Bootstrap:Data Literacy (Pyret)

Looking for the Data Literacy materials for CODAP?

Lesson Plans

Computing Needs All Voices: 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.
Ethics, Privacy, and Bias: Students consider ethical issues and privacy in the context of data science.
Introduction to Data Science: Students learn about Categorical and Quantitative data, are introduced to Tables by way of the Animals Dataset, and consider what questions can and cannot be answered with available data.
Simple Data Types: Students begin to program, exploring how Numbers, Strings, Booleans and operations on those data types work in Pyret. Booleans offer an excellent opportunity for students to explore the meaning and real-world uses of inequalities.
Contracts for Strings and Images: Students encounter a useful representation of functions called a "Contract", which specifies the Name, Domain and Range of a function. Students learn how useful this representation is when trying to apply Functions in the programming environment, using image-producing functions to provide an engaging context for this exploration.
Contracts for Tables and Rows: Students learn about functions for sorting and counting data in tables, as well as extracting rows.
Contracts for Data Visualization: Students learn about functions that create data visualizations.
Bar and Pie Charts: Students use data visualizations like bar and pie charts to create 1- and 2-level groupings to visualize the distribution of categorical data. This lesson optionally includes Project: Make an Infographic🎨 .
Dot Plots: 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.
From Dot Plots to Histograms: Students compare and contrast dot plots and histograms. Students learn to create histograms by hand and in Pyret.
Histograms: Visualizing "Shape": Students practice reading and describing histograms, using new vocabulary to describe histogram shape.
Data Collection: 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🎨.
Probability, Inference, and Sample Size: 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.
The Data Cycle: 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🎨.
Project: Dataset Exploration: 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.
Choosing Your 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.
Scatter Plots: 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!
Measures of Center: Students are introduced to mean, median and mode(s) and consider which of these measures of center best describes various quantitative data.
Histograms: Interpreting "Shape": Students explore how their understanding of histogram "shape" can help them to interpret data.
Introduction to Box Plots: Students compute five-number summaries from quantitative datasets, and then use those five-number summaries to create box plots.
Box Plots: Interpreting Spread: Students learn to use box plots to describe the spread of a quantitative column, and then deepen their perspective on shape by matching box plots to histograms.
Standard Deviation: 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.
Correlations: Students deepen their understanding of scatter plots, learning to describe and interpret direction and strength of linear relationships.
Linear Regression: Students compute the “line of best fit” using the function for linear regression, and summarize linear relationships in a dataset.
Threats to Validity: Students consider possible threats to the validity of their analysis. This lesson optionally includes the Project: When Data Science Goes Bad🎨.

Projects Available for this Course

Make an Infographic , Design a Survey , Snack Habits , Time Use , Research Capstone , Dataset Exploration , When Data Science Goes Bad

Student Workbooks

Sometimes, the best place for students to get real thinking done is away from the keyboard! Our lesson plans are tightly integrated with a detailed Student Workbook, allowing for paper-and-pencil practice and activities that don’t require a computer. That’s why we provide a free PDF of the core workbook, as well as a link to the book with every optional exercise included.

Of course, we understand that printing them yourself can be expensive! Click here to purchase beautifully-bound copies of the student workbook from Lulu.com.

Other Resources

Of course, there’s more to a curriculum than software and lesson plans! We also provide a number of resources to educators, including standards alignment, a complete student workbook, an answer key for the programming exercises and a forum where they can ask questions and share ideas.

Contracts Reference — Complete student-facing documentation for all the functions used in these lessons (also printed in the back of the student workbook).
Glossary — A list of vocabulary words used in this pathway. We also provide a bilingual glossary, which defines all vocabulary words across our lessons in English and Spanish.
Standards Alignment — Find out how our materials align with National and State Standards, as well as some of the most commonly used math textbooks.
Teacher-Only Resources — We also offer several teachers-only materials, including an answer key to the student workbook, keys to all the exercises, and pre- and post-tests for teachers who are participating in our research study. For access to these materials, please fill out the password request form. We’ll get back to you soon with the necessary login information.
Online Community (Discourse) — Want to be kept up-to-date about Bootstrap events, workshops, and curricular changes? Want to ask a question or pose a lesson idea for other Bootstrap teachers? These forums are the place to do it.

Going Further

This pathway is designed to minimize the math and programming requirements, focusing primarily on data visualization, middle-school statistics, ethics and social impacts. If your students are ready for functions (typically delivered in 8th or 9th grade), lessons like Functions Make Life Easier! and Functions: Contracts, Examples & Definitions open up opportunities for filtering rows and building columns, or advanced data visualizations.

These lessons - and much more - are available in our full Data Science pathway, which includes everything from Bootstrap:Data Literacy and builds to functions and advanced data science.

You can also pick-and-choose the lessons you want, using our ../.././customPathway.shtml tool!

These materials were developed partly through support of the National Science Foundation, (awards 1042210, 1535276, 1648684, 1738598, 2031479, and 1501927). Bootstrap by the Bootstrap Community is licensed under a Creative Commons 4.0 Unported License. This license does not grant permission to run training or professional development. Offering training or professional development with materials substantially derived from Bootstrap must be approved in writing by a Bootstrap Director. Permissions beyond the scope of this license, such as to run training, may be available by contacting contact@BootstrapWorld.org.