WGU D467 Final Exam: Data Types, Structures, and Ethics | Actual verified
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Module 1 Data Types and Structures
I. Data Generation and Collection
A. Data is created through observations, measurements, surveys, transactions,
and system activity
B. Analysts select data based on relevance to the problem or objective
C. Quality data collection is essential for accurate analysis and decision-making
II. Data Types
A. Numerical Data
1. Represents numbers
2. Used for calculations and measurements
3. Examples: age, height, revenue
B. Categorical Data
1. Represents labels or groups
2. Used for classification
3. Examples: gender, department, color
C. Boolean Data
1. Represents true/false values
2. Used for logical conditions
3. Examples: yes/no, pass/fail, active/inactive
D. Fields and Values
1. Field: category or column name
2. Value: specific entry within a field
III. Data Structures
A. Structured Data
1. Organized into rows and columns
2. Easily stored in spreadsheets and databases
3. Example: Excel tables, SQL databases
B. Unstructured Data
1. Not organized in a predefined format
2. Harder to analyze without processing
3. Examples: emails, images, videos, text documents
IV. Data Formats
A. Wide Format
1. Each variable has its own column
2. Easier for viewing and reporting
3. Common in spreadsheets
B. Long Format
1. Variables stored in fewer columns, values stacked in rows
2. Easier for statistical analysis and visualization
3. Common in data analysis tools
V. Data Integrity and Quality
A. Biased Data
1. Data that favors certain outcomes or groups
2. Leads to inaccurate conclusions
B. Unbiased Data
1. Fair and representative of the population
, 2. Improves reliability of analysis
C. Objective Data
1. Based on facts, not opinions
study complete Solutions | 2026 Updates | 100% Pass Guarantee
Module 1 Data Types and Structures
I. Data Generation and Collection
A. Data is created through observations, measurements, surveys, transactions,
and system activity
B. Analysts select data based on relevance to the problem or objective
C. Quality data collection is essential for accurate analysis and decision-making
II. Data Types
A. Numerical Data
1. Represents numbers
2. Used for calculations and measurements
3. Examples: age, height, revenue
B. Categorical Data
1. Represents labels or groups
2. Used for classification
3. Examples: gender, department, color
C. Boolean Data
1. Represents true/false values
2. Used for logical conditions
3. Examples: yes/no, pass/fail, active/inactive
D. Fields and Values
1. Field: category or column name
2. Value: specific entry within a field
III. Data Structures
A. Structured Data
1. Organized into rows and columns
2. Easily stored in spreadsheets and databases
3. Example: Excel tables, SQL databases
B. Unstructured Data
1. Not organized in a predefined format
2. Harder to analyze without processing
3. Examples: emails, images, videos, text documents
IV. Data Formats
A. Wide Format
1. Each variable has its own column
2. Easier for viewing and reporting
3. Common in spreadsheets
B. Long Format
1. Variables stored in fewer columns, values stacked in rows
2. Easier for statistical analysis and visualization
3. Common in data analysis tools
V. Data Integrity and Quality
A. Biased Data
1. Data that favors certain outcomes or groups
2. Leads to inaccurate conclusions
B. Unbiased Data
1. Fair and representative of the population
, 2. Improves reliability of analysis
C. Objective Data
1. Based on facts, not opinions
