Alignments

Use data to highlight or propose cause and effect relationships, predict outcomes, or communicate an idea. [See: Introduction to Computational Data Science.]

Describe, classify and construct triangles, including equilateral, right, scalene, and isosceles triangles. Recognize triangles in various contexts. [See: Contracts.]

Use and evaluate variables in expressions, equations, and inequalities that arise from various contexts, including determining when or if, for a given value of the variable, an equation or inequality involving a variable is true or false. [See: Simple Data Types.]

Develop programs that utilize combinations of repetition, conditionals, and the manipulation of variables representing different data types. [See: If-Expressions.]

Create and analyze box and whisker plots observing how each segment contains one quarter of the data. [See: Displaying Categorical Data; Data Displays and Lookups; Grouped Samples; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Spread of a dataset.]

Collect data using computational tools and transform the data to make it more useful. [See: Spread of a dataset.]

Identify how the same data can be represented in multiple ways. [See: Displaying Categorical Data.]

Develop and use the fact that the sum of the interior angles of a triangle is 180° to determine missing angle measures in a triangle. [See: Contracts.]

Select and modify an existing algorithm in natural language or pseudocode to solve complex problems. [See: Simple Data Types; Table Methods.]

Develop programs that utilize combinations of repetition, compound conditionals, and the manipulation of variables representing different data types. [See: If-Expressions.]

Decompose problems into parts to facilitate the design, implementation, and review of increasingly complex programs. [See: Method Chaining.]

Use reasoning with proportions to display and interpret data in circle graphs (pie charts) and histograms. Choose the appropriate data display and know how to create the display using a spreadsheet or other graphing technology. [See: Displaying Categorical Data; Data Displays and Lookups; Grouped Samples; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Spread of a dataset.]

Collect data using computational tools and transform the data to make it more useful and reliable. [See: Spread of a dataset.]

Create multiple representations of data. [See: Displaying Categorical Data.]

Know that every rational number can be written as the ratio of two integers or as a terminating or repeating decimal. [See: Simple Data Types.]

Compare and order rational numbers expressed in various forms using the symbols <, >, and =. [See: Simple Data Types.]

Recognize and generate equivalent representations of rational numbers, including equivalent fractions. [See: Simple Data Types.]

Develop programs that utilize combinations of nested repetition, compound conditionals, procedures without parameters, and the manipulation of variables representing different data types. [See: Simple Data Types.]

Decompose problems and subproblems into parts to facilitate the design, implementation, and review of complex programs. [See: Method Chaining.]

Incorporate existing code, media, and libraries into original programs of increasing complexity and give attribution. [See: Defining Functions.]

Develop, implement, and refine a process that utilizes computational tools to collect and transform data to make it more useful and reliable. [See: Introduction to Computational Data Science; Grouped Samples.]

Analyze multiple methods of representing data and choose the most appropriate method for representing data. [See: Displaying Categorical Data; Data Displays and Lookups; Grouped Samples; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Spread of a dataset.]

Communicate and publish key ideas and details individually or collaboratively in a way that informs, persuades, and/or entertains using a variety of digital tools and media-rich resources. Describe and use safe, appropriate, and responsible practices (netiquette) when participating in online communities (e.g., discussion groups, blogs, social networking sites). [See: Choosing Your Dataset; Threats to Validity.]

Use knowledge of solving equations with rational values to represent and solve mathematical and real-world problems (e.g., angle measures, geometric formulas, science, or statistics) and interpret the solutions in the original context. [See: Defining Functions.]

Describe a data set using data displays, describe and compare data sets using summary statistics, including measures of central tendency, location, and spread. Know how to use calculators, spreadsheets, or other appropriate technology to display data and calculate summary statistics. [See: Grouped Samples; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data.]

Select and apply counting procedures, such as the multiplication and addition principles and tree diagrams, to determine the size of a sample space (the number of possible outcomes) and to calculate probabilities. [See: Table Methods; Defining Table Functions; Method Chaining.]

