How does the Common Core Data Science curriculum prepare students for college?

The curriculum focuses on analytical skills, statistical methods, and data interpretation, providing a strong foundation for college-level STEM courses and data-driven research across disciplines.

What technology skills will students develop in this course?

Students will learn to use statistical software, create data visualizations, and interpret complex datasets, developing crucial tech skills for modern careers in data analysis and research.

How is student progress assessed in the Data Science curriculum?

Assessment includes project-based work, data analysis reports, statistical modeling tasks, and traditional exams that evaluate both conceptual understanding and practical application skills.

What career paths does this curriculum support?

This curriculum prepares students for careers in data science, analytics, research, economics, and other fields that require strong quantitative and analytical skills.

How does the curriculum integrate with other math subjects?

The Data Science curriculum builds on algebraic and geometric concepts, applying them to real-world data analysis and statistical modeling, reinforcing core math skills.

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High School Math Courses - Common Core Data Science Curriculum

Discover our comprehensive high school data science curriculum aligned with Common Core standards. Explore statistics, probability, and data analysis to prepare for college and careers in STEM fields.

Common Core High School Math Curriculum - Data ScienceHelp

Common Core ID

Standard

StudyPug Topic

CC.HSN.Q.A.1

Use units as a way to understand problems and to guide the solution of multi-step problems; choose and interpret units consistently in formulas; choose and interpret the scale and the origin in graphs and data displays.

CC.HSN.Q.A.2

Define appropriate quantities for the purpose of descriptive modeling.

CC.HSN.Q.A.3

Choose a level of accuracy appropriate to limitations on measurement when reporting quantities.

CC.HSA.SSE.A.1

Interpret expressions that represent a quantity in terms of its context.

CC.HSA.CED.A.1

Create equations and inequalities in one variable and use them to solve problems. Include equations arising from linear and quadratic functions, and simple rational and exponential functions.

CC.HSA.CED.A.2

Create equations in two or more variables to represent relationships between quantities; graph equations on coordinate axes with labels and scales.

CC.HSA.REI.D.11

CC.HSF.IF.A.2

Use function notation, evaluate functions for inputs in their domains, and interpret statements that use function notation in terms of a context.

CC.HSF.IF.B.4

For a function that models a relationship between two quantities, interpret key features of graphs and tables in terms of the quantities, and sketch graphs showing key features given a verbal description of the relationship.

CC.HSF.IF.B.6

Calculate and interpret the average rate of change of a function (presented symbolically or as a table) over a specified interval. Estimate the rate of change from a graph.

CC.HSF.BF.A.1

Write a function that describes a relationship between two quantities.

CC.HSF.LE.A.1

Distinguish between situations that can be modeled with linear functions and with exponential functions.

CC.HSF.LE.B.5

Interpret the parameters in a linear or exponential function in terms of a context.

CC.HSS.ID.A.1

Represent data with plots on the real number line (dot plots, histograms, and box plots).

CC.HSS.ID.A.2

Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets.

CC.HSS.ID.A.3

Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).

CC.HSS.ID.A.4

Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages. Recognize that there are data sets for which such a procedure is not appropriate. Use calculators, spreadsheets, and tables to estimate areas under the normal curve.

CC.HSS.ID.B.5

Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data.

CC.HSS.ID.B.6

Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.

CC.HSS.ID.C.7

Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.

CC.HSS.IC.A.1

Understand statistics as a process for making inferences about population parameters based on a random sample from that population.

CC.HSS.IC.A.2

Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation.

CC.HSS.IC.B.3

Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each.

CC.HSS.IC.B.4

Use data from a sample survey to estimate a population mean or proportion; develop a margin of error through the use of simulation models for random sampling.

CC.HSS.IC.B.5

Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.

CC.HSS.IC.B.6

Evaluate reports based on data.

CC.HSS.CP.A.1

Describe events as subsets of a sample space (the set of outcomes) using characteristics (or categories) of the outcomes, or as unions, intersections, or complements of other events ("or," "and," "not").

CC.HSS.CP.A.2

Understand that two events A and B are independent if the probability of A and B occurring together is the product of their probabilities, and use this characterization to determine if they are independent.

CC.HSS.MD.A.2

Calculate the expected value of a random variable; interpret it as the mean of the probability distribution.