Share this video to introduce the course. Discuss student takeaways from the video. How is this course similar or different from other courses you have taken in the past?

Show the visual below and ask students: What do you notice? What do you wonder? What story can you tell from this visual? View slide (from Unit 1 Slideshow, make your copy above or here)

Launch the first discussion about variability by asking questions like, “What are the different factors that are changing in the story? What is the visual telling us about Steph Curry’s shooting?” Students may mention:

• Where Steph Curry takes his shots
• How likely he is to make shots at different locations
• How frequently he shoots from different areas

Say something like, “These represent variability in his basketball shooting. Variability is an aspect of all of life that we can analyze and represent through data science and data visuals. There is variability in anything that changes. For example, there is no variability in the color of my purple mug, it is purple everyday, but there is variation in which mug I pick each morning. That variation is something we could collect data on and represent. It would not be interesting to collect data on the color of my purple mug; it would be purple every day!

There is all sorts of variability in the world. In data science we collect specific data and then represent that data to tell a story about its variability. This visual representation of the data is the lens through which we view the data. In this graphic, data was collected for a specific year for Steph Curry, but it could have represented data for more than one year. This visual could have represented shots for the whole Warriors team. As data scientists, there are decisions we make about what we will collect and then how we represent the variability.”

Show students the variability visual (Slide). Ask them questions like: “What does this visual communicate to you? How would you relate this visual to Steph Curry’s basketball shooting? How does this visual help you understand the idea of variability and the data science process?”

Variability is everywhere. Ask students, ‘Where do we see variability day to day in our lives?’ Possible examples could be different types of dogs, weather, terrain, hair color, appearance, clothing, height, car make and color…

In data science, we attempt to collect data that represents the variability within the world. After collecting data we build a model or a visual to represent our data. This visual helps others to see the data and tell a story from the data. This is not an objective process and is often influenced by individual bias. We have to make choices about what data we collect and how we will collect the data. We also make decisions about how to model the data or create a visual for the data. This process is creative and may lead to different representations depending on the data scientist.

The Good Group Work activity is designed to set the norms for group work throughout the course. This activity is important in constructing a collaborative problem-solving environment valuing all students’ ideas.

The activity launches by asking students to reflect on the things that they do and do not like people to say and to do when working on math problems in a group. Ask them to generate a list individually.

Once students have generated their lists, assign students to a group. Assign each group a facilitator (some methods to consider: the student with the most recent birthday, wearing the lightest color, etc) who will be responsible for recording the group ideas and ensuring each student will have a chance to share their thoughts in the group. Groups will consider the following questions (Slide):

• What do you like when working in groups doing math with other people? What do you not like?
• What are some math stereotypes we may have while working in a math group?

After groups have had time to plan their responses, have group members share out their responses to the class. On the board curate a list of the “What we don’t like” and “What we like” list. Save this list of group norms to be referred to throughout the course.

Launch the next activity as an opportunity to explore variability within our own lives visually. Start with making a connection between the differences in people and the variability that exists in the world leading to variability in data. Share the story of Georgia and Stefanie’s year of data postcards. Consider watching the video of their story as a class.

Set students up to work in groups to make sense of a pair of postcards in the activity Exploring Variability (Handout 1, Resources for Handout 1, Sample Student Work, Slide for Handout 1).

There are four different sets of postcards. Groups will be working on exploring one of the four. Once groups have finished their exploration of the postcards, set them up to share ideas in jigsaw groups in which each pair of postcards is represented. Put one group member from each group with a different group member from each group so that one person can share about each of the four different pairs of graphs.

As a whole class, discuss what surprised students about the postcards? What was interesting or challenging? What types of variability were Georgia and Stefanie exploring through their data collection and visuals? (Dear Data Postcard Discussion Slide)

In the next few classes, you will be making your own Dear Data postcard. You will be considering variability in your day-to-day experience around the topic of your choice and how to capture this variability. Here are Dear Data Project Examples (Slide 1 and Slide 2).

This will be our first time of many during this course of working through the data science process. Share the slide containing the visual of the data science process. 

The Data Science Process involves these components:

• Ask questions 
• Gather and organize data 
• Model
• Analyze and synthesize
• Communicate data

This is an iterative process that includes feedback, revision and refinement at every stage.  

