, and predictability, performance,
opportunity and initiative success security of initiative and results
quality, and security, or with gaps
in accuracy or detail

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Proposal: Data Meets “Proficient” criteria, and Evaluates the existing or desired Evaluates the existing or desired Does not evaluate the existing 6.33
response is exceptionally detailed data for its applicability to data for applicability to initiative, and/or proposed data for its

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or provides nuanced discussion proposed data analytics initiative but with gaps in accuracy or applicability to proposed
of the specific value and detail initiative
challenges of data as applied to

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the initiative and context
Proposal: Tool
Applicability to co rc
Meets “Proficient” criteria and
evidences nuanced insight into
Assesses the existing data
analytic tools for their
Assesses existing data analytic
tools, but not in terms of their
Does not assess the existing data
analytic tools
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Initiative benefits and limitations of applicability to the initiative applicability to the initiative, or
different approaches within with gaps in necessary detail
specific business contexts
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Proposal: Tool Meets “Proficient” criteria and Comprehensively assesses the Assesses the applicability of the Does not assess the applicability 6.33
Applicability to Data shows exceptional insight into applicability of the existing tools existing tools given the of the tools given the
the specific capabilities and given the characteristics of the characteristics of the data, but characteristics of the data
limitations of the tools in relation data being used with gaps in detail or accuracy
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to business contexts
Proposal: Tool Meets “Proficient” criteria and is Recommends two tools that Recommends two tools, but the Does not recommend two tools 6.33
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Recommendations exceptionally well aligned with could reasonably be applied to tools are not reasonable for the that could be reasonably applied
the specific needs of the initiative the initiative in terms of their initiative or are not assessed in to the initiative
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or real-world business concerns applicability and value for the terms of their applicability and
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data and goals of the initiative value for the data and goals of
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the initiative
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 Conclusion: Value Meets “Proficient” criteria and Articulates value of applying data Articulates the value of applying Does not articulate the value of 6.33
evidences keen insight the value analytics to the company or data analytics to the company or applying data analytics to the
of data for business decision business based on analysis of the business, but not based on the company or business
making, or response is well value of the proposed initiative estimated value of the initiative,
supported and comprehensive or with gaps in detail or accuracy

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Conclusion: Insights Meets “Proficient” criteria, and Effectively communicates to Communicates the insights Does not communicate the 6.33

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response demonstrates keen management the insights to be gained from the data analytics insights gained from the data
insight into the communication gained from the initiative, data, project proposal process, but analytics project proposal
needs and best practices for data analytic tools, and response lacks detail or is not process

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delivering data insights to non- technology proposed audience-appropriate

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data audiences
Communication: Meets “Proficient” criteria and Effectively communicates the Communicates the insights and Does not communicate insights 6.33

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Visualization makes excellent use of insights and conclusions using conclusions using appropriate and conclusions using
visualizations to communicate appropriate visualizations and visualizations and depictions of appropriate visual displays and

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concepts to audience, showing depictions of data possibilities
data possibilities, but depictions of data possibilities
keen insight into best communication is not always
visualization practices effective

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Communication: Meets “Proficient” criteria, and Employs appropriate Employs communication Does not employ communication 6.33
Presentation presentation techniques and communication techniques to techniques to present proposal techniques to present proposal

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tactics evidence keen insight into present proposal to management to management but does not to management
needs of audience and best with the goal of acceptance select or adhere to techniques
practices of proposal likely to lead to acceptance

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presentation
Articulation of
Response
Submission is free of errors
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related to citations, grammar,
Submission has no major errors
related to citations, grammar,
Submission has major errors
related to citations, grammar,
Submission has critical errors
related to citations, grammar,
5.05
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spelling, syntax, and organization spelling, syntax, or organization spelling, syntax, or organization spelling, syntax, or organization
and is presented in a professional that negatively impact readability that prevent understanding of
and easy to read format and articulation of main ideas ideas
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Earned Total 100%

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