These materials contain minimal programming content, and are appropriate for teachers of middle-school and 9th grade Data Science classes, or for those who wish to integrate Data Science into their existing Math, Science, or History classes.

For more advanced courses we recommend our Data Science materials.

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.

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 a programming language. Booleans offer an excellent opportunity for students to explore the meaning and real-world uses of inequalities.

Contracts: Making Tables and Displays

Students learn about functions for sorting and counting data in tables, then are introduced to one-variable displays.

Bar and Pie Charts

Students use data displays like bar and pie charts to create 1- and 2-level groupings to visualize the distribution of categorical data.

Project: Make 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 and Pie Charts.

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.

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.

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.

Project: Dataset Exploration

Students choose a real world dataset that is interesting to them and practice making and interpreting a range of displays 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.

Histograms

Students are introduced to Histograms by comparing them to bar charts, learning to construct them by hand and in the programming environment.

Visualizing the "Shape" of Data

Students explore the concept of "shape", using histograms to determine whether a dataset has skewness, and what the direction of the skewness means. They apply this knowledge to the Animals Dataset, and then to their own.

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.

Box Plots

Students are introduced to box plots, learn to evaluate the spread of a quantitative column, and deepen their perspective on shape by matching box plots to histogram.

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.

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!

Linear Regression

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

Ethics, Privacy, and Bias

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

Collecting Data

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

Project: Design a Survey

Students come up with a research question and design a survey to gather data to answer it. They exchange surveys to get some hands-on practice with clean and dirty data and incorporate what they learn to polish their surveys. This project supports the learning goals of our lesson on Collecting Data.

Threats to Validity

Students consider possible threats to the validity of their analysis.

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.

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.