Task 2#
The Proposal -a detailed paper outlining your question/problem and planned data analysis solution.
Do the Analysis Now!#
Task 2 is written as a proposal. However, parts C of task 2 and F of task 3 require a specific type of data analysis. An inductive argument using descriptive methods will not meet the requirements of these sections. Analyzing the data before writing task 2 will help:
Ensure the minimum requirements of the project will be met.
Better align the research question/organizational need with project outcomes.
Better align tasks 2 and 3 allowing task 3 to be completed faster.
Though more of an initial time investment, the analysis must be done at some point. Doing it first allows Task 2 to be written to known (rather than anticipated) results and allows Task 3 to be completed by (mostly) rewriting Task 2.
Warning
Throughout this page, we will be assuming you have completed the data analysis.
Write Task 2#
Write your proposal following Task 2: Project Proposal template:
To gauge the level of detail evaluators typically expect, see the task 2 examples - passing, albeit not the greatest, projects. Individual sections should be written to individually meet their respective rubric section requirements.
Tip
Sections are assessed independently against the rubric requirements, i.e., when evaluating a section, the evaluator will check for the fulfillment of the requirements within that section. They don’t assess writing style. You can (and sometimes must) reuse content from other areas as needed. It’s not about writing something fun to read -it’s about demonstrating that the competencies as defined by the rubric requirements are met.
A: Proposal Overview#
The Proposal Overview summarizes one question or organizational need that you intend to answer by collecting and analyzing a set of data. Think of it as a summary meant to be read by upper management or a general audience.
This part introduces and summarizes a thesis (A1), a literature review (A3), a problem (A2), and your solution (A4 & A5). This section can establish a roadmap for the rest of the paper, but if you write it first expect the need to revisit and revise it once other sections are complete. Remember to keep the introduction concise but informative, providing enough information to clearly meet the requirements of individual sections.
A1: Research Question or Organizational Need#
Much of Tasks 2 and 3 must align with this section. Particularly the rigorous method from part C. After investigating your data, you may need to adjust from what was provided in Task 1. Making changes from Task 1 is acceptable (and somewhat expected) without resubmitting Task 1. However, we recommend contacting your assigned course instructor if significant changes are necessary.
Research Question OR Organizational Need?#
“Research question or organizational need,” is the language of the rubric. Addressing a research question best fits the rubric requirements, and any research question can be described to have an organizational benefit (the pragmatism of which is not assessed). So most projects should choose a research question and advice on this page will favor that direction.
What is a Research Question?#
A research question should identify what your analysis plans to discover. For this project, it should be:
Narrow/specific enough to define an answerable question.
Supportable with a statistical test or model.
Feasible within your available time, resources, and abilities.
A good research question should also be interesting, relevant, and novel. However, these characteristics are not directly or indirectly assessed as the capstone is meant to demonstrate proficiencies in analysis -not scientific research. Particularly do not worry about the novelty of projects as we have no shareable list of previous topics or investigated datasets.
Note
Many of the rubric requirements must align with A1. Particularly part C](task2:C). If you’ve followed the recommendation of first completing the analysis, your research question can be directly related to your hypothesis
A2: Context & Background#
Briefly detail the project’s background and provide enough context making the purpose of your project clear.
A3 & A3A: Summary of Works & Project Relation#
Think of this section as a literature review where you summarize a work (A3) and then relate it to your project (A3A). They need only connect to an aspect of your project; they don’t need to align with it entirely. You can use anything created by an industry professional, e.g., online articles, whitepapers, technical documentation, etc. Most importantly, you must have three different works cited following APA guidelines using in-text citations, e.g., (Author, year). Typically, papers cite sources as needed, but for evaluation purposes, they added this section to demonstrate research.
Review a work. For example, online articles, blogs, case studies, white papers, videos, etc.
Summarize the work. Simply tell your reader what’s in the resource you reviewed, no need to offer an opinion or analyze it -simply summarize the content. We recommend 1-2 paragraphs per work.
Relate the work to your project. Following the summary, describe how the work expands the context of the problem or supports the implementation solution.
