D789 is the first course in a series in WGU's M.S. Software Engineering, AI Engineering specialization.
WGU describes it as familiarizing students with machine learning through the lens of a software engineer: the challenges and opportunities of applying ML to business problems and strategic goals across industries.
Technical students often underestimate how much of the work is communication. The competencies include explaining ML strategies and recommendations to diverse stakeholders, so a perfect model description that a finance director or operations manager cannot follow will still fall short. The guide lists three competency units.
Course at a Glance
| Item | Details |
|---|---|
| University | Western Governors University (WGU) |
| Course code | D789 |
| Level | Graduate (3 competency units in the guide) |
| Program | M.S. Software Engineering, AI Engineering |
| Subject area | Applied machine learning and business strategy |
| Typical work | A business case with a model approach and stakeholder recommendation |
What D789 Covers
| Competency (summarized) | What it means in practice |
|---|---|
| Analyze industry challenges and opportunities | Spot where ML helps and where it is a poor fit |
| Select suitable models | Match the algorithm family to the problem and goal |
| Apply a model to an identified business need | Show how the chosen approach addresses the need |
| Communicate with diverse stakeholders | Tailor the message to what each audience cares about |
Key Concepts Explained
Framing the Problem
Start from the decision the business wants to improve, then ask what data exists and what a useful prediction would look like. Many failed ML projects begin with a technique instead of a question.
Example: A subscription company wants to reduce cancellations. The business question is "which customers are likely to leave next month?", which is a classification problem. The output is a ranked list that the retention team can contact.
Choosing a Model Type
Classification predicts categories, regression predicts numbers, clustering finds groups and recommendation matches items to users. The right choice also depends on data volume, explainability needs and cost of mistakes.
Example: For loan decisions, a simpler model that can explain its reasoning may be preferred over a slightly more accurate black box, because the business must justify its decisions.
Success Measures
Technical metrics such as accuracy, precision or recall must be linked to business outcomes such as cost saved or revenue protected. Which error matters more (a false alarm or a miss) depends on the case.
Typical Assignments and How to Approach Them
| Task element | What it tests | How to approach it |
|---|---|---|
| Industry analysis | Opportunity spotting | Describe a use case, the data needed and the main risk |
| Model selection | Matching method to problem | Compare two or three approaches and justify your pick |
| Business application | Linking model to value | State the decision, the output and how it will be used |
| Stakeholder message | Clear writing | Lead with the recommendation, then evidence, then risks, in non-technical language |
Writing for Different Stakeholders
The same recommendation reads differently for different audiences. Prepare a short version for each stakeholder named in your task, and check that every version states the same facts.
| Audience | Cares about | Lead with |
|---|---|---|
| Executive | Value and risk | Expected business effect and cost |
| Operations | Workflow change | How the output will be used day to day |
| Engineering | Feasibility | Data needs, integration and maintenance |
| Compliance | Fairness and privacy | Data handling and bias checks |
Readiness Checklist Before You Submit
A short check against the four competencies catches most gaps before an evaluator does.
| Check | What to confirm |
|---|---|
| Opportunity analyzed | The industry use case, data needed and main risk are stated |
| Model selected | You compared options and explained the choice |
| Applied to the need | The decision, output and use of the result are clear |
| Stakeholders addressed | Each named audience gets a tailored message |
| Limits stated | Data gaps, errors and ethical risks are acknowledged |
Where Students Get Stuck
- Technique-first thinking. Begin with the decision and the data, not the algorithm.
- Under-developed communication. Stakeholder messaging is a named competency.
- Ignoring data quality. Say what data exists, what is missing and what it would cost to fix.
- Metrics detached from value. Connect each metric to a business result.
Study Tips for D789
- Make a one-page map of problem types and model families.
- Practice explaining one model to a non-technical friend in three sentences.
- Always state limitations and risks next to benefits.
- Look ahead to D790, which deepens the ethics and oversight themes.
How We Help with D789
Send the scenario, the rubric and your draft. A writer with data science and software experience can prepare a worked model business case for study, explain model families in plain language, or review your communication for clarity. Our master's assignment help guide describes the process.
GradeEssays is independent of Western Governors University. Model work is for study and reference; your submission must be your own under WGU's academic integrity policy, and no result is promised. Orders are written from scratch with free revisions within the scope of your original request, and a full refund if late.
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Frequently Asked Questions
WGU's guide lists it in the M.S. Software Engineering, AI Engineering plan, described as the first in a series.
Three, according to the guide.
The competencies include applying a model to a business need; check your task instructions for whether coding is required.
Match the audience. Executives need value and risk; engineers need data and integration detail.
Choose the one linked to the cost of errors in your case, and explain why.
The AI Engineering specialization continues with Human Centered AI (D790) and Integrating AI with Modern Software Applications (D791).