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.

Lesson Goals

Students will be able to…​

  • Explain why they chose their dataset

  • Describe their dataset

  • Make subsets from their dataset

Student-facing Lesson Goals

  • Let’s all choose an interesting dataset to investigate.



  • Decide how much choice you’re ready to offer your students before you begin. Research shows that choice increases student engagement! But focusing the whole class on a single dataset is also an option.

    • Would focusing your students on a single dataset make this doable for you? Because you teach younger students who might need more scaffolding? Or because you are new to teaching data science and managing fewer moving parts would increase your confidence? We recommend focusing on Global Food Supply & Production.

    • Are you ready to jump straight into supporting your students in working on a wide range of topics of their choosing? We have a full dataset library!

    • Want to give students choice from a shorter curated list…​ to shorten the decision-making process, focus on topics related to curriculur goals, or just to have fewer options to manage during class? We’ve assembled descriptions of individual datasets here. For those looking for a precurated shorter list, we’ve starred a few of them for you.

    • If you have time, you may want to complete all of the lessons with everyone getting extra practice analyzing Global Food Supply & Production and then have your students choose a dataset to analyze for their culminating research papers!

  • All students (or pairs) should log into CPO and open the "Animals Starter File" they saved from the prior lesson. If they don’t have the file, they can open the Animals Starter File.

categorical data

data whose values are qualities that are not subject to the laws of arithmetic

data science

the science of collecting, organizing, and drawing general conclusions from data, with the help of computers


a collection of related information that is composed of separate elements, but can be manipulated as a unit by a computer

quantitative data

number values for which arithmetic makes sense

random sample

a subset of individuals chosen from a larger set, such that each individual has the same probability of being chosen

statistical inference

using information from a sample to draw conclusions about the larger population from which the sample was taken

🔗Review 20 minutes


Students practice making lots of chart types, focusing specifically on the "Consider Data" step in the Data Cycle and how it can be used alongside Contracts to help go from questions to code.


Let’s get some practice isolating the Rows and Columns needed to answer various questions, and use our knowledge of Contracts to help turn those questions into working code!

Be sure to review student answers.

🔗Choosing a Dataset 30 minutes


Students select a dataset that interests them, and do some thinking about why it interests them, what questions they’d like to answer and what hypotheses they have. They’ll be analyzing this data for a long time, so it’s critical to ensure a high degree of buy-in before signing off on a student’s choice!

If you are opting to focus your whole class on a single dataset, we recommend skipping to the Exploring Your Dataset section of this lesson and using the dataset provided there. (It focuses on global food supply and production through environmental / geographic / cultural lenses and the variables were carefully selected to make sure it lends itself well for all kinds of data displays and discussions. You can, of course, opt to choose any dataset you’d like, from our library or otherwise.)


Data Science: it’s all about YOU!

What data matters to you? What questions do you care about? We live in a world filled with data, gathered about almost every subject you can imagine.

  • Climate sensors are gathering data on temperature, humidity, oxygen and more…​practically everywhere on the globe.

  • Census data tracks the number of different groups of people, as well as their education, income level, and more.

  • Companies like Facebook, Amazon, and Google gather massive amounts of data on the websites you visit, what you chat about online, what you purchase, etc.

This data is used to set public policy, draw voting districts, approve drugs, calculate school funding, decide which advertisements you see, and more.

  • Where else do you see data being gathered?

  • What are some other ways data is used in the world around you?

Below is a list of every dataset already provided to students, with a corresponding Starter File that instantly imports the (cleaned) data into Pyret. We suggest giving students a direct link to this page, which lists all of the relevant links found in the lesson plan.

Students can also find their own dataset, and use this Blank Dataset Starter File for Bootstrap:Data Science. See this tutorial video for help importing your own data into Pyret.


Have students choose a dataset that is interesting to them and save a copy of it in their programs!

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


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


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


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



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


Dataset Starter File


Dataset Starter File


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


Soda, Coffee & Other Drinks

Dataset Starter File

Fast Food Nutrition

Dataset Starter File


Have students share which datasets they chose, and why they are interesting or important to them. What questions did they come up with?

For the rest of this course, you’ll be learning new programming and Data Science skills, practicing them with the Animals Dataset and then applying them to you own data.

🔗Exploring Your Dataset Start Today…​ continue in Upcoming Lessons


Students apply what they’ve learned about describing and making subsets from the Animals Dataset to their own dataset. If your students will all be focusing on the same dataset, we recommend using Global Food Supply & Production.


By now you’ve already learned what to do when you approach a new dataset.

  • With the Animals Dataset, you first read the data itself, and wrote down your Notices and Wonders.

  • You described the columns in the Animals Dataset, identifying which were categorical and which were quantitative, and whether they were Numbers, Strings, Booleans, etc.

  • You took random samples of the dataset, to explore inference and probability.

Now, you’re doing to do the same thing with your own dataset.


  • Look at the spreadsheet or table for your dataset. What do you Notice? What do you Wonder?

  • Complete My Dataset, making sure to include at least two questions that can be answered by your dataset and one that cannot.

  • Save a copy of your starter file. In the Definitions Area, use random-rows to define at least three tables of different sizes: tiny-sample, small-sample, and medium-sample.

Today we will begin working on the Dataset Exploration, which will prepare students for writing their research papers. We will return to this in upcoming lessons. We are just going to work on the first section for now.

  • Make a copy of Dataset Exploration, and open the starter file for your dataset.

  • Complete the first set of questions in the exploration paper.

  • What are the categorical columns in your dataset? How are those values distributed?

  • Turn to Complete Data Cycle: Shape of My Dataset, and use the Data Cycle to generate pie and bar charts.

  • What do these charts tell you? Add the images of these charts - along with your interpretation! - to the "Making Displays" section of the exploration document.

  • Do these displays bring up any interesting questions? If so, add them to the end of the document.


Have students share their findings. Were any of them surprising?

These materials were developed partly through support of the National Science Foundation, (awards 1042210, 1535276, 1648684, and 1738598). CCbadge 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