In this next project we will be working through the whole data science process. You will decide which question you will ask, make a plan for gathering data and then gather data, model your data by creating a data visual, get feedback on your visual, make revisions based on feedback, analyze and synthesize your data by considering what you can learn from your visual, and communicate your findings. The feedback and revision process are aspects of the iterative nature of this process.

Dear Data Final Project Overview (Handout 2) is a handout that summarizes this project. The next sections of the lesson plan will walk you through the process of the project step by step. You can choose to share the handout with students now or at any point during the project to give them an overview of the project. Let them know however that you will walk through the sections of the project as a class. When you share the project overview you can also share the project criteria.

Share the slide with the data science process. Let students know that in this class, they will begin by deciding on a question they will ask (arrow 1) and then they will make a plan for how they will gather data (arrow 2). Ask students in their groups to brainstorm some ideas for questions they could ask and explore from their lives and then gather data around. Generate a class list of topics or questions students could explore. (Discussion Brainstorm Slide)

Set students up to work in pairs to choose a question and plan their data gathering in the activity Planning your Dear Data Project (Handout 3). 

As partners are wrapping up their work together, have them share their slideshow with you. Check that they have created slides that share their topic or question and reasoning why they are exploring the topic or question and their plan for how they will gather data. Refer students to Dear Data Final Project Overview and Planning Dear Data Project handouts for details and ideas on what to include.

Set students up to continue their data gathering over the next few days based on their revisions. Let them know when they will be bringing their final set of data to class. Over the next few days set aside a few minutes each day to check-in with students about their data gathering: see what questions students have and if they have any issues they need help resolving.

This intermission has two data ethics lessons, which you can conduct over the 2-day period that is suggested for student data gathering.

Share the ‘Readability of Privacy Terms’ visual with the students (Slide). Ask students:

• What do you notice?
• What do you wonder?
• What story does this graph tell?

There are many ways people use data and as we dive into a world with data science as a tool helping us to understand patterns we see in the world and our lives we need to also talk about the ethics of using data. Understanding data and how to use it is powerful – and in the words of Uncle Ben from Spider-Man, “with great power comes great responsibility”. Using this power to the detriment of society with or without consideration of impact leads us to the importance of Data Ethics conversations. Who is collecting data? How is it being used? How do we critique the use of data as educated consumers of information? This is just the beginning of the conversation, it is integrated in our discussions about data as we move through this course together on the path of becoming ethical Data Scientists.

Share with your students that they are telling a story about themselves in the Dear Data project.  You can collect your own data. But you aren’t the only one interested in the variability of your experience and telling a story about it. In your day to day life, there are other stories being told about you. Companies and the government collect data about you. When you use any of your electronic devices: iPhones, computers, Alexa, Nest, apps, Google, social media accounts, websites, etc data is collected on you. Each of your apps and social media accounts have privacy settings about what information companies can collect about you. When you go on a website at the bottom of the page it often asks you to accept cookies. Where the website tracks your movements, personalizes, and saves information about your time on their site. These are just a few examples of ways data is collected about you.

Before starting the video, ask students why they think the privacy data visual was used in an introduction to our conversation around data ethics. As you are discussing this, notice if students are acknowledging the difficulty level of the writing used in the terms and conditions and companies may be using your data in ways that you are not aware.

Set students up to watch this video that captures people on the street prompted to read and react to the terms and agreements of an app. Have them consider the following questions as they watch it. (Video Slide, Discussion Question Slide)

• What did you learn about data?
• What does this make you wonder?
• What questions does this leave you with?
• Why do you think privacy is part of the data ethics conversation?

Once the video is done have students write about the prompts to prepare for a class discussion. Give students at least two minutes for writing.

Invite students to discuss their responses to the questions about the video. Make space for as many students to contribute to this discussion as possible. During the discussion students will likely share ideas about the scary ways apps use data. Listen for ideas that contrast this fearful outlook. Add a discussion around the benefits to the conversation explicitly by inviting students to consider the benefits of sharing data with different apps.

Frame the next Data Ethics conversation saying something like, “As we move through the Data Science Process when working with data, there are Data Ethics questions at each stage. When we gather data we consider where the data comes from, who collected it, and how it was collected. When we model and communicate the data we consider what data is being used, how it is being used, and how we critique the use of data as educated consumers of information. This is the aspect of Data Ethics that we will explore in the next activity.”

