D1 Data Literacy: manage, analyse, and use data to make convincing arguments and informed decisions, in various contexts drawn from real life

D1.1 describe the difference between qualitative and quantitative data, and describe situations where each would be used

D1.2 collect data from different primary and secondary sources to answer questions of interest that involve comparing two or more sets of data, and organize the data in frequency tables and stem-and-leaf plots

D1.3 select from among a variety of graphs, including multiple-bar graphs, the type of graph best suited to represent various sets of data; display the data in the graphs with proper sources, titles, and labels, and appropriate scales; and justify their choice of graphs

D1.4 create an infographic about a data set, representing the data in appropriate ways, including in frequency tables, stem-and-leaf plots, and multiple-bar graphs, and incorporating any other relevant information that helps to tell a story about the data 

C4 apply the process of mathematical modelling to represent, analyse, make predictions, and provide insight into real-life situations

• Mathematical Modelling is a key process expectation that connects across multiple strands. Opportunities to engage students in modelling may arise naturally within rich, real-world contexts — for example, in financial literacy (e.g., creating a budget), measurement (e.g., designing a garden space), or data (e.g., interpreting results from a student survey).

• We recommend using open-ended tasks where students define problems, make decisions, and justify their thinking — even in informal ways — as early steps toward developing modelling skills.

• See our Mathematical Modelling page for more information.

Process Expectation Focus: Communicating, Representing