WGU D599: Data Preparation and Exploration

What D599 Is and Why It Sits Early in the MSDA

WGU D599, Data Preparation and Exploration (course code DTAN 5218), is a course in the School of Technology's Master of Science in Data Analytics program. It applies your analytical programming skills to the first real steps of the data analytics life cycle: getting raw, messy data into a trustworthy shape, then exploring it well enough to know what you actually have. You typically reach it after The Data Analytics Journey, Data Management, and Analytics Programming, which come earlier in the MSDA sequence, so you arrive already able to write code and query data.

Direct answer: D599 is a performance assessment, not a proctored multiple-choice test, so you "pass" by submitting deliverables that satisfy a rubric. Work through the assessment task by task, follow the rubric line for line, and make sure your data cleaning, your statistical testing, and your market basket analysis are each explained clearly in writing, not just coded. Do that and revisions stay minimal.

The reason this course matters is simple: in real analytics work, clean data is the difference between an insight and a mistake. D599 is where WGU makes you prove you can profile a dataset, fix its problems, run and interpret statistics honestly, and communicate the result to someone who does not read code. Those are the habits every later course and every analytics job leans on.

What the Assessment Covers

D599 is built around applied data work rather than memorization. Based on WGU's course description and program materials, the skills and topic areas you should expect include:

• Data cleaning and quality — checking structure, accuracy, completeness, and consistency; handling missing values, duplicates, and outliers.
• Data profiling — systematically describing a dataset before you touch it, so your cleaning choices are defensible.
• Descriptive and inferential statistics — summarizing data and drawing supported conclusions from it.
• Data visualization — building charts that reveal distributions, relationships, and problems in the data.
• Data wrangling — reshaping and transforming data to prepare it for further analysis.
• Hypothesis testing — applying parametric tests and interpreting the results correctly.
• Market basket analysis — using association rules to find items that occur together in transactions.
• Communicating findings — explaining technical results to a non-technical audience within an organization.

These map onto the assessment's deliverables, which students describe as a data profiling and cleaning report, a statistical analysis and testing task, and a market basket analysis report. Most students complete the coding work in Python with libraries such as pandas, though you should follow whatever your current task instructions specify.

How Hard It Is, and How Long to Budget

Difficulty here is less about raw concepts and more about precision and writing. If you were comfortable in Analytics Programming, the code in D599 will feel familiar; the friction tends to come from interpreting statistical output correctly and from matching every rubric point in the written portion. Many students report that the technical steps are manageable but that the write-ups and rubric alignment are what drive resubmissions.

Because it is a multi-task performance assessment, timelines vary widely with your coding fluency. Students who already work with pandas and statistics often move through it in a couple of focused weeks; those who need to relearn hypothesis testing or association rules should plan more. Rather than chase someone else's pace, budget by task: give each deliverable its own block of days, and treat the writing and evaluation reviews as real work, not an afterthought.

A Task-by-Task Study Plan

Performance assessments reward structure, so build one deliberately instead of coding freely and hoping it adds up.

• Read the rubric first, then reread it. Before writing any code, list every rubric aspect as a checklist. Each aspect is a thing an evaluator must be able to point at in your submission. Write to that list, not around it.
• Profile before you clean. Run summary statistics, check data types, count nulls and duplicates, and inspect distributions. Document what you found first; your cleaning decisions become easy to justify when they answer documented problems.
• Use active recall on the statistics. Close your notes and explain, out loud, when a parametric test is appropriate, what the null and alternative hypotheses are, and what a p-value does and does not tell you. If you cannot say it plainly, you cannot write it clearly.
• Practice on a throwaway dataset. Grab any public dataset and rehearse the full pipeline — profile, clean, visualize, test, interpret — once before you do it "for real." Spaced repetition of the whole workflow beats cramming one piece.
• Rebuild market basket analysis from scratch. Make sure you can explain support, confidence, and lift in your own words and show where each appears in your results, rather than pasting library output you cannot interpret.
• Write for a non-technical reader. After each analysis, draft a short plain-language summary of what it means for a decision-maker. That is an explicit skill the course assesses, and it is often the difference between a pass and a return.

If your statistics feel shaky, a refresher through material like C955A Applied Probability and Statistics can steady the hypothesis-testing portion, and the visualization habits from D466 Analyzing and Visualizing Data transfer directly to the exploration work here.

Mistakes That Send D599 Back for Revision

• Cleaning without documenting why. Evaluators want the reasoning behind each fix. Dropping rows or imputing values without explaining the impact is a common resubmission trigger.
• Reporting a test result without interpreting it. A p-value with no plain-English conclusion, or a conclusion that overstates what the test supports, gets flagged.
• Confusing statistical significance with importance. Say what the finding means for the scenario, not just whether it cleared a threshold.
• Skipping the non-technical explanation. Strong code plus a purely technical write-up still misses the communication aspects of the rubric.
• Submitting code that does not run cleanly. Restart your notebook and run it top to bottom before submitting; an evaluator who hits an error may return the whole task.
• Ignoring required tools or formats. Follow the task's specified libraries, file types, and structure exactly rather than substituting your favorites.

D599 Readiness Checklist

Before you submit, confirm you can honestly say yes to each of these:

• Can you profile a raw dataset and describe its quality issues in writing?
• Can you justify every cleaning decision by pointing to a documented problem?
• Can you explain the difference between descriptive and inferential statistics with an example?
• Can you state when a parametric test is appropriate and set up its hypotheses correctly?
• Can you interpret a p-value and state a conclusion without overclaiming?
• Can you define support, confidence, and lift and identify them in your market basket results?
• Can you build a visualization that actually reveals something and explain what it shows?
• Can you summarize each finding for a non-technical stakeholder in a few sentences?
• Does your notebook run start to finish without errors on a fresh restart?

FAQ

Is D599 an OA or a PA?

D599 is assessed by a performance assessment. Instead of a proctored objective test, you submit deliverables — commonly a data profiling and cleaning report, a statistical analysis and testing task, and a market basket analysis report — that are graded against a rubric.

What tools do I need for D599?

The coding work is typically done in Python with data libraries such as pandas, and the analysis is usually written up in a notebook. Always follow the specific tools and formats named in your current task instructions, since course materials are updated over time.

What comes before D599 in the program?

The Data Analytics Journey, Data Management, and Analytics Programming come earlier in the MSDA sequence, so you should already be comfortable writing code and working with datasets before starting D599.

How long does D599 take?

It depends heavily on your coding fluency. Students who already work fluently with pandas and statistics often finish in a couple of focused weeks, while others need longer. Budgeting time per deliverable is more reliable than aiming for a fixed number of weeks.

Why does my submission keep getting returned?

The most common reasons are cleaning steps that are not justified in writing, statistical results that are reported but not interpreted, and missing plain-language explanations for a non-technical audience. Aligning each section to a specific rubric aspect usually fixes this.

Where does D599 fit in the overall program?

It sits early in the MSDA, building the data-preparation foundation that later courses depend on, all the way through the D606 Data Science Capstone. You can see how the whole School of Technology track fits together on the technology college hub or browse every WGU study guide.