Include an APA style in-text citation, e.g., (Author, year); follow [APA guidelines and use the MS reference tool] see (task2:grammar)
Tip
Stuck? Return to this section later. You will likely collect sources while conducting research for other sections. The works do not have to be a one-to-one match to your project. What’s accepted is very broad.
Tip
You can search WGU’s library and other open-source libraries using google.scholar.com Go to >’Google.scholar>setting>libraires>’ and then add WGU and other libraries.
A4: Summary of Data Analytics Solution#
Summarize the proposed solution. Include a summary of the analytic method(s) and its implementation summarized in B3 and detailed in C4. Detail how the solution:
Will be realistically implemented.
Address the research question or organizational need.
A5: Benefits & Support of Decision-Making Process#
Describe how the solution from A4 will:
Provide a benefit and why.
Assist in decision-making and why.
The benefit can directly or indirectly be related to solution assisting in decision-making.
B: Data Analytics Project Plan#
In this part, you will discuss the design details of your data analytics solution. Think of it as a business plan for part C targeting middle management.
B1: Goals, Objectives, & Deliverables#
Goals and objectives are very similar. Goals are broader, defining the end you are trying to achieve (e.g., improving customer service). You need at least one goal. Objectives are more specific, often measurable steps supporting the goal (e.g., real-time inventory updates for customers). Goals and objectives can be considered high-level and mid-management tasks, respectively. Deliverables are tangible tasks completed supporting the objectives (e.g., an inventory status screen reporting real-time inventory to customers).
For this section, you must:
Describe each goal (every project should have at least one)
Describe each objective and how the objectives support the goal(s).
Describe each deliverable, and how each deliverable supports an objective.
Each goal, objective, and deliverable should align with each other, the project as described in section A, and the timeline provided in section B4. A nested bullet point format will help evaluators identify descriptions and intended alignment.
Example
Goal 1: The goal of this project is to …
Objective 1.1: Determine if … - Deliverable 1.1.1: The deliverable for this objective is … - Deliverable 1.1.2: The deliverable for this objective is …
Objective 1.2: Provide a … - Deliverable 1.2.1: The deliverable for this objective is …
Also, see part B1 of these examples.
B2: Scope of Project#
Describe:
What the project will entail.
What the project will not entail.
Warning
Don’t forget the “not” part! You must include at least one specific item outside the project’s scope. Overlooking this is one of the most common reasons for returns.
B3: Standard Methodology#
The methodology is the process you will follow when implementing your solution. Include specific details to adequately describe the steps that will take place in each phase. In this section, you must:
Identify a “standard” methodology used to plan the project, e.g., Waterfall or ADDIE.
Describe the project steps to be completed in each phase of the methodology, e.g., analysis, design, etc.
The second step provides a detailed implementation plan also outlined in section A2. If using a less-known methodology, relate it to a “standard” one.
B4: Timeline & Milestones#
Provide a timeline aligning with the milestones and deliverables described in section B1. While a table is not specifically required, it provides a succinct presentation satisfying the requirements of B.4., and it is what evaluators have come to expect.
Example
Milestone or Deliverable |
Duration |
Projected Start Date |
Projected End date |
|---|---|---|---|
Some milestones |
7 days |
7/23/2022 |
7/30/2022 |
Some deliverables |
14 days |
7/16/2022 |
7/30/2022 |
\(\vdots\) |
\(\vdots\) |
\(\vdots\) |
\(\vdots\) |
B5: Resources & Costs#
Include a list of all necessary resources and the approximate cost of each resource. The budget must include estimated costs for hardware, software, and work hours.
Example
Hardware item: $1000
Software item 1: No cost
Software item 2: No cost
10 work hours: \(500 (10 hours at \)50 per hour)
\(\dots\)
Be realistic as possible when estimating costs. However, this is not a business project and values are not rigorously assessed. The minimum number of listed items is three.
