2.7: Assessments
- Page ID
- 58286
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Chapter 2 Learning Objectives
- Classify data as structured, semi-structured, or unstructured and explain how each type impacts storage, processing, and analysis approaches.
- Distinguish between quantitative and qualitative data, providing examples of each and explaining why this distinction matters when selecting analysis methods.
- Identify and apply the four levels of measurement — nominal, ordinal, interval, and ratio — and determine which statistical operations are appropriate for each level.
- Compare internal and external data sources, describing common examples of each (e.g., CRM systems, public datasets, IoT sensors, social media) and explaining how integrating both enriches analytics.
- Define the key dimensions of data quality — accuracy, completeness, consistency, timeliness, validity, uniqueness, and integrity — and explain why each is critical for reliable analysis.
- Analyze real-world consequences of poor data quality and describe techniques organizations use to prevent and correct quality issues (e.g., validation rules, data profiling, deduplication).
- Explain the role of metadata in making data discoverable, understandable, and manageable, and identify different types of metadata (descriptive, structural, administrative, technical).
- Differentiate between data governance and data stewardship, describing how governance sets policies and strategy while stewardship handles day-to-day enforcement and quality management.