(Also available in CODAP)

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.

## 🔗Review: Consider Data 20 minutes

### Overview

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.

### Launch

The Data Cycle is a roadmap that guides us in the process of data analysis. You’ve learned that the Data Cycle includes four steps. Let’s review what those steps entail.

• What are the different types of questions we can ask?

• Lookup, arithmetic, and statistical questions.

• What’s the difference between arithmetic and statistical questions?

• A statistical question anticipates variability in the data related to the question and accounts for it in the answers, while an arithmetic question anticipates a specific answer related to a particular arithmetic process.

Consider Data

• What do we need to determine in this phase?

• What data do we need: "What rows should we investigate?" and "What columns do we need?"

Analyze Data

• We’ll choose what kind of display we’ll need to answer our question.

• Which two displays work with categorical data? Why might you choose one over the other?

• Bar and pie charts work with categorical data. A pie chart only makes sense when you have the full picture, whereas a bar chart shows the count. .

Interpret the Data

• In your own words, what happens during this phase?

• We answer questions and summarize results, which often leads to new questions.

### Investigate

In this lesson, we’re going to get some practice with the second step of the cycle - Consider Data - identifying the Rows and Columns needed to answer various questions, and using our knowledge of Contracts to help turn those questions into working code!

### Synthesize

• What strategies did you use to determine which columns to use?

• Why do the contracts for some displays require more arguments than others?

## 🔗Choosing a Dataset 30 minutes

### Overview

Students select a dataset that interests them (or are assigned a real-world dataset to focus on), 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!

Note: Some teachers opt to focus their classes on a single dataset. We recommend Global Food Supply & Production Starter File for this purpose.

### Launch

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.

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?

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 another dataset.

Make sure you’ve made a decision about how much choice you are giving students and have modified the directions that follow accordingly!

• Choose a dataset that is of interest to you from our Dataset Library.

• Open your dataset’s starter file in Pyret and save a copy.

• Look at the spreadsheet or table for your dataset.

• What do you Notice? What do you Wonder?

### Investigate

By now students will either have chosen a dataset of their own or you will have decided to focus your class on a single dataset (we recommend Global Food Supply & Production Starter File for this purpose.) They will be applying what they learn to this new dataset.

• Using your Pyret starter file, complete My Dataset.

• Make sure to include at least two questions that can be answered by your dataset and one that cannot.

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

### Synthesize

• Which dataset did you select? And why?

• What questions are you curious to dig into?

## 🔗Dataset Exploration Project flexible

### Overview

Students are introduced to the Dataset Exploration Project, which will be woven into lessons from here on out.

Today we’ll start by adding four items to their Data Exploration Project Slide Template:

1. a description of their dataset, including its source, structure, and relevance

2. at least one bar chart

3. at least one pie chart

4. any interesting questions they develop

To learn more about the scope and sequence of the exploration project, visit Project: Dataset Exploration.

### Launch

For the rest of this course, each time we learn about a new data science concept, you will add displays, questions, and analyses about your Dataset Exploration Project.

• Save your own copy of the slide deck.

• Let’s get a sense of what this project is all about - take a few minutes to look at the slides.

• What do you Notice? What do you Wonder?

• Students will likely notice references to many displays they are unfamiliar with.

• They may wonder how there is going to be so much analysis on just one dataset!

• Blue text is included to provide examples.

• Slides can be duplicated if students want to add additional displays or interpretations.

### Investigate

• Complete all of the slides you see in the "About this Dataset" portion of the slide deck.

• It may be helpful to refer to what you wrote on My Dataset.

• Choose one categorical column from your dataset that you will represent with a bar chart.

• Turn to the top section of Data Cycle: Categorical Data and record a question that your bar chart could answer.

• Complete the rest of the data cycle, recording how you considered, analyzed, and interpreted the question.

• Repeat this process for at least one more categorical column - but this time, create a pie chart.

• Then, copy/paste at least one bar chart and one pie chart into that section of your slide deck.

• Be sure to also add any interesting questions that you developed while making and thinking about these displays to the "My Questions" slide at the end of the template.

You may need to help students locate the “Bar Charts” section, “Pie Charts” section, and “My Questions” slide in the template.

### Synthesize

Let’s share what we learned about our datasets!

• Did you discover anything surprising or interesting about your dataset?

• What questions did the bar and pie charts inspire?

• Did other students make any discoveries that were surprising or interesting to you?

These materials were developed partly through support of the National Science Foundation, (awards 1042210, 1535276, 1648684, 1738598, 2031479, and 1501927). 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.