When launching the activity, say something like, “The goal of this next activity is for you to understand the importance of bringing your critical thinking to how data is used in articles, tweets, facebook and more. It is essential to ask questions of the story presented, since what is presented may not be the complete ‘truth’. You should always ask, ‘Is there more to the story?’” 

Share with them that they will be looking at four different pairs of data visuals to consider these questions about how data can be misrepresented. The first of the pair of visuals will share data that is true, but misleading. The second of the pair will show a bigger picture with more data. For each, you will tell what story each piece of data tells. You will then give specifics on why the first visual is misleading given the additional data given in the second graphic. Tell students that this part of being a Data Scientist requires them to be a skeptic in their approach to interpreting data. Invite students to share ideas about what a skeptic would do or say when interrogating a data visual. 

When discussing students’ ideas about being a skeptic, introduce the Skeptic framework by telling students they need to be convincing. A wonderful teacher named Cathy Humphreys uses this framework. In mathematics we must always be convincing. The first level of convincing is when you convince yourself. The second level of convincing is when you convince a friend. The third and most challenging level is when you try to convince the skeptic.

Let your students know that the topics in this activity are sensitive topics. Say something like,
“One of the goals of this course is to become critical consumers of data and data visuals. Keep in mind, however, that these topics hit close to home for many of us in different ways. The topics in this section have to do with polio vaccinations and death rates, DACA recipients and crime rates, and taxes and income. These are all sensitive topics for different reasons. If I am a DACA recipient or have close friends or family who are, this is a very important and personal topic to me. If I have experienced a death related to COVID, then talking about vaccinations and death rates can be an emotional topic. We don’t want to avoid these topics because they are sensitive, but we do want to understand and be supportive of our classroom community as we talk about them.” 

Set students up to work in groups to discuss the ethics of a collection of data visuals. Share the activity handout and data visual resources with students, Misuse of Data (Handout A) & Visual Resources. Let them know that all of the resources in the activity come from the book, Data and Data Graphics for this activity from Alberto Cairo, How Charts Lie, Chapter 4. Make mention that knowing where you are getting information from is an important piece of being a data scientist. 

While students are working, encourage them to be skeptics about the numbers and visuals. Remind them that skeptics ask a lot of questions to make sense of what they see, what it means, and if it’s valid. Encourage them to find specific parts of the visuals to use as evidence to make sense of what it communicates. 

Listen to what students are noticing, some examples of what they might bring up below:

• How area is used to compare amounts: the size of the rectangles compared to each other and the size of the circles compared to each other
• There is an issue reporting numbers without including the total number that the data is out of (2,139 out of how many?, 40 out of ____, 99 out of _____)
• Average can be misleading. You lose a lot of data when you report an average because the values can have such a large range that one number doesn’t capture the situation.
• When is it better to use actual numbers of people or rates? When sharing data about hunger, poverty, or other humanitarian topics, you can lose a sense of magnitude when you report percentages. Using the numbers of people humanizes the data and story.
• When and how can rates be used to make comparisons? For example, we could use the rate of people getting smallpox who are not vaccinated to predict how many people would have been infected if no one got the vaccine. This would have been 0.4 x 1,000,000. In this case this would have been a more valid number to compare to the 99 who died after getting the vaccine.

After students have had an opportunity to work through the three pairs of graphs. Debrief each pair of graphs as a class. As groups share out around each pair, begin to build a class list of misuses of data that we should be aware of when looking at data and data graphics. For example, reporting values without a comparison group can be very misleading.

As a class, discuss the data and data gathering process (slide). Have students share around the following questions. Consider having students individually jot notes around these questions and then share out as a class. 

• What surprised you about your data?
• What was challenging about gathering data?
• What worked? What didn’t? 
• What sources of variability did you hope to capture? 
• What would you do differently in this next iteration? 

Students will have an opportunity to work in their pairs to revise their plan, but you can take some brief time to brainstorm around a few issues students are having with data gathering to help them get ideas for their plan revisions. 

Let your students know that there are topics in this course that are sensitive or personal. In this case, looking at personal data is part of ourselves and can feel vulnerable to share. In other situations we will be looking at data that may affect us in different ways and can feel vulnerable or very personal as the data applies to our identity, our experiences, or a situation or someone close to us. We think it is important to approach these topics so that we can be informed and educated when we look at data. However, we want to keep in mind that we need to be sensitive to each other as we discuss these topics in class. Being sensitive in this case means being aware of how someone else may be feeling, not making jokes about the topic, listening to others to hear how they are experiencing the topic, understanding that emotions may come up for some of us that may not come up for others. As participants in this community, each of us need to own our impact on the learning community. This means that each of us must consider our impact before speaking, not just our intent. Even if we may have good intentions, we can still be hurtful to others in our language.

