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Lesson 1: Data Trails

Lesson 1: Data Trails


Students will understand what are data, how they are collected, and possible effects of sharing data.


  1. Video: The Target Story found at:

  2. Data Science (DS) journal (quad-ruled composition book or similar); MUST be available for every lesson

  3. Data Diary handout (LMR_1.1_Data Diary)

  4. Video: Terms and Conditions found at:


data observations data trails privacy

Essential Concepts:

Essential Concepts:

Data are a collection of recorded observations. Data are gathered by people and by sensors. Patterns in data can reveal previously unknown patterns in our world. Data play a large, and sometimes invisible, role in our lives.


Before implementing the IDS curriculum, ensure that:

a) Students have been placed in teams and each student understands his or her role in the team.

b) Each student knows who his/her partner is within each team.

c) Expectations regarding collaborative teamwork are discussed and understood (see Team Roles in Teacher Resources).

  1. Introduce the lesson by showing The Target Story video:

  2. In pairs, ask students to discuss the following question using the TPS strategy (see Instructional Strategies in Teacher Resources):

    1. How do you think Target knew about the daughter? In other words, how did Target know the daughter was pregnant before her father did? Target used the information gathered from the daughter’s Red Card and compared it to information about other shoppers. Typically, women who bought those particular products were pregnant.
  3. After students have had time to share their responses, engage in a whole class discussion regarding:

    1. What are data? Data are information, or observations, that have been gathered and recorded.

    2. Where do data come from? Data can come from a variety of places. Some examples might include: cell phones, computers, school records, surveys, etc.

    3. Give an example of data. Answers will vary. One example might be information about a person – including their age, height, weight, eye color, etc.

    4. Give an example of something that is not data (e.g., something that was never written down). Answers will vary. One example might be just watching an event happen. If it wasn’t recorded in some way, it cannot be counted as data.

  4. Explain to the students that we create "data trails" as we go through life. A data trail is the data collected about us as individuals that could be used to see the patterns in our personal lives. Inform the students that they will learn about their own data trails by keeping a data diary and logging entries over the next 24 hours. It is likely that students do not realize how often they leave a data trail or what information is being collected about them on a regular basis.

  5. Distribute the Data Diary handout (LMR_1.1) and be sure to go over the instructions, along with the first example to give the students an idea of how to proceed.

  6. Inform the students that you will collect the handouts during the next class in order to assess their understanding of data.

  7. To get students thinking about what happens to their data, show the Terms and Conditions video:

  8. Engage the students in a whole class discussion about the video, particularly noting:

    1. What terms in the privacy statements were concerning or worrisome?

    2. Do you read the agreements when you download phone apps?

Class Scribes:

One team of students will give a brief talk to discuss what they think the 3 most important topics of the day were. Be prepared to facilitate a good discussion and to ask probing questions in order for students to elaborate on their thinking so that vague responses such as “we learned about data” can be avoided.


Students will complete the Data Diary handout. When grading the homework, be aware of whether the data really could be collected, and whether the students' ideas about how the data might be used are reasonable. For instance, students will often imagine that there is a "spy" watching them; this is not what we are after. We are after actual instances in which sensors or electronic surveillance records their actions or records information about them. For example, "someone saw me going into the store" is not valid data for this exercise, but "a camera recorded me entering the store" is valid data.