3  Techniques for Addressing Data Quality Issues

3.1 Assignment: 20 Marks

3.1.1 Instructions

• Organize into groups, with a maximum of 5 members per group.

• Each member should have a distinct role and individual contribution toward the final submission.

3.1.2 Task

• Identify and explore methods to address common data quality issues (e.g., handling missing values, detecting and managing outliers, resolving data inconsistencies, etc.).

• Use a sample dataset to demonstrate these methods in R. Your implementation should include clear R code and explanations of the methodologies used.

3.1.3 Submission Requirements

1. Report: The group must prepare a comprehensive report that details:

• An overview of data quality issues and the chosen methods.

• The step-by-step process for implementing these methods in R.

• Sample R code with commentary and explanations. Insights or findings based on the sample dataset.

2. Presentation:

• Each member must present only the part of the work they personally contributed to.

3.1.4 Evaluation Criteria

• Quality and accuracy of the report, including clarity of explanations and code functionality. Effectiveness and relevance of the techniques chosen for data quality issues.

• Depth of individual contributions in both the report and the presentation.

• Cohesiveness and clarity of the group presentation.

• To earn marks, each member must contribute to both the written report and the presentation.

3.1.5 Deadline

• Submit the report and presentation files by November 9, 2024

• Final presentations: November ₁₀ 12, 2024