Choosing Your Dataset

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Lesson Pathway, Standards and Practices

Standards (click one)

Common Core Math Standards

6.SP.A.1: Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

CSTA Standards

1B-AP-11: Decompose (break down) problems into smaller, manageable subproblems to facilitate the program development process.
3A-AP-16: Design and iteratively develop computational artifacts for practical intent, personal expression, or to address a societal issue by using events to initiate instructions.
3A-AP-17: Decompose problems into smaller components through systematic analysis, using constructs such as procedures, modules, and/or objects.
3A-AP-23: Document design decisions using text, graphics, presentations, and/or demonstrations in the development of complex programs.
3B-AP-14: Construct solutions to problems using student-created components, such as procedures, modules and/or objects.

K-12CS Standards

P7: Communicating About Computing

Oklahoma Standards

OK.6.D.1.3: Create and analyze box and whisker plots observing how each segment contains one quarter of the data.
OK.7.D.1.2: Use reasoning with proportions to display and interpret data in circle graphs (pie charts) and histograms. Choose the appropriate data display and know how to create the display using a spreadsheet or other graphing technology.
OK.8.DA.S.01: Analyze multiple methods of representing data and choose the most appropriate method for representing data.
OK.A1.D.1.1: Describe a data set using data displays, describe and compare data sets using summary statistics, including measures of central tendency, location, and spread. Know how to use calculators, spreadsheets, or other appropriate technology to display data and calculate summary statistics.
OK.L1.DA.CVT.01: Use tools and techniques to locate, collect, and create visualizations of small- and largescale data sets (e.g., paper surveys and online data sets).
OK.L1.IC.C.02: Test and refine computational artifacts to reduce bias and equity deficits.
OK.PA.A.2.2: Identify, describe, and analyze linear relationships between two variables.
OK.PA.D.1.1: Describe the impact that inserting or deleting a data point has on the mean and the median of a data set. Know how to create data displays using a spreadsheet and use a calculator to examine this impact.

Textbook Alignment

IM.Alg1.3.10

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.

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.

Materials

Preparation

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

Jump to Datasets and Starter files

Language Table

Types

Functions

Values

Number

+, -, *, /, num-sqrt

4, -1.2, 2/3, pi

String

string-length, string-repeat, string-contains

"hello", "91"

Boolean

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

true, false

Image

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

🔵🔺🔶

Table

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

🔗The Data Cycle 20 minutes

Overview

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.

Launch

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.

Investigate

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 57).

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
Movies: 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 2016: Dataset Starter File
State Demographics: Dataset Starter File
Countries of the World: Dataset Starter File
U.S. Income: Dataset Starter File
Arctic Sea Ice: Dataset Starter File
U.S. Presidents: Dataset Starter File
Music: 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.

Synthesize

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

Overview

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.

Launch

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.

Investigate

Have students look at the spreadsheet for their dataset. What do they Notice? What do they Wonder? Have them complete My Dataset (Page 53), 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 54).
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 55), and use the Design Recipe to write the filter functions that you planned out on Samples from My Dataset (Page 54). 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?

Synthesize

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). 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 contact@BootstrapWorld.org.