Unit9

Unit 9 - Data

Unit Overview

Students explore and visualize datasets from a wide variety of topics as they hunt for patterns and try to learn more about the world around them from the data. Once again, students work with datasets in App Lab, but are now asked to make use of a data visualizer tool that assists students in finding data patterns. They learn how different types of visualizations can be used to better understand the patterns contained in datasets and how to use visualizations when investigating hypotheses. At the conclusion of the unit, students learn about the impacts of data analysis on the world around them and complete a final project in which they must uncover and present a data investigation they've completed independently.

Unit Philosophy and Pedagogy

  • The Data Analysis Process: This unit is built around a data analysis process that helps students break down how data is turned into new information about the world. Some lessons are designed around different steps of this process, like cleaning data or building visualizations. Other lessons focus on ways this process is applied in the real world in contexts like citizen science or machine learning. The data analysis process helps provide a consistent reference point as students explore the importance of data analysis in computing.

  • Exploring Data with the Data Visualizer: The Data Visualizer is a tool built into App Lab that allows students to quickly create visualizations of the data they've added to their projects. The set of possible visualizations is intentionally limited to a few ways to change or modify the chart. The goal of this tool is to encourage the exploration of the different kinds of questions that can be answered with data visualizations, with a greater emphasis on students’ ability to quickly create a variety of visualizations.

Major Assessment and Projects

Students use the data visualizer to find and present a data story. Using what they've learned about the data analysis process, students either choose a dataset inside the data library, or upload one, of their own and create visualizations that find interesting patterns that possibly reveal new insights and knowledge. Students complete an activity guide describing their findings and the process they used in identifying them. Students will also complete an end-of-unit assessment aligned with CS Principles framework objectives covered in this unit.

AP Connections

This unit and unit project helps build towards the enduring understandings listed below. For a detailed mapping of units to Learning Objectives and EKs please see the "Standards" page for this unit.

  • DAT-2: Programs can be used to process data, which allows users to discover information and create new knowledge.
  • IOC-1: While computing innovations are typically designed to achieve a specific purpose, they may have unintended consequences.

This unit includes content from the following topics from the AP CS Principles Framework. For more detailed information on topic coverage in the course review Code.org CSP Topic Coverage.

  • 2.3 Extracting Information from Data
  • 2.4 Using Programs with Data
  • 5.3 Computing Bias
  • 5.4 Crowdsourcing

Week 1

Lesson 1: Learning from Data

  • Warm Up (5 mins)
  • Activity (30 mins)
  • Wrap Up (10 mins)

Learn how...

Teacher Links: Presentation Students Links: Link

Lesson 2: Exploring One Column

  • Warm Up (5 mins)
  • Activity (35 mins)
  • Wrap Up (5 mins)

Learn how...

Teacher Links: Presentation Students Links: Activity Guide

Lesson 3: Filtering and Cleaning Data

  • Warm Up (2 mins)
  • Activity (33 mins)
  • Wrap Up (10 mins)

Learn how...

Teacher Links: Presentation Students Links: Activity Guide

Lesson 4: Exploring Two Columns

  • Warm Up (5 mins)
  • Activity (30 mins)
  • Wrap Up (10 mins)

Learn how...

Teacher Links: Presentation Students Links: Activity Guide

Lesson 5: Big, Open, and Crowdsourced Data

  • Warm Up (5 mins)
  • Activity (30 mins)
  • Wrap Up (5 mins)

Learn how big data, open data, and crowdsourcing apply the data analysis process in real world context to solve problems that matter.

Teacher Links: Presentation Students Links: Activity Guide

Week 2

Lesson 6: Machine Learning and Bias

  • Warm Up (5 mins)
  • Activity (35 mins)
  • Wrap Up (5 mins)

In this lesson, students are introduced to the concepts of Artificial Intelligence and Machine Learning using the AI for Oceans widget. First students classify objects as either "fish" or "not fish" to attempt to remove trash from the ocean. Then, students will need to expand their training data set to include other sea creatures that belong in the water. In the second part of the activity, students will choose their own labels to apply to images of randomly generated fish. This training data is used for a machine learning model that should then be able to label new images on its own.

Teacher Links: Presentation

Lesson 7: Project - Tell a Data Story Part 1

  • Warm Up (2 mins)
  • Activity (43 mins)
  • Wrap Up (0 mins)

Learn how...

Teacher Links: Presentation Students Links: Activity Guide

Lesson 8: Project - Tell a Data Story Part 2

  • Warm Up (0 mins)
  • Activity (35 mins)
  • Wrap Up (10 mins)

Learn how...

Teacher Links: Presentation Students Links: Activity Guide

Lesson 9: Assessment Day

  • Assessment (25 mins)
  • Assessment Review (20 mins)

Students complete a multiple choice assessment which covers the unit topics.