D795 is a graduate computer science course in WGU's Master of Science programs.
WGU describes it as building on algorithm design and optimization for efficient resource use, with an emphasis on benchmarking, reasoning and Big O notation, and practical work that tests data structures, develops algorithmic solutions and optimizes performance.
The difficulty is joining theory to measurement. A student can state that an approach is O(n log n) but must also show how it behaves on real inputs with profiling and benchmarking tools. The guide lists three competency units.
Course at a Glance
| Item | Details |
|---|---|
| University | Western Governors University (WGU) |
| Course code | D795 |
| Level | Graduate (3 competency units in the guide) |
| Programs | M.S. Computer Science: AI and Machine Learning, and Human-Computer Interaction |
| Subject area | Algorithm design, analysis and optimization |
| Typical work | Implementation, analysis and benchmark report |
What D795 Covers
| Competency (summarized) | What it means in practice |
|---|---|
| Design algorithms to optimize performance and resource use | Choose approaches and data structures suited to the task |
| Implement diverse algorithmic techniques | Apply techniques such as divide and conquer, dynamic programming or greedy methods |
| Optimize performance through analysis and implementation changes | Find hot spots, improve them and measure again |
Key Concepts Explained
Big O and What It Hides
Big O describes how running time or memory grows with input size, ignoring constants. It is the right tool for comparing approaches at scale, but constants and memory behavior can dominate on small or medium inputs, which is why measurement matters.
Example: Searching a sorted list of a million items takes about 20 comparisons with binary search (O(log n)) versus up to a million with a linear scan (O(n)). On a list of ten items, the difference is too small to matter.
Benchmarking Method
A fair benchmark uses the same inputs for each candidate, several input sizes, repeated runs and a stated environment. Results are reported as a table or chart so growth can be seen.
Example: Run each sort on arrays of 1,000, 10,000 and 100,000 elements, each several times, and plot time against size. If time per tenfold increase is roughly ten times, growth is near linear; roughly a hundred times suggests quadratic behavior.
Profiling and Optimization
Profilers show where time is spent. Optimize the hottest part first, change one thing at a time, and confirm correctness after each change. A faster algorithm that returns wrong answers is a failure.
Typical Assignments and How to Approach Them
| Task element | What it tests | How to approach it |
|---|---|---|
| Algorithm design | Matching technique to problem | State the problem, candidate approaches and the reason for your choice |
| Complexity analysis | Big O reasoning | Derive time and space cost with the key steps shown |
| Benchmark | Empirical evidence | Describe method, show data and compare with predicted growth |
| Optimization write-up | Evidence-based improvement | Show profile, change, new profile and correctness check |
Linking Theory to Measurement
The strongest D795 write-ups close the loop between prediction and result. Predict the growth first, measure second and then explain any difference.
| Step | What to write |
|---|---|
| Predict | "I expect O(n log n) growth because..." |
| Measure | Table of times for several sizes and repeats |
| Compare | Does growth match the prediction? |
| Explain gaps | Caching, constants, input shape or system noise |
| Improve | One targeted change and a new measurement |
Readiness Checklist Before You Submit
A benchmark report is easy to weaken by leaving out small details, so check these.
| Check | What to confirm |
|---|---|
| Problem defined | Input, output and constraints are stated |
| Complexity derived | Time and space cost are justified, not just asserted |
| Benchmark fair | Same inputs, several sizes, repeated runs, environment stated |
| Prediction compared | Measured growth is compared with the Big O expectation |
| Correctness retested | Results are confirmed after each optimization |
Where Students Get Stuck
- Theory only. Big O without measured evidence leaves a gap.
- Unfair comparisons. Different inputs or setups invalidate a benchmark.
- Unverified optimization. Always retest correctness after a change.
- Single run timings. Repeat and report variation.
Study Tips for D795
- Memorize common growth rates and an example algorithm for each.
- Write a small benchmarking harness once and reuse it.
- Use one table format for all results so they are easy to compare.
- Explain in words what each chart shows.
How We Help with D795
Send the task, any starter code, the rubric and your draft. A writer with a computer science background can prepare a custom worked analysis for study, explain complexity reasoning, or review your benchmark method and write-up. 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 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 description includes implementing and testing algorithms; your task instructions state the language and deliverables.
A way to describe how an algorithm's cost grows as the input grows, ignoring constant factors.
Real systems include constants, memory effects and input patterns that theory alone does not capture.
C950 is the undergraduate data structures and algorithms course; D795 stresses benchmarking, profiling and optimization at graduate level.