DBST 667: Data Mining

Data Mining covers the knowledge discovery process, including statistical, machine learning, and database algorithms.

What DBST 667 covers

Prerequisite: DBST 651. An overview of the data mining component of the knowledge discovery process. Data mining applications are introduced, and algorithms and techniques useful for solving different problems are identified.

Topics include the application of well-known statistical, machine learning, and database algorithms, including decision trees, similarity measures, regression, Bayes theorem, nearest neighbor, neural networks, and genetic algorithms. Discussion also covers researching data mining applications and integrating data mining with data warehouses.

Typical DBST 667 assignments

Expect an assignment requiring you to apply a specific data mining algorithm (e.g., decision trees or regression) to a dataset and interpret the results.

Key topics in DBST 667

• Knowledge discovery process
• Data mining algorithms (decision trees, regression, neural networks)
• Data mining application research
• Integration with data warehouses

Writing tips for DBST 667

Follow the assignment instructions and rubric line by line

UMGC graduate assignments for DBST 667 are graded against a specific rubric or grading criteria your instructor provides — every requirement has to be visibly addressed. Skipping a requirement because it seems minor is one of the most common reasons a strong submission loses points.

Document your remote-lab database work, not just the final schema or query

DBST 667 assignments require use of a remote access laboratory, and are usually graded on the actual database design or query process — the modeling decisions, the tradeoffs considered, the steps taken — not just a final schema diagram or query result shown without explanation.

Use current, credible database technology sources

Database technologies (NoSQL, distributed systems, security standards) evolve quickly. Strong DBST 667 submissions cite current vendor documentation and recent peer-reviewed database research rather than relying on outdated general-IT sources.

Why students seek help with DBST 667

Students sometimes describe a data mining algorithm conceptually without applying it to an actual dataset and interpreting the output — the rubric typically wants that applied interpretation shown.

How GradeEssays helps with DBST 667

Share your DBST 667 assignment and rubric, and your writer will help you apply and interpret the required data mining algorithm.

Prerequisites and course context

DBST 667 requires DBST 651 as a prerequisite.

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