D797 is a foundation course shared across WGU's Master of Science, Computer Science programs.
WGU describes it as exploring foundational AI and machine learning concepts and applications: the history and growth phases of AI, computational theories and logical frameworks, the hardware advances behind deep learning, data integrity and wrangling, and the societal and ethical dimensions. It has no prerequisites.
Students find that it asks for both technical and reflective writing. You may prepare data and apply an algorithm, then discuss ethical consequences with the same care. The guide lists three competency units.
Course at a Glance
| Item | Details |
|---|---|
| University | Western Governors University (WGU) |
| Course code | D797 |
| Level | Graduate (3 competency units in the guide) |
| Programs | M.S. Computer Science: AI and Machine Learning, and Human-Computer Interaction |
| Subject area | AI and ML foundations, data preparation, ethics |
| Typical work | Applied analysis with data preparation and an ethics discussion |
What D797 Covers
| Competency (summarized) | What it means in practice |
|---|---|
| Analyze AI and ML capabilities | Explain what these systems can and cannot do |
| Prepare data for machine learning | Clean, transform and check data before modeling |
| Apply AI and ML algorithms to real-world problems | Select and run an algorithm that fits the task |
| Optimize AI systems for performance and efficiency | Tune and compare approaches |
| Evaluate ethical implications | Discuss bias, privacy, accountability and societal effects |
Key Concepts Explained
Data Wrangling
Raw data usually has missing values, inconsistent formats, duplicates and outliers. Preparation decisions affect results, so each should be justified and documented.
Example: In a customer table, 8 percent of ages are blank. You could remove those rows, fill with the median or add a missing flag. State which you chose, why, and what effect it might have on the model.
Learning Paradigms
Supervised learning uses labeled examples, unsupervised learning finds structure without labels and reinforcement learning learns from rewards. The type of problem and the data available guide the choice.
Example: Predicting house prices from past sales is supervised regression. Grouping shoppers by buying patterns without predefined labels is unsupervised clustering.
Ethics in Practice
Ethical analysis becomes useful when it is specific: which groups might be harmed, which data introduced the bias and what could reduce the risk.
Typical Assignments and How to Approach Them
| Task element | What it tests | How to approach it |
|---|---|---|
| Historical and conceptual analysis | Understanding of the field | Link a historical development to a present capability |
| Data preparation | Wrangling skill | Document each step and the reason for it |
| Algorithm application | Choosing and using methods | Compare at least two approaches with a metric |
| Ethical evaluation | Reflective judgment | Name affected groups, risks and mitigations |
Balancing the Technical and Ethical Halves
Give each half of the assessment equal care. A balanced structure helps readers see that every competency has an answer.
| Section | Contents |
|---|---|
| Problem and data | The question, data source and quality issues |
| Preparation | Steps taken and justification |
| Modeling | Algorithms tried and the metric used |
| Ethics | Risks, who is affected and what you would do about them |
Readiness Checklist Before You Submit
Both halves of the assessment need visible answers.
| Check | What to confirm |
|---|---|
| Capabilities analyzed | What the technology can and cannot do is explained |
| Data prepared | Each cleaning step and its reason is documented |
| Algorithms compared | At least two approaches with a metric |
| Optimization shown | You tuned or improved and measured the effect |
| Ethics specific | Groups, risks and mitigations are named |
Splitting Your Study Time
Competency-based pacing is set by you and your mentor, so decide in advance where the effort goes. For D797 the data preparation and the ethics reflection are the two places students most often run short.
| Stage | Focus |
|---|---|
| Concepts and history | Learn the vocabulary of paradigms and the field's growth phases |
| Data preparation practice | Clean one messy dataset and log every decision |
| Algorithm application | Run and compare two methods with one metric |
| Ethics reflection | Write the risks for your specific data and use |
| Final read-through | Match each competency to a visible section |
Where Students Get Stuck
- Technical focus only. The ethics component is a named competency.
- Undocumented cleaning. Silent data changes make results hard to trust.
- One algorithm. Compare approaches to show why your choice fits.
- Generic ethics. Tie concerns to the specific data and use.
Study Tips for D797
- Create a one-page map of problem types and algorithm families.
- Keep a cleaning log while you work.
- Write ethics notes as you go, not at the end.
- Read ahead into D801 to see how these ideas deepen.
How We Help with D797
Send the task, rubric, any dataset instructions and your draft. A writer with a data science background can prepare a worked model for study, explain data preparation steps, or review your ethics discussion for specificity. See our master's assignment help guide for how orders work.
GradeEssays is independent of Western Governors University. Model work is for study and reference; what you submit must be your own under WGU's academic integrity policy, and no outcome 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 description states there are no prerequisites.
WGU's guides list it in the M.S. Computer Science plans for AI and Machine Learning, and Human-Computer Interaction.
Three, according to the guides.
The competencies include preparing data and applying algorithms, so expect some. Your task states the tools.
Cleaning and transforming raw data so it is suitable for analysis or modeling.
Be specific about the data, the groups affected, the likely harm and the steps to reduce it.