Data Analytics Foundations
by pluginagentmarketplace
Core data analytics concepts, Excel/Google Sheets fundamentals, and data collection techniques
Skill Details
Repository Files
7 files in this skill directory
name: data-analytics-foundations description: Core data analytics concepts, Excel/Google Sheets fundamentals, and data collection techniques version: "2.0.0" sasmp_version: "2.0.0" bonded_agent: 01-data-analytics-foundations bond_type: PRIMARY_BOND
Skill Configuration
config: atomic: true retry_enabled: true max_retries: 3 backoff_strategy: exponential
Parameter Validation
parameters: skill_level: type: string required: true enum: [beginner, intermediate, advanced] default: beginner focus_area: type: string required: false enum: [excel, sheets, data_quality, collection, all] default: all
Observability
observability: logging_level: info metrics: [usage_count, success_rate, completion_time]
Data Analytics Foundations Skill
Overview
Master the foundational concepts of data analytics including data types, collection methods, spreadsheet fundamentals, and basic data manipulation techniques.
Core Topics
Data Fundamentals
- Understanding data types (quantitative, qualitative, structured, unstructured)
- Data sources and collection methods
- Data quality dimensions (accuracy, completeness, consistency, timeliness)
Spreadsheet Proficiency
- Excel fundamentals and advanced formulas
- Google Sheets collaboration features
- Data cleaning and transformation in spreadsheets
- Pivot tables and data summarization
Data Collection
- Survey design and implementation
- Web scraping basics
- API data extraction
- Database querying fundamentals
Learning Objectives
- Understand core data analytics terminology and concepts
- Master Excel and Google Sheets for data analysis
- Implement effective data collection strategies
- Apply data quality assessment techniques
Error Handling
| Error Type | Cause | Recovery |
|---|---|---|
| Formula error | Invalid syntax | Validate formula structure |
| Data type mismatch | Wrong input format | Convert data types explicitly |
| Missing data | Incomplete dataset | Apply imputation or filtering |
| Performance issue | Large dataset | Use data sampling or optimization |
Related Skills
- databases-sql (for advanced data querying)
- statistics (for data analysis techniques)
- visualization (for presenting insights)
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