B6: Criteria for Success#
Provide specific objective means of assessing success. You should base these criteria on successful execution and correct interpretation of your methods -not the results. It is acceptable that a test fails to find results statistically significant provided the conclusion and methods are appropriate.
The criteria can be metrics, but also can be project-related task that can be counted as having been completed. See B.6 of the Task 2 examples.
C: Design of Data Analytics Solution#
In this part, you will discuss the details of your data analytics solution targeting an expert audience -fellow data analysts. These sections contain the rigorous data analysis requirements of task 2. Thus, we recommend completing at least a rough outline of this part before investing time in parts A and B.
C1: Hypothesis#
Provide a hypothesis supporting the research question or organizational need given in section A1. The minimum required hypothesis is one. Your hypothesis must be supportable by a hypothesis test or model.
If using a statistical test, you’ll conduct a hypothesis test and you should state the alternative hypothesis you’ll use here. For models, state a claim supportable by the metric you plan to use to measure the model’s success.
C2 & C2A: Analytical Method#
Identify (C2) and justify (C2A) each statistical test or model which will support each hypothesis given in section C1. Summarize how each method will be performed or developed. The minimum required method is one per hypothesis.
C3: Tools & Environment#
Identify the tools, e.g., IDE, languages, libraries, etc., which will be used to complete the analytical method(s) described in section C2.
C4 & C4A: Methods & Metrics to Evaluate Statistical Significance#
For each statistical test, provide the following information (C4):
A null hypothesis (the opposite of your hypothesis).
The name of the proposed statistical test, e.g., 1-sample t-test, Chi-square, correlation, etc.
The metric(s) generated from that test (e.g., a t-stat) from which probability (the \(p\)-value) is derived.
The alpha value (denoted \(\alpha\); usually 1% or 5%) that will be used to determine statistical significance (e.g., if \(\alpha = .05\) and \(p\)-value \(= .025\) then the null hypothesis will be rejected and there is sufficient evidence to support the hypothesis).
For each model, provide the following information (C4):
The type of model, e.g., supervised regression, supervised classification, etc.
The name of the proposed model, e.g., linear regression, logistic regression, Bayesian, CNN, etc.
The metric(s) to be used to assess performance.
The benchmark to which the above metric(s) will be compared to determine the success of the model(s), e.g., “If the correlation coefficient is \(\geq .6\), the model will be considered successful…”
For each statistical test or model, describe why it is an appropriate choice (C4A). This may repeat (verbatim) parts of section C2A.
C5: Practical Significance#
Practical significance refers to how meaningful your findings are in practical application. Results are practically significant when the difference is large enough to be meaningful in real life. This is subjective. But at minimum discuss some criteria to judge the practical significance and how this will be used to support the research question or organizational need from A1. Consider including an example of how the client might apply your work discussed in sections C1 through C4A.
C6: Visual Communication#
Task 3, the Project Report, must include graphic visualizations (at least two) for visually communicating elements of your project (see Task 3: G2). Describe a plan to include at least two visualizations of the data, statistical test(s), or model(s). Specifically, name the types of graphs, what they will visualize, and the tools you’ll use to generate the images.
D: Description of Dataset#
This part discusses the current state of your data (pre-processed). Write from the assumption that the data sources have been identified, collected, and previewed, but not in any other way processed.
D1: Source of Data#
Simply identify each data source. The minimum number of sources is one.
D2: Appropriateness of Dataset#
Describe why each data source provided in section D1 is appropriate for supporting the research question or organizational need from section A1.
D3: Data Collection Methods#
Describe how each data source listed in section D.1 was collected, e.g., “the data was collected by downloading the .csv file from <www.kaggle.com/data_source_link.html.”>
D4: Observations on Quality & Completeness of Data#
Describe both the quality and completeness of the data and any accommodation needed. Often, data is already clean and complete, but it is still necessary to comment on both.
D5 & D5A: Data Governance, Privacy, Security, Ethical, Legal, & Regulatory Compliances#
Specifically, address how each of the following relates to your data and project (D5):
Data governance.
privacy.
Security.
Ethical, legal, and regulatory compliance considerations.
