Bootstrap:Data Science 🖼Show image
(Also available in CODAP)
Lesson Plans
 Introduction to Computational 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, explorings how Numbers, Strings, Booleans and operations on those data types work in this programming language.
 Contracts

Students learn how to apply Functions in the programming environment and interpret the information contained in Contracts: Name, Domain and Range. Imageproducing functions provide an engaging context for this exploration.
 Displaying Categorical Data

Students learn to generate and compare pie charts & bar charts, explore other plotting & display functions, and (optionally) design an infographic.
 Data Displays and Lookups

Students use displays to answer questions, focusing on which displays make sense for the data they are working with. They also learn how to extract individual rows from a table, and columns from a row.
 Table Methods

Students learn about table methods, which allow them to order, filter, and build columns to extend the animals table.
 Defining Functions

Students discover that they can make their own functions, and learn to connect function descriptions across three representations: Contracts (a mapping between Domain and Range), Examples (a list of discrete inputs and outputs), and Definitions (symbolic).
 Defining Table Functions

Students use the Design Recipe to define operations on tables, developing a structured approach to answering questions by transforming tables.
 Method Chaining

Students learn how to chain Methods together, and define more sophisticated subsets.
 IfExpressions

Imagescatterplots expose deeper insight into subgroups within a population, motivating the need for more advanced analysis and adding ifexpressions to students' programming toolkit.
 Randomness and Sample Size

Students learn about random samples and statistical inference, as applied to the Animals Dataset. In the process, students get a light introduction to the role of sample size and the importance of statistical inference.
 Grouped Samples

Students practice creating subsets and think about why it might sometimes be useful to answer questions about a dataset through the lens of specific subsets.
 Choosing Your Dataset

Students select a real world dataset to investigate for the remainder of the course. They begin their analysis by identifying categorical and quantitative columns, and defining a few random and logical subsets.
 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.
 Spread of a dataset

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.
 Checking Your Work

Students consider the concept of trust and testing — how do we know if a particular analysis is trustworthy?
 Scatter Plots

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

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

Students consider possible threats to the validity of their analysis.
 All the lessons

This is a single page that contains all the lessons listed above.
Ordering Student Workbooks?
While we give our workbooks away as a PDF, we understand that printing them yourself can be expensive! You can purchase beautifullybound copies of the student workbook from Lulu.com. Click here to order.
Teaching Remotely?
If you’re teaching remotely, we’ve assembed an Implementation Notes page that makes specific recommendations for inperson v. remote instruction.
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.

Glossary — A list of vocabulary words used in this pathway.

Standards Alignment — Find out how our materials align with National and State Standards, as well as some of the most commonly used math textbooks.

Workbook — Sometimes, the best way for students to get real thinking done is to step away from the keyboard! Our lesson plans are tightly integrated with the Student Workbook, allowing for paperandpencil practice and activities that don’t require a computer.

TeacherOnly Resources — We also offer several teachersonly materials, including an answer key to the student workbook, keys to all the exercises, and pre and posttests 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 uptodate 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.
These materials were developed partly through support of the National Science Foundation, (awards 1042210, 1535276, 1648684, and 1738598). 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.