Share the Data Science Process visual and remind students that the process is iterative. Let them know that they asked a question, made a plan and began gathering data. Based on their experience they will now go back and make revisions to their question and plan to gather data. Depending on how their data gathering has gone, these revisions may be very minimal or more major. For some it may be gathering some additional data and for others it may involve revising their question significantly and restarting their data gathering. Set up students to work with their partner to revise and refine their plan based on their experiences and the class discussion in the activity Revise Data Gathering Plan (Handout 4). During this time they will make a copy of the slide they created and make revisions to their plan on the new slide.

Set students up to continue their data gathering over the next few days based on their revisions. Let them know when they will be bringing their final set of data to class. Over the next few days set aside a few minutes each day to check-in with students about their data gathering: see what questions students have and if they have any issues they need help resolving.

This intermission has a 2-day data ethics lesson, which you can conduct over the period that is suggested for student data gathering.

Before launching the jigsaw activity, remind students about the video they watched during the first intermission, about the terms and conditions for apps. In this jigsaw, students will be looking more deeply into data ethics topics concerning privacy and our devices. 

Use the following questions to launch this activity. Based on the video and your own experience, as a class discuss (Slide):

• Why is privacy an issue?
• What companies have our data and what are they doing with it?
• Why do they want our addresses? To know our movements?

Set students up for a jigsaw around the following data ethics topics (Slide):

• Cookies
• Location services
• Targeted advertising
• Privacy settings
• Research and read about your own topic (Optional)
  

Each student will read articles on one of these data ethics topics and be prepared to summarize what they learned from the article about their topic (a list of suggested articles is here). After students have read the articles around their topics, group them with students who have picked the same topic. Give groups time to share with each other their findings and discuss what they will share with others about their topic. For day 2, regroup students so that each topic is represented in each group and have students share about their topic in their new group. (See slide for a description of a jigsaw.) After sharing their topics have students generate a list of take-aways and pros and cons of the topics they have read about. 

As a class, summarize findings from the articles. What are your take-aways? What are the pros and cons? What are the ethics issues around these topics? (Slide)

Come back to the discussion around the Dear Data project by asking students how this second round of data gathering went. What surprised them? What was challenging or less challenging this time? 

Share with students the visual of the Data Science Process (Slide) and highlight that they are at the modeling phase of the Data Science Process. In this stage, we create a model of our data, in this case a visual representation, that allows us to explore patterns in the data. Let students know that they will now have an opportunity to share their data with each other and begin to consider how they will represent their data visually. 

Set students up in pairs, to take turns sharing their data gathering with each other in the activity Sharing and Visualizing Data (Handout 5, Discussion Questions Slide). When they are done with their discussion, encourage students to add ideas to the slides they created during their discussion.

Have students share out a few of the factors they will need to capture in their data visual. Have students describe some of their thoughts around how they might represent these different factors visually.

Alongside their partner, for conversation as questions and ideas arise, have students work individually on making a draft of how they might visually present their data. Encourage students to ask each other questions and share their ideas with each other and with you as they work. Students will start from different places, some may start drawing and then create a key, while other students may first work on a key and then begin their drawing. Students may create multiple drafts.

Have students share their drafts with one other person, their partner or a different person in the class. As they look at each others’ work, ask students to: (Slide)

• Ask questions of each other and clarify what different symbols mean. 
• Help each other brainstorm solutions to outstanding questions. 

Return to the Data Science Process (Slide) and discuss where students are in the process. Students have received feedback and will now have a chance to revise their model (in this case a data visual) based on that feedback.

Set students up to work individually on creating a final version of their data visual based on feedback, analyze their visual, communicate their discoveries and reflect on the whole process in Design a Dear Data Postcard (Handout 6 ) activity. 

Having students add to their Dear Data slideshow to show all they have done and learned is the communication phase of the data science process. Review the criteria for this project with them at this time giving them an opportunity to ask questions about the meaning of the criteria.

