Bootstrap:Data Literacy (CODAP)

Students learn about a diverse group of programmers through a short film and a gallery walk of our Pioneers in Computing and Mathematics poster series, then consider the problem solving advantages that diverse teams foster.

Students learn about Categorical and Numeric data, are introduced to Tables by way of the Animals Dataset, and consider what questions can and cannot be answered with available data.

Students explore CODAP, then discover how Numbers, Strings, and operations on those data types work in CODAP.

Students learn to generate and compare dot plots and bar charts, explore other plotting and display functions in CODAP, and (optionally) design an infographic.

Infographics are a powerful tool for communicating information, especially when made by people who understand how to connect visuals to data in meaningful ways. This project is an opportunity for students to become more flexible math thinkers while tapping into their creativity. This project supports the learning goals of our lesson on Bar Charts and Dot Plots.

Students are introduced to the Data Cycle, a four-step scaffold for answering questions from a dataset…​and then generating the next question! Students learn to identify - and ask - statistical questions, by comparing and contrasting them with other kinds of questions. This lesson optionally includes Project: Snack Habits🎨.

Students analyze their class' snacking habits in comparison with data on childhood obesity in the U.S. This project supports the learning goals of our lesson on The Data Cycle.

Students explore sampling and probability as a mechanism for detecting patterns. After exploring this in a binary system (flipping a coin), they consider the role of sampling as it applies to relationships in a dataset.

Students practice making a variety of chart types and then begin to investigate a real world dataset, which they will continue to work with for the remainder of the course.

Students choose a real world dataset that is interesting to them and practice making and interpreting a range of visualizations using that dataset. This project spans up to nine of our Data Science lessons, each of which includes an optional section with project-specific directions. We have built a Library of Datasets to support this project.

Students create and interpret dot plots, considering the distribution and typicality of the data. Students define variability multiple ways, and then describe different levels of variability that they observe on dot plots.

Students compare and contrast dot plots and histograms. Students learn to create histograms by hand and in CODAP.

Students practice reading and describing histograms, using new vocabulary to describe histogram shape.

Students explore how their understanding of histogram "shape" can help them to interpret data.

Students are introduced to mean, median and mode(s) and consider which of these measures of center best describes various quantitative data.

Students compute five-number summaries from quantitative datasets, and then use those five-number summaries to create box plots.

Students learn how standard deviation serves as Data Scientists' most common measure of "spread": how far all the values in a dataset tend to be from their mean. When we looked at box plots, we visualized spread based on range and interquartile range. Now we’ll return to histograms and picture the spread in terms of standard deviation.

Students investigate scatter plots as a method of visualizing the relationship between two quantitative variables. In the programming environment, points on the scatter plot can be labelled with a third variable!

Students deepen their understanding of scatter plots, learning to describe and interpret direction and strength of linear relationships.

Students compute the “line of best fit” using the function for linear regression, and summarize linear relationships in a dataset.

Students consider ethical issues and privacy in the context of data science.

Students learn about the importance of careful data collection, by confronting a "dirty" dataset. They then design a simple survey of their own, gather their data, and import it into Pyret. This lesson optionally includes Project: Design a Survey🎨.

Students learn two different ways to filter - by manually deleting table rows, and by using a Transformer to build and modify a copy of the table.

Students learn more about Transformers, including Transform Attribute and Build Attribute.

Students learn how to compose Transformers, which act as functions operating on tables.

Students practice creating grouped samples (non-random subsets) and think about why it might sometimes be useful to answer questions about a dataset through the lens of one group or another.

Students consider possible threats to the validity of their analysis. This lesson optionally includes the Project: When Data Science Goes Bad🎨.

Students investigate four types of threats to validity by pretending to be “bad data scientists” who fail to consider the impact of selection bias, bias in the study design, poor choice of summary data, and confounding variables. This project supports the learning goals of our lesson on Threats to Validity.

This project can be used as a capstone for Bootstrap: Data Science. It is designed to give students a deep dive into a dataset and use everything they’ve learned throughout the course, not only about making and interpreting visualizations, but about the practice of refining our questions through the Data Cycle and deciding which visualizations are most useful in telling the data’s story. This project is an extension of the Project: Dataset Exploration.