Data Science SOL Prep

Data in Context

• DS.1 — Identify problems suitable for data-driven solutions and define the stages of the data science cycle.
• DS.2 — Design a data project plan aligned with the data science cycle, including sampling methodology.

Data Bias

• DS.3 — Recognize data literacy and identify biases in existing analyses and visualizations.
• DS.4 — Identify biases in data collection and understand implications and privacy issues.

Data and Communication

• DS.5 — Use storytelling to communicate data science project results effectively.
• DS.6 — Justify the design, use, and effectiveness of different data visualizations.

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.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.

Data and Computing

• DS.12 — Select and utilize technological tools to process and prepare data for analysis.
• DS.13 — Select and utilize technological tools to analyze and communicate data effectively.

What is on the Data Science SOL test?

Data Science is organized into 5 strands: Data in Context, Data Bias, Data and Communication, Data Modeling, Data and Computing.

How many standards does Data Science cover?

Data Science covers 13 individual Virginia SOL standards across its 5 strands.