After students have completed their Dear Data slideshow, give students time to complete the self-assessment portion of the Dear Data Criteria and Feedback Rubric. Since this is their first self-assessment, you may want to discuss as a class what they might include as evidence and comments for growth and strength areas.

After the project, allow students to see the work of their classmates in the form of a “gallery walk” (in person), or by putting work on a Google Jamboard or slide deck that students can go through and leave supportive comments if they so desire.  The goal is to build a classroom community in this first activity in which students support each others’ work.

Given this is the first project of this course it is important to create the opportunities students will need to have success. Making space for students to get feedback and revise as they need to meet the project’s expectations will impact their mindset and approach to learning in this course.

Show students the visual from the Pew data (slide). Give students time to look at the visual. Ask students:

• What do you notice? 
• What do you wonder? 
• What story does this graph tell?

Have students share their ideas with the class. Ask students what data might have been collected to create this graph? Does this visual raise any data ethics questions for you?

This visual comes from the Pew Research Center. These data are from a phone survey that was collected between January 8 and February 2021. In total, the data represents a sample of 1,502 U.S. adults aged 18+, with 300 answering from a landline and 1,202 answering from their cell phone. Given the context of how the data is collected, does the fact that participants opt into this data collection make it more ethical? How might the fact that this is a phone survey skew the data?

Tell students something like: “We are going to be spending more time with the Pew dataset. The same dataset that the visual we discussed in our data talk was drawn from. This dataset has been cleaned and stored in a spreadsheet, which you can see here (download a copy using the link in the Materials Required area below).”

Set students up to look at the spreadsheet in groups. Ask students to discuss the following questions in their groups (Slide):

• What do you notice/wonder about this dataset?
• How is the data organized?
  • What does each row represent? 
  • What does each column represent?
  • How do you know this?
• What about the world is this dataset capturing? What variability?
  
• Are there any ethical questions raised by this data?

As a class discuss the questions above. Let students know that datasets are often organized in this way where a row of a spreadsheet represents all the responses from one individual and the columns represent all the responses to one question. Have students share the variability they are seeing in the dataset. Use this conversation to continue to build on their understanding of variability. Put up the variability visual (Slide)— how does this relate to the exploration we just engaged in?

Show students the Data Science Process Visual (Slide). Let students know that they are going to brainstorm questions they can ask of the data (arrow 1). Set students up in groups to look back at the data and think about what questions they could ask about the data. As students work in groups, you can ask students what they find interesting about the data. What would they want to know more about around these topics? (Slide) For example, are they interested in comparing social media usage across age groups? Are they curious about how many books people read? How books read and internet use are related to each other?

After students have generated a few questions in their groups, invite them to share out as a class. Record students’ questions as they share. (Slide)

Introduce students to CODAP. CODAP is an online tool that allows you to import spreadsheets and explore the data in that spreadsheet. This tool allows you to explore the data by generating graphs of the data. Let students know that they will first have an opportunity to explore and learn about CODAP. Give students a link to CODAP with the data loaded in. Encourage them to play with the software and figure out what it can do, in pairs. Share with them the page of links to resources. There is a video tutorial and additional tutorials available for those who want to use it here. Keep in mind that students learn new technology in all different ways. Some like to dive in and explore and others will want to start with a tutorial. Welcome this variety of approaches. When students are ready they can start trying to answer some of their questions from the brainstorm. 

Run a quick class discussion to share CODAP features students discovered and how they think they will be useful.

Share with students the data science process visual. You will be deciding which questions to ask, using CODAP to model the data, synthesizing and analyzing your data, and communicating your findings. Modelling the data will look different in different situations. In this context, visuals of the data will be the models, with CODAP as our tool for creating those visuals.

Set students up in small groups to work on exploring three questions of their choice, in the activity “What Story Does the Data Tell?” (Handout 7). In small groups, students create a poster on a jamboard slide that shares the answers they found, the graphs they used to find them, and a short explanation of why that graph answers the question they had. 

Once groups have completed their posters hold a virtual gallery walk where students can visit each other’s posters and leave comments on virtual sticky notes. Students can share on the post-it a question, something they found interesting about the finding, or an insight they gained from the board.

Project the data talk visual. Give them some time to look and make sense of it. This might take longer than in other data talks since tables are information-dense. (Slide)

Ask students:

• What do you notice about this table?
• What is it about?
• What do the rows and columns mean?
• What can you say about the data?

