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Manipulating data
Visualizing and reporting data
Applying basic statistical methods
Analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle
Mining data
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Governance, Quality, and Controls | 14% | - Explain key aspects of data quality - Summarize important data governance concepts - Compare and contrast types of data controls |
| Topic 2: Data Concepts and Environments | 15% | - Compare and contrast different data types - Compare and contrast common data structures and file formats - Identify basic concepts of data schemas and dimensions |
| Topic 3: Data Analysis | 23% | - Explain the purpose of inferential statistical methods - Summarize types of analysis and key analysis techniques - Given a scenario, apply the appropriate descriptive statistical methods |
| Topic 4: Data Mining | 25% | - Given a scenario, execute techniques for data manipulation - Explain common techniques for data manipulation and optimization - Explain data acquisition concepts - Identify common reasons for cleansing and profiling datasets |
| Topic 5: Visualization | 23% | - Given a scenario, use appropriate methods for dashboard development - Explain important aspects of a report that tell a data story - Given a scenario, apply the appropriate type of visualization - Given a scenario, translate business requirements to support data-driven decisions - Compare and contrast types of reports |
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