D600: Statistical Data Mining

D600 Statistical Data Mining is a three-unit course in the second term of WGU's Master of Science in Data Analytics.

WGU describes it as focusing on data preparation and supervised and unsupervised machine learning techniques, with basic knowledge of statistics, preparation, regression and dimensional reduction.

Learners implement supervised classification and prediction models to find relationships not visible with surface-level techniques, and learn when and why to use unsupervised models. The listed prerequisite is Data Preparation and Exploration. The hardest part for most students is interpretation: producing a model is easy, explaining it is not.

What D600 Covers

Key Concepts Explained

Linear Versus Logistic Regression

Linear regression predicts a continuous outcome such as price. Logistic regression predicts the probability of a category, such as whether a customer churns. Choose by the type of outcome variable.

Principal Component Analysis

PCA creates new variables (components) that capture as much variance as possible. It is used to reduce dimensions and to reduce correlation among predictors. Explain how many components you keep and why, and what each represents.

Model Checking

Check assumptions and fit: residuals for linear models, classification performance for logistic ones, and variance explained for PCA. Report what the checks show.

Typical Assignments and How to Approach Them

Reporting Regression Results

A strong regression write-up goes beyond a table of numbers. State the outcome and predictors, report fit statistics, interpret key coefficients in context, note significance and practical meaning, and describe limitations.

Interpreting Principal Components

After computing components, look at the variance each explains and at the loadings, which show how strongly each original variable contributes. Give each retained component a meaningful label if the loadings support one, and say how many components you kept and the rule you used, such as a variance threshold or a scree plot.

A Short Walkthrough

Suppose you model monthly spend using age, tenure and plan type. Check correlations among predictors first. Fit a linear model, read the coefficients, and plot residuals to look for patterns. Then, if you also want to predict whether a customer will cancel, fit a logistic regression and report an odds ratio for tenure, such as "each extra year of tenure is associated with lower odds of canceling."

For PCA on a set of survey items, standardize them, extract components and explain what the first two represent in plain language.

Connecting to Later Courses

D600 supports Deployment, Machine Learning and the later specialization work, so the habits you build matter beyond this course. Learn to report a model in a form another analyst could repeat: data used, steps taken, settings chosen and results obtained. Doing this now makes the comparison and deployment tasks that follow far easier.

Where Students Get Stuck

• Multicollinearity. Check correlations among predictors.
• Interpreting logistic output. Convert to odds or probabilities.
• Choosing components. Justify your retention rule.
• Recommendations without support. Tie each to a result.

Study Tips for D600

• Review D599 statistics before starting.
• Write interpretations in plain English as you go.
• Standardize variables before PCA when scales differ.
• Keep code, output and commentary together.

How We Help with D600

Send the prompt, rubric and data. We can prepare a custom analysis, explain regression and PCA output, or review your work. See our regression analysis help guide.

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