Does the color coding and other visuals help? How? (Slide)

Share with students that the data science process is used in all fields of study. The concepts and tools you learn in this course can be applied to any content area you study. Throughout the course we will be looking at data in a variety of contexts and fields of study: community, environment, music preferences, media images, and more. We also learn a variety of data modeling tools and share data analysis in multiple communication styles. In the first project you were analyzing your personal data creatively and in this project you will analyze how data is used in business to make decisions and communicate your findings in a professional business report.

Ask the class if any of them are business owners or if they know any business owners. Share with them the idea that there is no age requirement to starting and running a business. Share with them the story of Me & the Bees and have them watch the video. (Slide) 

Companies like Me & the Bees will have a team in their company or an outside consulting firm analyze their data to give recommendations on how to improve their business. Let students know that they will be creating a business report for Me & the Bees. They will be given data to explore, model (find patterns within), and analyze. Finally, they will write a report to communicate their findings and describe recommendations for the company to better understand and improve their business.

Me and the Bees Business Report Project Overview (Handout 8,) is a handout that summarizes this project. The next sections of the lesson plan will walk you through the process of the project step by step. You can choose to share the handout with students now or at any point during the project to give them an overview of the project. Let them know however that you will walk through the sections of the project as a class. When you share the project overview you can also share the project criteria.

Invite students to brainstorm what data a company like Me & the Bees would collect and what the purposes would be. Highlight for students, this is like the step Ask Questions from the Data Science Process. (Show Data Science Process Slide)

Let students know that before they dive into the Me and the Bees data, they will take a look at a sample business report from an analysis of recycling trends. This will help them get a sense of what might be included in a business report.

Set students up in groups to look at the business report. Give students a few minutes to read, take notes and annotate the report. Let them know that after reading they will discuss the following (Slide):

• What does this business report include?
• What are the features of the writing?

It is important to remember that this is not meant to be an exact model of what students will be writing when they write their own business report in the next sections. What is important are some of the key features of the writings. Such as: concise, clear, visuals, color, graphs and tables as supporting evidence, bullet points, headings, etc. 

Let students know that before they begin working with the data (cleaning, modeling, finding patterns, and analyzing) we want them to have an opportunity to explore the data and understand how it is organized in a spreadsheet. Understanding this visual organization will be helpful as we move later in this course into situations where we can’t see the data as we work with larger datasets in other programs like Google Colab.

Distribute the Me & The Bees sample spreadsheets to the class. Have students spend some time looking at the Me & the Bees sample spreadsheets in pairs, and answering the following questions in their journals (Slide):

• What is the spreadsheet about?
• What does each row represent?
• What about each column?
• Brainstorm some things you could learn about the Me & The Bees business from the data in the spreadsheet.
• What are some questions you still have about it?
• What are additional things about the business you want to know?

Share with students that sometimes, like in this case, we have to clean the data first in order to facilitate working with it. We will be doing that here because we have multiple spreadsheets that we want to look at as a single dataset. Cleaning the data is included as part of Gathering Data in the Data Science Process.

Ask students to discuss in groups the steps they would need to take to merge the data into one spreadsheet. Create a class brainstorm. (Slide)  Some examples of what students will say is to add rows so that all the months are on one page, add columns to that spreadsheet adding the temperature and city, etc. Ask them what will be important to consider as they do this. (Keeping rows and columns aligned with dates matched up so that data doesn’t end up in the wrong location.)

Before students begin, let them know that an important part of data cleaning is to keep the original data as is. Ask students why this might be the case. Students will likely highlight that we may want to go back and look at the original data in case we made a mistake or think we missed something along the way. In order to do this ask students to create a new spreadsheet where they can copy in data. This way the original data will be available to reference if needed.

In small groups, share the video about merging the data. Then have students work on merging the three months of data into a single spreadsheet and also joining this dataset with the temperature dataset for the store location. (Slide) For this project, we recommend two computers per group, so each pair of students has a computer to look at. Each member of a group should be working in the same spreadsheet as the rest of their group members (Each group has their own spreadsheet). Encourage students to share how they are doing tasks with each other as they work, to ensure that every group member understands how to work in the spreadsheet.

As you are circulating, notice if there are data moves that groups are struggling with. Encourage students to check in with all their group members before asking you questions. There is likely one person in their group that will know how to do the necessary actions in the spreadsheet such as selecting cells, copying, pasting, etc. Encourage students to record notes on their data cleaning moves in their journal or notebook.

