Teacher Resources for Data Science

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Essentials

Implementation Options [link] Bootstrap:Data Science was designed to be flexible, allowing for implementations in Middle and High School ranging from 1-week exposures to full-year courses! Find recommendations for the course structure that’s right for you.
Online Community (Discourse) [link] - Talk with other Bootstrap teachers, ask questions, and share out ideas!
Collection of Bootstrap:Data Science Desmos Activities [link] - All of our Desmos Data Science lessons, in one place.
Contracts [link] - A PDF of the contracts pages from the back of the student workbook.
Glossary [link] - All of the vocabulary words (and their definitions) used in this pathway.
Bilingual Glossary of Bootstrap Terms (English-Spanish) [PDF] - For teachers with ELL/ESL students, we provide a bilingual glossary for all of the terms used across our curriculum library.

Starter Files

Many of our lessons have their own starter files, linked from the materials section of each lesson plan. Below is a collated list of all the starter files from across the curriculum.

Projects

Working in pairs, students complete different projects as they explore their own datasets. These datasets can be collected by students themselves, or chosen from our library.

Single-Lesson Projects

Making an Infographic [rubric] Students develop a ratio statement from a dataset of their choice, and then illustrate it via a compelling infographic.
Food Habits [rubric] Students analyze their snacking habits in comparison with data on childhood obesity in the U.S.
Time Use [rubric] Students investigate the amount of time they spend interacting with technology and doing homework as compared with other Americans.
Design a Survey [rubric] 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.
Design Recipe Telephone [rubric]
Olympic Records [rubric] Students use scatter plots and linear regression to analyze data about olympic records in running, swimming, or speed skating.
Threats to Validity [rubric] Students pretend to be terrible data scientists who develop and support claims based on faulty sampling techniques (selection bias, bias in the study design, poor choice of summary data, and confounding variables).

Multi-Lesson, Capstone Projects

Exploration Paper - Students begin by exploring their dataset, generating charts and coming up with possible research questions. The end product is the Dataset Exploration. You can find the description of this project (and the grading rubric!) at the Exploration Paper page.
Research Paper - Once students have come up with a research question, they can continue on to the complete Research Paper. You can find the description of this project (and the grading rubric!) at the Research Paper page.
Datasets to Choose From - Students can import their own data into a Blank Starter File following instruction in this tutorial video or choose from one of the many provided datasets below:

Looking for a shorter list? We’ve starred a few good beginner datasets.

The Environment & Health

Global Waste by Country 2019: Dataset Starter File
World Cities' Proximity to the Ocean: Dataset Starter File
Earthquakes: Dataset Starter File
Air Quality, Pollution Sources & Health in the U.S.: Dataset Starter File
Health by U.S. County: Dataset Starter File
COVID in the U.S. by County: Dataset Starter File
Arctic Sea Ice: Dataset Starter File

Politics

Countries of the World: Dataset Starter File
Gerry Mandering: Dataset Starter File
Marijuana Laws & Arrests by State 2018: Dataset Starter File
LAPD Arrests 2010-2019: Dataset Starter File
NYPD Stop, Search & Frisk 2019: Dataset Starter File
Refugees 2018: Dataset Starter File
State Demographics: Dataset Starter File
U.S. Income: Dataset Starter File
U.S. Jobs: Dataset Starter File
U.S. Voter Turnout 2016: Dataset Starter File

Sports

Esports Earnings: Dataset Starter File
MLB Hitting Stats: Dataset Starter File
NBA Players: Dataset Starter File
NFL Passing: Dataset Starter File
NFL Rushing: Dataset Starter File

Entertainment

★Movies: Dataset Starter File
IGN video game Reviews: Dataset Starter File
International Exhibition of Modern Art: Dataset Starter File
North American Pipe Organs: Dataset Starter File
Pokemon: Dataset Starter File
Music: Dataset Starter File

Education

College Majors: Dataset Starter File
U.S. Colleges 2019-2020: Dataset Starter File
★R.I. Schools: Dataset Starter File
Evolution of College Admissions in California: Dataset Starter File

Nutrition

Soda, Coffee & Other Drinks: Dataset Starter File
Fast Food Nutrition: Dataset Starter File

Would you like to contribute a dataset of your own, or is there something you’d like to change about one of ours?

