OhioData Science Foundations
Ohio Data Science Foundations Curriculum
Video lessons and practice for every Data Science Foundations topic. Aligned to Ohio's Learning Standards Math for high school students.
Ohio Data Science Foundations | StudyPugHelp
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 | Explain why the x-coordinates of the points where the graphs of the equations y = f(x) and y = g(x) intersect are the solutions of the equation f(x) = g(x); find the solutions approximately, e.g., using technology to graph the functions, make tables of values, or find successive approximations. Include cases where f(x) and/or g(x) are linear, polynomial, rational, absolute value, exponential, and logarithmic functions. |
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. |
Data Science Foundations in Ohio High School
Data Science Foundations is a high school math course designed to build students' ability to work with real-world data. Ohio high school students in this course learn how to interpret quantities, create mathematical models, analyze statistical data, and reason about probability. All topics align to Ohio's Learning Standards Math.
Key Topics in This Course
- Using units to understand and solve multi-step problems
- Creating equations and inequalities in one or more variables
- Understanding and using function notation to model relationships
- Interpreting key features of graphs, tables, and scatter plots
- Calculating average rate of change and estimating from graphs
- Comparing data distributions using mean, median, standard deviation, and IQR
- Fitting data to a normal distribution and estimating population percentages
- Summarizing categorical data in two-way frequency tables
- Interpreting slope and intercept of linear models in context
- Computing and interpreting correlation coefficients using technology
- Distinguishing between correlation and causation
- Making inferences about populations from random samples
- Evaluating sample surveys, experiments, and observational studies
- Understanding independence and calculating expected value of random variables
How StudyPug Helps Ohio Students
StudyPug provides video lessons and practice problems for every topic in Data Science Foundations. Each lesson is broken into short segments of 5–15 minutes so students can learn at their own pace. Students can pause, replay, and practice as many times as needed to fully understand a concept before moving on.
Whether your child is preparing for an upcoming test, catching up after missing class, or trying to get ahead, StudyPug makes it easy to find exactly the topic they need and start learning right away.
Aligned to Ohio's Learning Standards Math
Every topic in StudyPug's Data Science Foundations course is aligned to Ohio's Learning Standards Math. This means students are always practicing skills that match what their Ohio school is teaching. From interpreting statistical models to evaluating probability, every lesson connects directly to the standards Ohio teachers use in the classroom.