Data Science · Data Modeling
Virginia SOL DS.9
Virginia SOL DS.9 is part of the Data Modeling strand in Data Science (Math). Under this Standards of Learning objective, students select and analyze data models to make predictions while assessing accuracy and uncertainty. Below is what DS.9 covers in plain language, the specific skills it is assessed on, the key concepts to review, and how to practice DS.9 for the Virginia SOL test.
What SOL DS.9 means
Select and analyze data models to make predictions while assessing accuracy and uncertainty.
Skills you’ll practice for DS.9
- Identify factors contributing to dataset behavior (e.g., true values, bias, noise).
- Fit models (e.g., univariate, bivariate) to data for prediction.
- Distinguish between linear and nonlinear associations using visualizations.
- Identify overly complex models that fit random noise, reducing predictive accuracy.
- Use regression techniques to select optimal features.
- Recognize implications of removing features from a model.
- Select the optimal model for a dataset using technological tools.
Key concepts covered by DS.9
- dataset behavior
- true values
- bias
- noise
- data factors
- model fitting
- univariate models
- bivariate models
- prediction
- linear associations
- nonlinear associations
- visualization analysis
- overfitting
- model complexity
- predictive accuracy
- noise fitting
- regression techniques
- feature selection
- optimal features
- feature removal
- model implications
- feature impact
- optimal model selection
- model evaluation
- technological tools
How to study and practice SOL DS.9
Start with a quick diagnostic to see whether DS.9 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.8 — Acquire and prepare big datasets for modeling and analysis.
- 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.9
What is Virginia SOL DS.9?
SOL DS.9 is a Data Science Standard of Learning in the Data Modeling strand. It expects students to select and analyze data models to make predictions while assessing accuracy and uncertainty.
What skills does SOL DS.9 cover?
SOL DS.9 is assessed on 7 skills: identify factors contributing to dataset behavior (e.g., true values, bias, noise); fit models (e.g., univariate, bivariate) to data for prediction; distinguish between linear and nonlinear associations using visualizations; identify overly complex models that fit random noise, reducing predictive accuracy; use regression techniques to select optimal features; recognize implications of removing features from a model; select the optimal model for a dataset using technological tools.
What strand of Data Science is SOL DS.9 in?
SOL DS.9 belongs to the Data Modeling reporting strand of the Data Science Virginia Standards of Learning.
How do I study and practice for SOL DS.9?
Start with a diagnostic to see whether DS.9 is already solid, then work the 7 skills above with guided notes, flashcards, and SOL-style practice questions. For official released items, see the Virginia SOL practice tests guide.