We have compiled some Notes on our provided datasets, to help you decide which might be most useful in your classroom.

Exercises and Solutions

Most exercises are part of the Student Workbook, and we provide password-protected Workbook Solutions as well.

You can find the 'exercise' and 'solution' versions of all supplemental materials as well, in the lists below.

Computing Needs All Voices

Computing Needs All Voices! [ exercise : solution ]
Notice and Wonder [ exercise : solution ]
Windows and Mirrors [ exercise : solution ]
Reflection: Problem Solving Advantages of Diverse Teams [ exercise : solution ]

Introduction to Data Science

Opening Questions [ exercise : solution ]
What Questions Can You Answer with the Given Data? [ exercise : solution ]
The Animals Dataset [ exercise : solution ]
Questions and Column Descriptions [ exercise : solution ]
Categorical or Quantitative? [ exercise : solution ]

Simple Data Types

Numbers and Strings [ exercise : solution ]
Booleans [ exercise : solution ]

Contracts

Applying Functions [ exercise : solution ]
Domain and Range Frayer model [ exercise : solution ]
Function and Variable Frayer model. [ exercise : solution ]
Practicing Contracts: Domain & Range [ exercise : solution ]
Matching Expressions and Contracts [ exercise : solution ]
Contracts [ exercise : solution ]
Using Contracts [ exercise : solution ]
Using Contracts (continued) [ exercise : solution ]
Triangle Contracts [ exercise : solution ]
Triangle Contracts (SAS & ASA) [ exercise : solution ]
Radial Star [ exercise : solution ]
Star Polygon [ exercise : solution ]

Bar and Pie Charts

Bar & Pie Chart - Notice and Wonder [ exercise : solution ]
Pie Chart - Notice and Wonder [ exercise : solution ]
Bar Chart - Notice and Wonder [ exercise : solution ]
Matching Bar Charts to Pie Charts [ exercise : solution ]
Exploring Displays [ exercise : solution ]
(More) Exploring Displays [ exercise : solution ]
What Display Goes with Which Kind of Data? [ exercise : solution ]

The Data Cycle

Which Question Type? [ exercise : solution ]
Question Types: Animals [ exercise : solution ]
Data Cycle: Consider Data [ exercise : solution ]
Data Cycle: Analyzing with Displays [ exercise : solution ]

Probability, Inference, and Sample Size

Finding the Trick Coin [ exercise : solution ]
Sampling and Inference [ exercise : solution ]
Predictions from Samples [ exercise : solution ]

Choosing Your Dataset

Consider and Analyze [ exercise : solution ]
My Dataset [ exercise : solution ]
Data Cycle: Shape of My Dataset [ exercise : solution ]

Histograms

Summarizing Columns [ exercise : solution ]
Making Histograms [ exercise : solution ]
Reading Histograms [ exercise : solution ]

Visualizing the “Shape” of Data

Identifying Shape - Histograms [ exercise : solution ]
Data Cycle: Shape of the Animals Dataset [ exercise : solution ]
Outliers: Should they Stay or Should they Go? [ exercise : solution ]
Data Cycle: Shape of My Dataset [ exercise : solution ]

Measures of Center

Summarizing Columns in the Animals Dataset [ exercise : solution ]
Data Cycle: Measures of Center [ exercise : solution ]
Critiquing Written Findings [ exercise : solution ]

Box Plots

Box Plot Vocab Concept Map [ exercise : solution ]
Identifying Shape - Box Plots [ exercise : solution ]
Matching Box Plots to Histograms [ exercise : solution ]
Matching Box Plots to Histograms [ exercise : solution ]
Data Cycle: Shape of My Dataset [ exercise : solution ]
Shape of My Dataset [ exercise : solution ]

