Students summarize their dataset by exploring the data and identifying categorical and quantitative columns, datatypes, and more. They also define a few sample rows, random subsets, and logical subsets.
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Relevant Standards |
Select one or more standards from the menu on the left (⌘-click on Mac, Ctrl-click elsewhere). CSTA Standards
K-12CS Standards
Next-Gen Science Standards
Oklahoma Standards
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Lesson Goals |
Students will be able to…
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Student-facing Lesson Goals |
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Preparation |
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Language Table |
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🔗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 49).
- U.S. Voter Turnout Rates 1986-2018
- 2016 U.S. Presidential Elections
- R.I. Schools
- Police Traffic Stops, Durham, NC, 2002-2013
- MLB Hitting Stats
- State Demographics
- Movies
- Countries of the World
- U.S. Income
- U.S. Presidents
- Music
- Summer Olympic Medals
- Winter Olympic Medals
- Pokemon Characters
- IGN Video Game Reviews
- U.S. Cancer Rates
- Sodas
- Cereals
Open the Research Paper template, and save a copy.
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Students fill in their first and last name(s), the teacher name on the first page of the Research Paper.
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Students should also copy the link to the dataset (spreadsheet), and paste it into the first page of the Research Paper.
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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 notes on these datasets, which we recommend for all teachers before having their students choose a dataset.
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
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Have students look at the spreadsheet for their dataset. What do they Notice? What do they Wonder? Have them complete My Dataset (Page 45), making sure to have at least two Lookup Questions, two Compute Questions, and two Relate Questions.
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In the Definitions Area, students use
random-rows
to define at least three tables of different sizes:tiny-sample
,small-sample
, andmedium-sample
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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 46).
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Students should fill in My Dataset portion of their Research Paper.
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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 47), and use the Design Recipe to write the filter functions that you planned out on Samples from My Dataset (Page 46). 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 Emmanuel Schanzer, Nancy Pfenning, Emma Youndtsmith, Jennifer Poole, Shriram Krishnamurthi, Joe Politz, Ben Lerner, Flannery Denny, and Dorai Sitaram with help from Eric Allatta and Joy Straub is licensed under a Creative Commons 4.0 Unported License. Based on a work at www.BootstrapWorld.org. Permissions beyond the scope of this license may be available by contacting schanzer@BootstrapWorld.org.