WGU D206 DTAN 5201 Data Cleaning Final Assessment (Qns & Ans) 2026

The Western Governors University (WGU) course D206, titled "Data Cleaning," is a pivotal component of the Master of Science in Data Analytics program. This course delves into the intricate processes of preparing data for analysis, emphasizing the importance of data integrity and accuracy. The final assessment, DTAN 5201, challenges students to apply their knowledge in practical scenarios, ensuring they are well-equipped for real-world data challenges.

Understanding the D206 Final Assessment

The DTAN 5201 assessment is designed to evaluate a student's proficiency in data cleaning methodologies. It encompasses various tasks that require meticulous attention to detail and a deep understanding of data preprocessing techniques. Students are often presented with raw datasets riddled with inconsistencies, missing values, and anomalies. The objective is to transform this chaotic data into a structured format suitable for analysis.

Key Components of the Assessment

1. Research Question Formulation: Students must articulate a clear and concise research question that can be addressed using the provided dataset. This step is crucial as it guides the subsequent data cleaning and analysis processes.

2. Data Cleaning Plan: A comprehensive plan outlining the steps to be taken to clean the data is essential. This includes identifying missing values, detecting outliers, and deciding on appropriate imputation methods.

3. Implementation: Using tools such as Python or R, students execute their data cleaning plan. This involves coding scripts to handle various data issues, ensuring that the data is transformed accurately.

4. Documentation and Reporting: A detailed report documenting the entire process, from the initial state of the data to the final cleaned version, is required. This report should include code snippets, explanations of decisions made, and justifications for the methods used.

Challenges Faced by Students

Many students find the D206 assessment demanding due to its open-ended nature. Unlike traditional exams with predefined questions, this assessment requires students to make judgment calls on the best approaches to clean the data. For instance, determining whether to remove or impute missing values can significantly impact the outcome of the analysis.

Additionally, the requirement to document and justify each step adds another layer of complexity. It's not enough to perform the data cleaning; students must also explain their rationale, demonstrating a deep understanding of the principles behind their actions.

Resources and Support

To aid in the successful completion of the assessment, WGU provides various resources:

• Webinars: Dr. Middleton's series of four lectures are particularly beneficial. The first three focus on data cleaning techniques, while the fourth delves into Principal Component Analysis (PCA), a topic that, while not central to data cleaning, is included to broaden students' analytical skills.

  Reddit

• Community Forums: Platforms like Reddit's r/WGU_MSDA offer peer support, where students share their experiences, challenges, and solutions related to the D206 assessment. Engaging with these communities can provide practical insights and moral support.

  Reddit

• Study Materials: Comprehensive exam guides and flashcards are available to reinforce key concepts and test knowledge. These resources can be instrumental in preparing for the assessment.

  Quizlet

Best Practices for Success

1. Start Early: Given the depth and breadth of the assessment, it's advisable to begin working on it well in advance of the deadline.

2. Engage with Available Resources: Utilize the webinars, forums, and study guides provided. They offer valuable insights that can clarify complex concepts and provide practical tips.

3. Document Meticulously: Keep detailed records of every step taken during the data cleaning process. This not only aids in creating the final report but also ensures that you can justify your decisions if questioned.

4. Seek Feedback: Don't hesitate to reach out to peers or mentors for feedback on your approach. A fresh perspective can often highlight areas for improvement that you might have overlooked.

Conclusion

The D206 Data Cleaning final assessment is a rigorous exercise that prepares students for the complexities of real-world data analysis. By engaging deeply with the material, utilizing available resources, and approaching the task methodically, students can not only succeed in the assessment but also build a solid foundation for their future careers in data analytics.

Below are sample Questions and Answers:

1. Which of the following is NOT a common technique for
handling missing data?
 a) Mean Imputation
 b) K-Nearest Neighbors Imputation
c) Duplicate Removal
d) Last Observation Carried Forward
 ANS : c) Duplicate Removal
 Rationale : Duplicate removal is a technique used for
identifying and eliminating duplicate records, not specifically for
handling missing data.
2. What is the primary purpose of data normalization?
 a) To summarize data
 b) To scale features to a common range
c) To convert categorical data to numerical
d) To fill in missing values
 ANS : b) To scale features to a common range
 Rationale : Normalization scales the data to ensure that each
feature contributes equally to the distance computations that
might affect the model performance.