Standard Deviation

Computing Standard Deviation [ exercise : solution ]
Computing Standard Deviation (2) [ exercise : solution ]
The Effect of an Outlier [ exercise : solution ]
Matching Mean & Standard Deviation to Data [ exercise : solution ]
Data Cycle: Standard Deviation in the Animals Dataset [ exercise : solution ]
Data Cycle: Standard Deviation in My Dataset [ exercise : solution ]

Scatter Plots

Creating a Scatter Plot [ exercise : solution ]
Data Cycle: Relationships in the Animals Dataset [ exercise : solution ]
Data Cycle: Relationships in Your Dataset [ exercise : solution ]

Ethics, Privacy, and Bias

Case Study: Ethics, Privacy, and Bias [ exercise : solution ]

Collecting Data

Analyzing Survey Results When Data is Dirty [ exercise : solution ]
Dirty Data! [ exercise : solution ]
Bad Questions Make Dirty Data [ exercise : solution ]

Row and Column Lookups

Defining Rows [ exercise : solution ]
Lookup Questions [ exercise : solution ]
More Practice with Lookups [ exercise : solution ]

Functions Make Life Easier!

The Great gt domain debate! [ exercise : solution ]
Let’s Define Some New Functions! [ exercise : solution ]
Let’s Define Some More New Functions! [ exercise : solution ]
Describe and Define Your Own Functions! [ exercise : solution ]

Functions: Contracts, Examples & Definitions

Mapping Examples with Circles of Evaluation [ exercise : solution ]
Matching Examples and Contracts [ exercise : solution ]
Matching Examples and Function Definitions [ exercise : solution ]
Creating Contracts From Examples [ exercise : solution ]
Contracts, Examples & Definitions - bc [ exercise : solution ]
Contracts, Examples & Definitions - Stars [ exercise : solution ]
Contracts, Examples & Definitions - Name [ exercise : solution ]
Do The Examples Have the Same Contracts? [ exercise : solution ]
Do The Examples Have the Same Contracts? (2) [ exercise : solution ]
Matching Contracts and Examples [ exercise : solution ]
Matching Contracts and Examples 2 [ exercise : solution ]

Custom Scatter Plots

Defining Row-Consuming Functions [ exercise : solution ]
Custom Scatter Plot - Explore [ exercise : solution ]
species-dot - Explore [ exercise : solution ]
Word Problem: species-dot [ exercise : solution ]
Word Problem: sex-dot [ exercise : solution ]
Animal-Image - Explore [ exercise : solution ]
Word Problem: fixed-dot [ exercise : solution ]
Dots for Value Ranges - Explore [ exercise : solution ]

Table Methods

Reading Row and Function Definitions [ exercise : solution ]
Function Cards [ exercise : solution ]
Exploring Methods [ exercise : solution ]
What Table Do We Get? [ exercise : solution ]

Solving Word Problems with the Design Recipe

Matching Word Problems and Purpose Statements [ exercise : solution ]
Writing Examples from Purpose Statements [ exercise : solution ]
Writing Examples from Purpose Statements 2 [ exercise : solution ]
Fixing Purpose Statements [ exercise : solution ]
Word Problem: rocket-height [ exercise : solution ]
Rocket Height Challenges [ exercise : solution ]
Blank Design Recipe [ exercise : solution ]
The Design Recipe (Restaurants) [ exercise : solution ]
The Design Recipe (Direct Variation) [ exercise : solution ]
The Design Recipe (Slope/Intercept 1) [ exercise : solution ]
The Design Recipe (Negative Slope/Intercept) [ exercise : solution ]
The Design Recipe (Geometry - Rectangles) [ exercise : solution ]
The Design Recipe (Geometry - Rectangular Prisms) [ exercise : solution ]
The Design Recipe (Geometry - Circles) [ exercise : solution ]
The Design Recipe (Geometry - Cylinders) [ exercise : solution ]
The Design Recipe (Breaking Even) [ exercise : solution ]
The Design Recipe (Marquee & Cubing) [ exercise : solution ]

Method Chaining

The Design Recipe: is-dog / is-female [ exercise : solution ]
The Design Recipe: is-old / name-has-s [ exercise : solution ]
Chaining Methods [ exercise : solution ]
Chaining Methods 2: Order Matters [ exercise : solution ]

