What makes Data Science Foundations challenging for students?

Data Science Foundations combines abstract statistical concepts with real-world applications, requiring strong analytical thinking and mathematical reasoning skills that students are still developing.

How are students assessed in Ohio's Data Science Foundations course?

Assessment includes project-based evaluations, statistical investigations, data analysis portfolios, and standardized testing through Ohio State Tests and End-of-Course examinations.

What career pathways does Data Science Foundations support?

The course prepares students for careers in data analysis, market research, healthcare informatics, business intelligence, scientific research, and technology sectors.

What key learning outcomes should students achieve?

Students should master descriptive statistics, probability theory, data visualization, correlation analysis, experimental design, and statistical inference methods.

How does technology integration enhance Data Science learning?

Technology tools like spreadsheets, graphing calculators, and statistical software help students visualize data patterns and perform complex calculations efficiently.

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Data Science Foundations Course - Ohio Curriculum

Explore comprehensive Data Science Foundations curriculum designed for Ohio students. Master statistical analysis, probability theory, and data interpretation through structured learning pathways that build critical thinking and analytical skills.

Ohio Data Science Foundations Course CurriculumHelp

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.