Students summarize their dataset by exploring the data and identifying categorical and quantitative columns, data types, and more. They also define a few sample rows, random subsets, and logical subsets.

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.



  • Make sure all materials have been gathered.

  • Decide how students will be grouped in pairs.

  • Computer for each student (or pair), with access to the internet

  • Student workbook, and something to write with

  • All students 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 a new one.

Supplemental Resources

Language Table





+, -, *, /, num-sqrt

4, -1.2, 2/3, pi


string-length, string-repeat, string-contains

"hello", "91"


<, <>, <=, >=, <, >, ==, <>, >=

true, false


star, triangle, circle, square, rhombus, ellipse, regular-polygon, radial-star, bar-chart, pie-chart, box-plot, scatter-plot, bar-chart-summarized, pie-chart-summarized



.row-n, .order-by, .filter, .build-column, random-rows

🔗The Data Cycle 20 minutes


Students learn about the Data Cycle, which helps them get situated in the process of analyzing the datasets they will select in this lesson. They browse through the library of provided datasets, and choose one they want to work with. NOTE: the selection process can also be done as a homework assignment, if all students have internet access at home.


Zoom out a little and help students reflect on what they’ve done so far. Students began by exploring the Animals Dataset, formulating questions and exploring them with data displays. This led to further questions, making subsets, and asking more questions.

🖼Show image The Data Cycle[*] is a roadmap, which helps guide us in the process of data analysis.

(Step 1) We start by Asking Questions - statistical questions that can be answered with data.

(Step 2) Then we Consider Data. This could be done by conducting a survey, observing and recording data, or finding a dataset that meets our needs.

(Step 3) Then it’s on to Analyzing the Data, in which we produce data displays and new tables of filtered or transformed data in order to identify patterns and relationships.

(Step 4) Finally, we Interpret the Data, in which we answer our questions and summarize the results. As we’ve already seen from the Animals Dataset, these interpretations often lead to new questions…​.and the cycle begins again.

Explain to students that they will now select a dataset for them to work with for the remainder of the course. Make sure they understand that it genuinely has to be something they are interested in - their engagement with the data is critical to engaging with the class.

Students can also find their own dataset, and use this Blank Starter file. See this tutorial video for help importing your own data into Pyret.

Students must have at least 2 questions that are both interesting and answerable using their dataset.


Have students choose a dataset that is interesting to them! They should have at least two questions that the dataset can help them answer, and write them on What’s on your mind? (Page 58).

Gerry Mandering

Dataset Starter File

World Cities' Proximity to the Ocean

Dataset Starter File

Marijuana Laws & Arrests by State 2018

Dataset Starter File

College Majors

Dataset Starter File

US Jobs

Dataset Starter File

Refugees 2018

Dataset Starter File

Fast Food Nutrition

Dataset Starter File

Beverages Nutrition

Dataset Starter File

North American Pipe Organs

Dataset Starter File

Esports Earnings

Dataset Starter File

R.I. Schools

Dataset Starter File


Dataset Starter File

International Exhibition of Modern Art

Dataset Starter File

MLB Hitting Stats

Dataset Starter File

NBA Players

Dataset Starter File

NFL Passing

Dataset Starter File

NFL Rushing

Dataset Starter File

NYPD Stop, Search & Frisk 2019

Dataset Starter File

U.S. Voter Turnout Rates 1986-2018

Dataset Starter File

State Demographics

Dataset Starter File

Countries of the World

Dataset Starter File

U.S. Income

Dataset Starter File

U.S. Presidents

Dataset Starter File


Dataset Starter File

IGN Video Game Reviews

Dataset Starter File

Open the Research Paper template, and save a copy.

  • Students fill in their first and last name(s), the teacher name on the first page of the Research Paper.

  • Students should also copy the link to the dataset (spreadsheet), and paste it into the first page of the Research Paper.

  • Students should click "Publish" in their Pyret Starter File, then copy/paste the resulting link into the first page of the Research Paper.

We have also compiled some , which we recommend for all teachers before having their students choose a dataset.


Have students share their datasets and their questions.

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

🔗Exploring Your Dataset flexible


Students apply what they’ve learned about describing and making subsets from the Animals Dataset to their own dataset. Note: this activity can be done briefly as a homework assignment, but we recommend giving students an additional class period to work on this.


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 Notice 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. Finally, you used the Design Recipe and table methods to make random and logical subsets.

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


  • Have students look at the spreadsheet for their dataset. What do they Notice? What do they Wonder? Have them complete My Dataset (Page 54), making sure to include at least two questions that _can be answered by their dataset and one that cannot.

  • In the Definitions Area, students use random-rows to define at least three tables of different sizes: tiny-sample, small-sample, and medium-sample.

  • In the Definitions Area, students use .row-n to define at least three values, representing different rows in your table.

  • Have students think about subsets that might be useful for their dataset. Name these subsets and write the Pyret code to test an individual row from your dataset on Samples from My Dataset (Page 55).

  • Students should fill in My Dataset portion of their Research Paper.

  • Students should fill in Categorical Visualizations portion of their Research Paper, by generating pie and bar charts for their dataset and explaining what they show.

Turn to The Design Recipe (Page 56), and use the Design Recipe to write the filter functions that you planned out on Samples from My Dataset (Page 55). When the teacher has checked your work, type them into the Definitions Area and use the .filter method to define your new sample tables.

Choose one categorical column from your dataset, and try making a bar or pie-chart for the whole table. Now try making the same display for each of your subsets. Which is most representative of the entire column in the table?


Have students share which subsets they created for their datasets.

[*] From the Mobilizing IDS project and GAISE

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