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Section 1
1.1 Explain data concepts
Preview
Preview: 1.0 Data Concepts and Environments (20%)
Preview includes- 4 of 7 lesson topics
- 1 overview segment
- 3 core concepts
- 2 exam tips
Lesson Topics
- Database Types: Relational vs. Non-relational
- File Formats and Extensions: CSV, XLSX, JSON, TXT, JPG, DAT
- Data Structures: Structured, Semi-structured, Unstructured
- Relational Structures: Tables, Schemas, Fact/Dimension/Bridge Tables
Overview
Objective 1.1 establishes the vocabulary used throughout DA0-002. You should be able to classify databases, files, structures, table roles, and data types from a scenario rather than merely recognize their names.
Core Concepts
- Relational databases organize data into tables related by keys and are commonly queried with SQL. Non-relational databases may use document, key-value, graph, or other models when rigid relational schemas are not the best fit.
- Know the exam file extensions: .csv for delimited tabular text, .xlsx for Excel workbooks, .json for hierarchical key/value documents, .txt for plain text, .jpg for image data, and .dat as a generic data file whose internal structure depends on the producer.
- Structured data follows a defined schema, commonly rows and columns. Semi-structured data carries organizational markers without a fixed relational schema; JSON and nested objects are the canonical exam examples. Unstructured data lacks a predefined tabular model, such as images or free-form documents.
Exam Tips
- Separate the container from the contents: a file extension tells you the storage format, while structure and field data types tell you how the contained data behaves.
- If the scenario says 'historical attribute values must remain reportable,' think slowly changing dimension rather than overwriting a dimension row without history.
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Section 2
1.2 Identify types of data sources
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Section 3
1.3 Identify infrastructure concepts
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Section 4
1.4 Identify common data analysis tools
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Section 5
1.5 Identify artificial intelligence concepts
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Section 6
2.1 Use data acquisition methods
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Section 7
2.2 Explore data for inconsistencies
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Section 8
2.3 Transform and cleanse data
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Section 9
3.1 Determine the communication approach
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Section 10
3.2 Select a statistical method or function
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Section 11
3.3 Troubleshoot basic analytical issues
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Section 12
4.1 Use appropriate visual elements
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Section 13
4.2 Use a delivery or consumption method
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Section 14
4.3 Troubleshoot report issues
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Section 15
5.1 Explain data management concepts
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Section 16
5.2 Summarize data compliance concepts
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Section 17
5.3 Compare privacy and protection practices
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Section 18
5.4 Compare data quality assurance practices
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