After students have merged the data into one spreadsheet have a discussion with the class. What kinds of moves did they use to clean the dataset? What are they helpful for? What do we have to be careful about as we do this work? (e.g. sorting in the same order when adding a column so it matches when you copy it over) Are there ways we can check that we didn’t make mistakes? (Slide)

Remind students that they will be writing a business report for Me and the Bees that analyzes the data and gives insight and recommendations about Lemonade sales based on the variables given. This report will reveal your findings: including visuals, analysis, and recommendations for the company on how to increase their sales. 

Let students know that in this section they will be working on Modeling the data (Data Science Process Visual) to find patterns using functions, filters, visuals and color coding. Tell them about the Spreadsheet Modeling Tool Guide they will create as a class which will be a tool they can use on this project and future projects. Let them know that they will work in groups on one of the spreadsheet modeling tools to make sense of how it works to teach it to the other groups. Highlight that each group will present on what they learn. (Slide)

Spreadsheet Modeling Tools:

1. Add a new column to calculate the revenue for each day.
2. Use conditional formatting to color code a column of your choice.
3. Sort the data in a different order to get a new perspective on it (for example: by day of the week or temperature rather than date).
4. Filter the data to only see that subset and answer your question.
5. Summary statistics functions: MIN, MAX, MEAN, MEDIAN, MODE.
6. Make a graph comparing two variables of your choice.

Assign each group one of the spreadsheet modeling tools. Set students up to work on their assigned task to figure out how to use the modeling tool, analyze what they learn from the information and then teach the class. Ask them to be prepared to explain to the class. Distribute the handout Modeling Data with Spreadsheets. Create a class copy of Spreadsheet Modeling Tools Guide Slideshow  and share with students. Add the link for the class copy to the handout.

As students are working together, encourage them to talk with each other and explain what they are doing as they are working in the spreadsheet. If students are struggling with how to do one of their tasks, encourage them to look up the topic and see if there are any helpful tutorials to reference. Many times when we are working with new technology in this course and elsewhere we may struggle with how to do something. Searching on the internet for more information is something that data scientists do as well. This will be a valuable approach to become comfortable with throughout this course and beyond. We have optional video tutorials for working with Google Sheets. They are linked with each task on the handout Modeling Data with Spreadsheets or can be found here.

Once all groups have had an opportunity to figure out how to do their modeling tool, analyze their findings, and decide how they will present it, have groups present to the class. Give the rest of the class time to try the modeling tool after each group has presented. Have the group walk around the class and respond to questions that come up as students are trying the tool. By the end, each group should have worked on each of these tasks in their own spreadsheets.

After each group has presented. Highlight for students that everything that was presented on the spreadsheet modeling strategies will be accessible as a resource for them when they work with their partner to write their business report for Me and the Bees. Give groups time to make any final changes to the slideshow.

Share the Data Science Process visual with students. Let students know that in this process, they have been asking questions of the data, organizing data, and using visuals and functions to model the data. They have also begun to analyze their findings. They will continue the work of modeling the data, analyzing and synthesizing their findings, and finally communicating their findings in the next part of this project. 

For this final activity in this project, divide groups into two pairs. Students will be working in these pairs to write a business report for Me and the Bees. Give them the activity ‘Me and the Bees Business Report’ (Handout 10) and criteria. In this activity, students will use the tools and strategies they learned for working in a spreadsheet, continuing to look for patterns in the data. Encourage students to keep a record of their visuals and findings. Students will then write up a business report based on what they learned from the data, sharing both findings and recommendations. Note: pairs will need to make a copy of their group spreadsheet to continue their modeling in pairs.

As pairs are working, students are welcome to reach out to the other pair in their group to discuss, ask questions and share findings. Encourage students to include the features of the business report they looked at earlier in the unit: concise, clear, visuals, color, graphs as supporting evidence, bullet points, headings, etc.

After students have completed their report, give students time to complete the self-assessment portion of the Me and the Bees (Unit 1): Criteria and Feedback Rubric.

Give students an opportunity to add their work from this unit to their portfolio in Google Slides. They will add one or two slides for Unit 1 that contain links to their slides from the unit and their individual reflection on the data science process. Invite students to be creative and include visuals on the slides that capture their learning from the unit.

In addition, students can link the Concept Organizer document into their Unit 1 slides.