Defining Table Functions

Making Connections [ exercise : solution ]
Design Recipe Telephone Set 1: is-dog [ exercise : solution ]
Design Recipe Telephone Set 1: days-to-adopt [ exercise : solution ]
Design Recipe Telephone Set 1: is-young [ exercise : solution ]
Design Recipe Telephone Set 2: is-old [ exercise : solution ]
Design Recipe Telephone Set 2: kilos [ exercise : solution ]
Design Recipe Telephone Set 2: is-cat [ exercise : solution ]
Design Recipe [ exercise : solution ]
Data Cycle: Analyzing Dogs [ exercise : solution ]
Data Cycle: Analyzing Cats [ exercise : solution ]

Grouped Samples

Grouped Samples from the Animals Dataset [ exercise : solution ]
Displaying Data [ exercise : solution ]
Data Cycle: Analyzing Categorical Data [ exercise : solution ]
Samples from My Dataset [ exercise : solution ]
The Design Recipe [ exercise : solution ]
The Design Recipe [ exercise : solution ]

Correlations

Identifying Form, Direction and Strength [ exercise : solution ]
Reflection on Form, Direction and Strength [ exercise : solution ]
Identifying Form, Direction and Strength (Matching) [ exercise : solution ]
Identifying Form and r-Values [ exercise : solution ]
Correlation Does Not Imply Causation! [ exercise : solution ]
Correlations in the Animals Dataset [ exercise : solution ]
Correlations in My Dataset [ exercise : solution ]

Linear Regression

Drawing Predictors [ exercise : solution ]
Which Questions Make Sense to Ask? [ exercise : solution ]
Age vs. Height Explore [ exercise : solution ]
Interpreting Regression Lines & r-Values [ exercise : solution ]
Data Cycle: Regression Analysis (Example) [ exercise : solution ]
Describing Relationships [ exercise : solution ]
Describing Relationships (2) [ exercise : solution ]
Data Cycle: Regression Analysis [ exercise : solution ]
Data Cycle: Regression Analysis 2 [ exercise : solution ]

Checking Your Work

“Trust, but verify …” [ exercise : solution ]
“Trust, but verify…” (2) [ exercise : solution ]
this article [ exercise : solution ]
Can Software be Biased? [ exercise : solution ]

Threats to Validity

Identifying Threats to Validity [ exercise : solution ]
Identifying Threats to Validity (2) [ exercise : solution ]
Selection Bias or Biased Study? [ exercise : solution ]
Fake News [ exercise : solution ]
Lies, Darned Lies, and Statistics [ exercise : solution ]
Identifying Threats to Validity (Part 1) [ exercise : solution ]
Identifying Threats to Validity (Part 2) [ exercise : solution ]
Identifying Threats to Validity (Part 3) [ exercise : solution ]

All Lesson Notes

The student workbook includes notes for various lessons. You can access all of these notes by clicking the links below:

Printables for Classroom Walls

Resources that Pair Well with Bootstrap:Data Science

What’s Going on in this Graph? - weekly intriguing data visualizations by the New York Times
The Pudding - fascinating, data-rich visual essays explaining ideas debated in culture
How Not To - friendly potshots at terrible Data Science, regularly posted to social media under the hashtag "HowNotTo"
Same Stats, Different Graphs - an illustration from Autodesk of why we must see the shape of data and not just focus on the descriptive statistics
Guess the Correlation - a game-ified resource (built by Omar Wagih) for building intuition for correlation, based on randomly-generated scatterplots
Sensitive r - an applet from Geogebra that shows how changing a single point can change the correlation coefficient r

Other Facilitation Resources

Implementation Options [link] - This guide includes suggestions for teachers looking spend a week, a month, or a semester teaching Bootstrap:Data Science.
Homework Submission Template [google form] - Efficiently collect hyperlinks to student work.
Broadening Participation [slides] - This compilation of best-practices from the CS-Education literature offers guidance on broadening student participation.

Standalone Hour of Code Activities from Bootstrap

Hour of Data [link] - A self-guided Desmos activity for exploring a real dataset, and using it to make sense of a real-world problem.

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.