Data Science · Data Modeling
Virginia SOL DS.8
Virginia SOL DS.8 is part of the Data Modeling strand in Data Science (Math). Under this Standards of Learning objective, students acquire and prepare big datasets for modeling and analysis. Below is what DS.8 covers in plain language, the specific skills it is assessed on, the key concepts to review, and how to practice DS.8 for the Virginia SOL test.
What SOL DS.8 means
Acquire and prepare big datasets for modeling and analysis.
Skills you’ll practice for DS.8
- Explain pros and cons of collecting data versus acquiring it from existing sources.
- Apply matrix operations (sort, select, filter, replace) to wrangle data using algebraic methods with technology tools.
- Clean data using technology tools.
- Format and enrich data for analysis.
- Combine and store data efficiently.
- Read data from different sources for preparation and analysis.
- Identify important parameters of a big dataset based on context.
- Document the process of ingesting, formatting, and cleaning data for decision-making.
Key concepts covered by DS.8
- data collection
- data acquisition
- pros and cons
- matrix operations
- data wrangling
- sorting
- filtering
- replacement
- data cleaning
- data preprocessing
- data scrubbing
- data formatting
- data enrichment
- data preparation
- data combination
- data storage
- data merging
- data reading
- data sources
- data ingestion
- dataset parameters
- big data analysis
- contextual parameters
- data documentation
- ingestion process
- cleaning process
- decision-making
How to study and practice SOL DS.8
Start with a quick diagnostic to see whether DS.8 is already solid, then work each skill above with guided notes, flashcards, and SOL-style practice questions. For official released items, see our Virginia SOL practice tests guide and how to study for the SOL test.
Related Data Science standards in Data Modeling
- DS.7 — Assess the reliability of source data for mathematical modeling.
- DS.9 — Select and analyze data models to make predictions while assessing accuracy and uncertainty.
- DS.10 — Summarize and interpret data in conventional and emerging visualizations.
- DS.11 — Select statistical models and tests to extract actionable knowledge from data.
Frequently asked questions about SOL DS.8
What is Virginia SOL DS.8?
SOL DS.8 is a Data Science Standard of Learning in the Data Modeling strand. It expects students to acquire and prepare big datasets for modeling and analysis.
What skills does SOL DS.8 cover?
SOL DS.8 is assessed on 8 skills: explain pros and cons of collecting data versus acquiring it from existing sources; apply matrix operations (sort, select, filter, replace) to wrangle data using algebraic methods with technology tools; clean data using technology tools; format and enrich data for analysis; combine and store data efficiently; read data from different sources for preparation and analysis; identify important parameters of a big dataset based on context; document the process of ingesting, formatting, and cleaning data for decision-making.
What strand of Data Science is SOL DS.8 in?
SOL DS.8 belongs to the Data Modeling reporting strand of the Data Science Virginia Standards of Learning.
How do I study and practice for SOL DS.8?
Start with a diagnostic to see whether DS.8 is already solid, then work the 8 skills above with guided notes, flashcards, and SOL-style practice questions. For official released items, see the Virginia SOL practice tests guide.