D599 Data Preparation and Exploration is a three-unit course in the second term of WGU's Master of Science in Data Analytics. WGU describes it as applying analytical programming skills to the early steps of the data analytics life cycle.
The course covers cleaning data for structure, accuracy and quality; interpreting descriptive and inferential statistics and visualizations; and wrangling data for later analysis. It introduces hypothesis testing with a focus on parametric tests and the skills to explain findings within an organization.
The listed prerequisites are The Data Analytics Journey, Data Management and Analytics Programming.
Course at a Glance
| Item | Details |
|---|---|
| University | Western Governors University |
| Course code | D599 |
| Level | Graduate (MS Data Analytics) |
| Prerequisites | The Data Analytics Journey, Data Management, Analytics Programming |
| Standard path | Term 2, 3 competency units |
| Typical work | Data dictionary interpretation, cleaning and wrangling, statistical interpretation |
What D599 Covers
| Competency (WGU wording, summarized) | What it means in practice |
|---|---|
| Interprets a data dictionary to understand the data set | Reading variable definitions, types and units |
| Interprets probability, descriptive and inferential statistics, and visualization | Summaries, distributions, confidence and tests |
| Wrangles data to ensure accuracy, format and integrity | Fixing missing values, duplicates, types and outliers |
Key Concepts Explained
Cleaning Decisions
Every cleaning step is a decision with consequences. Dropping rows with missing values can bias results; imputing values changes distributions. Document what you did and why.
Illustration (hypothetical): A column of customer ages contains 999 as a placeholder for unknown. Treating 999 as a real age distorts the mean. The cleaner approach is to recode it as missing, check how many are affected and note the handling.
Descriptive Versus Inferential Statistics
Descriptive statistics summarize the data you have. Inferential statistics use a sample to say something about a larger population, with uncertainty.
Hypothesis Testing
State the null and alternative hypotheses, choose a suitable parametric test, check assumptions, compute the result and interpret the p-value in context. Say what the result means for the business question, not only whether it is significant.
Typical Assignments and How to Approach Them
| Task type | What it tests | How to approach it |
|---|---|---|
| Data dictionary and profiling | Understanding data | Summarize each variable and flag quality problems |
| Cleaning and wrangling | Technical judgment | Record every change and its rationale |
| Statistical interpretation | Applied statistics | Match test to data, check assumptions, explain results |
A Repeatable Cleaning Workflow
A consistent order of operations avoids mistakes.
- Read the data dictionary and note each variable's meaning and type.
- Profile the data: counts, missing values, ranges and duplicates.
- Fix types and formats so values are comparable.
- Handle missing values and outliers with a stated reason.
- Explore with summaries and charts.
- Test hypotheses where required and interpret the results.
Reading Statistics and Visuals
The second competency covers probability, descriptive and inferential statistics, and visualization. When you present a chart, say what it shows, what pattern matters and what limits apply. A histogram reveals shape and outliers; a box plot compares groups; a scatter plot shows relationships. Pair each with a sentence of interpretation.
A Short Cleaning Walkthrough
Imagine a customer file with a date column stored as text in two formats, a few duplicate rows and some blank income values. Steps: convert both date formats to one, drop exact duplicates and record how many, then decide on the blank incomes.
If only a small share are missing, you might remove them; if many are missing, you might fill with the median and note the effect. Compare summary statistics before and after to show what changed.
Connecting to Earlier Courses
D599 builds on the three courses before it. From The Data Analytics Journey, bring the habit of starting with the question. From Data Management, bring comfort with data types and structure.
From Analytics Programming, bring the ability to script a repeatable process. Reviewing short notes from each before you begin will make the cleaning and testing tasks feel like a continuation rather than a new start.
Where Students Get Stuck
- Silent data changes. Keep a cleaning log.
- Wrong test choice. Check data type and assumptions.
- Misreading p-values. Explain what they do and do not show.
- Weak visuals. Label axes and choose chart types deliberately.
Study Tips for D599
- Review prerequisites, especially programming and data handling.
- Always look at the data before and after each step.
- Write a short interpretation under every table or chart.
- Keep code and commentary together.
How We Help with D599
Send the prompt, rubric and dataset. We can prepare a model workflow, explain statistics in plain language, or review your analysis. See our statistics assignment help guide.
GradeEssays is independent and not affiliated with WGU. Our work is a study and reference aid. Your submission must be your own work under WGU's academic integrity policy. Orders are written from scratch, with free revisions within the scope of your original request and a full refund if late.
Get Your Data Prep Right
Share the prompt, rubric and data. We prepare a model workflow or review your analysis.
Start My D599 HelpFree revisions · Full refund if late · Written from scratch for your order
Frequently Asked Questions
Data cleaning, descriptive and inferential statistics, visualization, wrangling and introductory hypothesis testing.
The Data Analytics Journey, Data Management and Analytics Programming.
Term two of the MS Data Analytics standard path.
The description focuses on parametric tests.
Yes, it applies analytical programming skills.
No. We provide tutoring, custom answers, editing and feedback only.