Describe any necessary precautions (D5A). In cases where an item is not relevant, you must explain why. You only need to discuss measures for handling human data if you collected that data
Grammar, Sources, and APA#
It’s easy to overlook them when focusing on content, but grammar, sources, and APA formatting are the most common reasons for rejected submissions!
Check your grammar using Grammarly.com 
(it’s what the evaluators use). Style is not assessed (Grammarly marks these in blue, green, or purple), but even a few grammar errors (marked in red) will prevent competency in Professional Communication. The free side has been sufficient, but if using the online app, you sometimes need to wait before mistakes are caught.
Warning
Students have reported missed mistakes when using the Google Doc Grammarly extension. Therefore, we advise copying content directly into the app or purchasing the premium version compatible with MS Word.
Sources and format should follow APA guidelines. Avoid reference errors by using the MS Word Reference Tool to create and manage references and review this guide on how to Avoid Common APA errors.
Get the best writing help from the writing experts: WGU Writing Center. While Writing Center Instructors cannot say whether a task will pass (no one but your specific evaluator can), they will help you revise your paper to meet WGU competency standards for professional communication, sources, and APA formatting. The Writing Center also offers live Q&A sessions. See a list of upcoming events here: Writing Center Live Events.
FAQ#
I’ve completed task 2. Should I send it to my course instructor for review?#
If you have specific questions or concerns -yes. However, in most cases, it’s best just to submit. What suffices as “sufficient detail” is highly subjective. We can always tell you to add more, but if you’ve done your best to fulfill the requirements, submit it and let them tell you which (if any) parts need to be rewritten. At best, it passes; at worst, we address the issues cited by the evaluator -and then it passes. Responding to the more narrow focus of the evaluator’s comments is generally easier than overworking the entire project.
You have unlimited submissions but limited time. And, typically this is the best and most efficient approach.
Are there any examples?#
Yes! See D195 examples.
How long should task 2 be? Is there a page length required?#
No. The individual evaluator determines what qualifies as “sufficient detail” and will vary depending on the project and writing style. If you feel you’ve met the requirements, simply move on to the next section. Upon submission, it will pass, or they will request more details.
How many submission attempts do I have?#
You have unlimited submissions (as with all WGU performance assessments). Furthermore, a project requiring multiple submissions is not precluded from being given an excellence award. However, do attempt to fully meet each requirement as submissions falling significantly short of the minimum requirements may be locked from further submissions without instructor approval. Moreover, such submissions do not receive meaningful evaluator comments.
I can’t find sources for section A3, Summary of Published Works. What can I do?#
You are likely over interpreting what’s required. Rarely are submissions sent back because cited works are unsuitable. You can use any referenceable work created by a professional which can be related to your project -it does not need to align with your project entirely. For example, an article about your chosen analytical method or tools would suffice.
My task two was returned for Sources stating citations are missing, but I properly cited everything#
If the evaluator wrote something like:
“In-text citations could not be found for portions of the task that have been quoted or paraphrased… “ What does this comment mean?
This indicates they could not find a matching in-text citation for every source on your reference list. Check that each reference has a match following APA style, e.g., (Author, year), and remove any references without matches. Use the MS Word Reference Tool to create, manage references, and avoid such errors. Follow the in-text citations and the reference page format of the tasks 2 and 3 examples.
How complex does my data or analytic method need to be?#
It must be complex enough to meet the needs of your project. There is no explicit minimal complexity for either. However, the data must meet the needs of the research question and the method must be appropriate for both the data and the research question which may indirectly require a minimal complexity. For example, testing for correlation inherently requires two variables and parametric methods often need a minimal sample number to assume normality.
Are there any restrictions on which datasets I can choose?#
Only that data must be legally available to use and share with evaluators. For example, using data belonging to a current employer would require submitting a waiver form.
You can use any dataset found on kaggle.com.
You can use simulated data.
You can use data used for previous projects (submitted by you or others).
You only need to apply for IRB review if you are collecting data involving human participants (this is rarely needed). Otherwise, your project is in IRB compliance.