Data Science · Data in Context
Virginia SOL DS.2
Virginia SOL DS.2 is part of the Data in Context strand in Data Science (Math). Under this Standards of Learning objective, students design a data project plan aligned with the data science cycle, including sampling methodology. Below is what DS.2 covers in plain language, the specific skills it is assessed on, the key concepts to review, and how to practice DS.2 for the Virginia SOL test.
What SOL DS.2 means
Design a data project plan aligned with the data science cycle, including sampling methodology.
Skills you’ll practice for DS.2
- Design a data project plan with components such as project goals, parameters, stakeholders, timeline, KPIs, resources, and limitations.
- Select and justify a sampling technique (simple random, systematic, stratified, cluster) for a data project.
- Identify bias considerations in the sampling process of a data project.
Key concepts covered by DS.2
- data project plan
- project design
- KPIs
- stakeholder analysis
- sampling techniques
- simple random sampling
- systematic sampling
- stratified sampling
- cluster sampling
- sampling bias
- bias mitigation
- data sampling considerations
How to study and practice SOL DS.2
Start with a quick diagnostic to see whether DS.2 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 in Context
- DS.1 — Identify problems suitable for data-driven solutions and define the stages of the data science cycle.
Frequently asked questions about SOL DS.2
What is Virginia SOL DS.2?
SOL DS.2 is a Data Science Standard of Learning in the Data in Context strand. It expects students to design a data project plan aligned with the data science cycle, including sampling methodology.
What skills does SOL DS.2 cover?
SOL DS.2 is assessed on 3 skills: design a data project plan with components such as project goals, parameters, stakeholders, timeline, KPIs, resources, and limitations; select and justify a sampling technique (simple random, systematic, stratified, cluster) for a data project; identify bias considerations in the sampling process of a data project.
What strand of Data Science is SOL DS.2 in?
SOL DS.2 belongs to the Data in Context reporting strand of the Data Science Virginia Standards of Learning.
How do I study and practice for SOL DS.2?
Start with a diagnostic to see whether DS.2 is already solid, then work the 3 skills above with guided notes, flashcards, and SOL-style practice questions. For official released items, see the Virginia SOL practice tests guide.