VA High School Data Science Curriculum
Give your student the support they need to succeed in Virginia high school Data Science. Our lessons and practice problems follow what local schools teach, from data analysis to probability.
About High School Virginia Data Science
Virginia high school Data Science follows the Virginia Standards of Learning (SOL), covering quantitative reasoning, statistical modeling, and data interpretation. This course works through units including data representation, descriptive statistics, linear and exponential modeling, two-variable analysis, statistical inference, probability, and expected value. StudyPug provides step-by-step video lessons and practice problems aligned to every strand, so students can get help exactly where they need it.
- Strands: quantities and modeling, descriptive statistics, bivariate data and linear models, statistical inference, and probability
- Video lessons for every Data Science topic in the Virginia SOL curriculum
- Practice problems with instant feedback and step-by-step solutions
- Full alignment to Virginia high school Data Science SOL standards
Curriculum topics by strand
Browse lessons and practice problems organized by strand, aligned to the Virginia Standards of Learning for high school Data Science.
VA High School Data Science Topics
- Quantities and Modeling: Using units to understand multi-step problems, choosing appropriate scales, defining descriptive quantities, and selecting measurement accuracy for reporting.
- Expressions, Equations, and Functions: Interpreting expressions in context, creating equations and inequalities in one or more variables, graphing on coordinate axes, and analyzing function notation and key graph features.
- Linear and Exponential Modeling: Writing functions to describe relationships, distinguishing linear from exponential models, interpreting parameters, and calculating average rate of change.
- Descriptive Statistics: Representing data with dot plots, histograms, and box plots; comparing center and spread; identifying outliers; fitting data to a normal distribution; and summarizing two-way frequency tables.
- Bivariate Data and Linear Models: Building scatter plots, interpreting slope and intercept in context, computing and interpreting correlation coefficients, and distinguishing correlation from causation.
- Statistical Inference and Sampling: Understanding inference from random samples, comparing sample surveys to experiments and observational studies, estimating population means and proportions, and evaluating data reports.
- Probability and Expected Value: Describing events as subsets of a sample space, determining independence, and calculating and interpreting expected value as the mean of a probability distribution.
How StudyPug Helps Virginia High School Data Science Students
When a student gets stuck on a concept like correlation coefficients or the normal distribution, StudyPug provides short, focused video lessons that walk through each step of the problem. Students can pause and rewatch as many times as they need, then test their understanding with practice problems that give instant feedback.
Because every lesson is organized by strand and aligned to the Virginia Standards of Learning, students can quickly find the exact topic their teacher covered that day - whether it is two-way frequency tables, margin of error, or distinguishing causation from correlation.
Aligned to Virginia High School Data Science Curriculum
All StudyPug content for this course is built to match the Virginia Standards of Learning (SOL) for high school Data Science, covering every strand from quantitative reasoning and statistical modeling to probability and inference, so students are always practicing what their school curriculum requires.
Written by the StudyPug Math Team
Last updated June 5, 2026
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