Identify the dependent and independent variables as well as the domain and range given a function, equation, or graph. Identify restrictions on the domain and range in real-world contexts. [See: Contracts.]

Write linear functions, using function notation, to model real-world and mathematical situations. [See: Contracts; Defining Functions.]

Given a graph modeling a real-world situation, read and interpret the linear piecewise function (excluding step functions). [See: Contracts.]

Evaluate reports based on data published in the media by identifying the source of the data, the design of the study, and the way the data are analyzed and displayed. Given spreadsheets, tables, or graphs, recognize and analyze distortions in data displays. Show how graphs and data can be distorted to support different points of view. [See: If-Expressions.]

Construct logical arguments to prove triangle congruence (SSS, SAS, ASA, AAS and HL) and triangle similarity (AA, SSS, SAS). [See: Contracts.]

Create a prototype that uses algorithms (e.g., searching, sorting, finding shortest distance) to provide a possible solution for a real-world problem. [See: Grouped Samples.]

Break down a solution into procedures using systematic analysis and design. [See: Defining Table Functions; Method Chaining; If-Expressions.]

Create computational artifacts by systematically organizing, manipulating and/or processing data. [See: Table Methods; Defining Table Functions; Method Chaining; If-Expressions.]

Evaluate and refine computational artifacts to make them more user-friendly, efficient and/or accessible. [See: Histograms; Visualizing the “Shape” of Data.]

Use tools and techniques to locate, collect, and create visualizations of small- and largescale data sets (e.g., paper surveys and online data sets). [See: Choosing Your Dataset.]

Show the relationships between collected data elements using computational models. [See: Scatter Plots; Correlations; Linear Regression.]

Evaluate the ways computing impacts personal, ethical, social, economic, and cultural practices. [See: Ethics and Privacy.]

Test and refine computational artifacts to reduce bias and equity deficits. [See: Randomness and Sample Size; Grouped Samples; Choosing Your Dataset; Checking Your Work; Threats to Validity.]

Explain the privacy concerns related to the large-scale collection and analysis of information about individuals (e.g., how businesses, social media, and the government collects and uses data) that may not be evident to users. [See: Ethics and Privacy.]

Evaluate the social and economic consequences of how law and ethics interact with digital aspects of privacy, data, property, information, and identity. [See: Ethics and Privacy.]

Create programming solutions by reusing existing code (e.g., libraries, Application Programming Interface (APIs), code repositories). [See: Table Methods.]

Develop programs for multiple computing platforms. [See: Defining Functions.]

Use data analysis tools and techniques to identify patterns from complex real-world data. [See: Linear Regression.]

Generate data sets that use a variety of data collection tools and analysis techniques to support a claim and/or communicate information. [See: Spread of a dataset.]

Evaluate the beneficial and harmful effects that computational artifacts and innovations have on society. [See: Ethics and Privacy.]

Debate laws and regulations that impact the development and use of software. [See: Ethics and Privacy.]

Recognize that a function is a relationship between an independent variable and a dependent variable in which the value of the independent variable determines the value of the dependent variable. [See: Contracts; Defining Functions.]

Use linear functions to represent and explain real-world and mathematical situations. [See: Defining Functions.]

Identify, describe, and analyze linear relationships between two variables. [See: Randomness and Sample Size; Grouped Samples; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data.]

Describe the impact that inserting or deleting a data point has on the mean and the median of a data set. Know how to create data displays using a spreadsheet and use a calculator to examine this impact. [See: Grouped Samples; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data.]

Explain how outliers affect measures of central tendency. [See: Measures of Center.]

Collect, display and interpret data using scatterplots. Use the shape of the scatterplot to informally estimate a line of best fit, make statements about average rate of change, and make predictions about values not in the original data set. Use appropriate titles, labels and units. [See: Scatter Plots; Correlations; Linear Regression.]

Determine how samples are chosen (random, limited, biased) to draw and support conclusions about generalizing a sample to a population. [See: Randomness and Sample Size.]