# Unit 1, Section 1: Data Are All Around

Instructional Days: 7

## Enduring Understandings

Data play an important role in our everyday lives. Organizing it can provide evidence about real-life events and people. The data collected by answering survey questions produce variability. Distributions, graphs, and plots are useful tools for organizing data to understand variability. Statistical questions address people, processes, and/or events that contain variability. Situations with variability can sometimes be simplified with some basic statistics.

## Engagement

The Target Story will introduce students to the idea that data are ubiquitous. The advent of computers has transformed the way data are collected, used, and analyzed. Video can be found at: https://www.youtube.com/watch?v=XvSA-6BJkx4

Note

Pre-loading the video on your computer prior to the beginning of class is highly recommended to avoid any technical difficulties.

## Learning Objectives

Statistical/Mathematical:

S-ID: Summarize, represent, and interpret data on a single count or measurement variable.

S-ID 1: Represent data with plots on the real number line (dotplots, histograms, bar plots, and boxplots.

S-ID 2: Use statistics appropriate to the shape of the data distribution to compare center (median, mean) of two or more different data sets. (Measures of spread will be studied in unit 2.)

S-ID 6: Represent data on two quantitative variables on a scatterplot, and describe how the variables are related.

Focus Standards for Mathematical Practice for All of Unit 1:

SMP-3: Construct viable arguments and critique the reasoning of others.

SMP-5: Use appropriate tools strategically.

Data Science:

Experience data handling using ubiquitous data, and organize data using rectangular or spreadsheet format as data storage structures.

Everyday activities can be observed and recorded as data. Become aware of the difference between plots used for categorical and numerical variables. Interpret and understand graphs of distributions for numerical and categorical variables.

Applied Computational Thinking using RStudio:

• Work effectively in teams.

• Explain how data, information, and knowledge are represented for computational use.

• Collect, upload, and share personal data via a Participatory Sensing campaign.

• Learn about different representations of distributions using software.

• Utilize software to begin to analyze plots of data collected via Participatory Sensing.

Real-World Connections:

Students begin to develop an awareness that data are all around us. Information can be collected and organized. Computers are powerful tools that make organizing, storing, retrieving, and analyzing data accessible to use in problem solving and decision making. Students will begin to see the relevance of data collection to their own lives. They will begin to understand that data on its own is just collected; but once interpreted, it can lead to discoveries or understandings.

## Language Objectives

1. Students will use complex sentences to construct summary statements about their understanding of data, how it is collected, how it is used, and how to work with it.

2. Students will engage in partner and whole group discussions and presentations to express their understanding of data science concepts.

3. Students will use complex sentences to write informative short reports that use data science concepts and skills.

## Data File or Data Collection Method

Data Collection Method:

1. Students will keep a Data Diary for 24 hours to track their daily data output.

2. Students will gather data from the cards in the Stick Figures file.

3. As a class, students will determine how to organize the Stick Figures data.

4. Students will collect data using paper and pencil on the Food Habits Data Collection activity sheet.

5. Food Habits Participatory Sensing Campaign: Students will collect data about their snacking habits.