This lesson gets into the basic mechanics of working with variables in programs. The lesson shows students how to create and assign values to variables and navigates through a series of common misconceptions about variables and how they work. Along the way, the lesson tries to build up the student’s mental model of how computers and programs work, which is essential for being able to reason about programs.

In this lesson, students add variables to two different exemplar apps to keep track of a score, or a count of some number of button clicks. The major topic is **variable scope** and understanding the differences, benefits, and drawbacks, of using global versus local variables. This lesson focuses more on using global variables, since in event-driven apps that’s what you need to keep track of data across multiple events.

The very basics of a **simple if statement** are also presented in this lesson, mostly to highlight the difference between the = and == operators. Finally, students are asked to apply what they’ve learned about variables, scope, and if statements, to make their own “clicker” game modeled after one of the exemplars they saw during the lesson.

In this lesson, students are introduced to the string data type as a way of representing arbitrary sequences of ASCII characters. They will use strings to accept input from a user as they work on mastering two new UI elements, the text input and the text area. Students combine these skills to develop a simple Mad Libs® app.

_Mad Libs® is a trademark of the Penguin Group (USA) LLC., which does not sponsor, authorize or endorse this site._

We take a whole lesson to learn about `if` statements, what they are, the terminology around them, and what they have to do with "selection" in programs. Students trace simple robot programs on paper to develop a sense of how to read and reason about code with `if` statements in it. Students also try their hand at writing code by hand to handle a robot situation.

In this lesson, students write `if` and `if-else` statements in JavaScript for the first time. The concepts of conditional execution should carry over from the previous lesson, leaving this lesson to get into the nitty gritty details of writing working code. Students will write code in a series of "toy" problems setup for them in App Lab that require students to do everything from debug common problems, write simple programs that output to the console, or implement the conditional logic into an existing app or game, like "Password Checker" or a simple Dice Game. The lesson ends with a problem requiring nested `if` statements to foreshadow the next lesson.

In this lesson, students will be introduced to the boolean (logic) operators NOT, AND, and OR as tools for creating compound boolean conditions in if statements. Students will learn how to more efficiently express complex logic using AND and OR, rather than deeply nested or chained conditionals. Students will work through a worksheet that covers the basics and a few problems with evaluating logical expressions, then write code in App Lab to practice using && and || in if statements. Finally, students will improve the Movie Bot so it can respond to multiple keywords and provide recommendations based on both the genre and rating provided by the user.

This lesson contains a series of activities you can use to help students familiarize themselves with Explore Performance Task, how it is scored, and some example tasks provided by the College Board.

Students review the Submission Requirements and Scoring Guidelines for the Explore PT. Subsequently they review three example scored Explore PT submissions with commentary to better understand how the Submission Requirements and Scoring Guidelines are used together. In a wrap-up conversation they identify a piece of advice, a "gotcha", and a remaining question they have about the Explore PT.

Note: Most the exemplar task, scores, and commentary on scoring shared in this lesson come directly from the College Board. Code.org's commentary is noted where applicable.

This lesson uses the [r explore-pt-survival-guide] as the backbone for a series of activities to ramp up to doing the actual Explore PT. It contains some brainstorming activities around what qualifies as a "computing innovation" for the task as well practical advice and strategies for handling some of the nuances of the task such as distinguising between a "harmful effect" and a "data security or privacy concern."

The lesson concludes by providing students with resources to make a plan to complete the task staring in the next lesson.

It is finally time for students to take on the Explore Performance Task. For a minimum of 8 class hours, students should work on their projects with only types of teacher support allowed (essentially: Advise on process, don’t influence or evaluate ideas).

The lesson includes reminders about how you can interact with students while they are working on their projects, and suggestions about time line. The Explore PT requires a minimum of 8 hours of class time. At the end, students will submit their computational artifact and written responses through their AP digital portfolio.

This lesson contains a series of activities you can use to help students familiarize themselves with Create Performance Task, how it is scored, and some example tasks provided by the College Board.

Students review the Submission Requirements and Scoring Guidelines for the Create PT. Subsequently they review three example scored Create PT submissions with commentary to better understand how the Submission Requirements and Scoring Guidelines are used together. In a wrap-up conversation they identify a piece of advice, a "gotcha", and a remaining question they have about the Create PT.

Note: Much the sample tasks, scores, and commentary on scoring shared in this lesson come directly from the College Board. Code.org's commentary is noted where applicable.

This lesson uses the [r create-pt-survival-guide] as the backbone for a series of activities to ramp up to doing the actual Create PT. It contains activities to help students understand the algorithm and abstraction requirements of the task, as well as activities to help them narrow down and brainstorm ideas for their actual project.

The lesson concludes by providing students with resources to make a plan to complete the task staring in the next lesson.

It is finally time for students to take on the Create Performance Task. For a total of 12 class hours, students should work on their projects with only types of teacher support allowed (essentially: Advise on process, don’t influence or evaluate ideas). Students may also work with a collaborative partner in *in development of their program* - written responses must be done on their own.

The lesson includes reminders about how you can interact with students while they are working on their projects, and suggestions about time line. The Create PT requires a minimum of 12 hours of class time. At the end, students will submit their program code, program video, and written responses through their AP digital portfolio.

In this kickoff to the Data Unit, students begin thinking about how data is collected and what can be learned from it. To begin the lesson, students will take a short online quiz that supposedly determines something interesting or funny about their personality. Afterwards they will brainstorm other sources of data in the world around them, leading to a discussion of how that data is collected. This discussion motivates the introduction of the Class Data Tracker project that will run through the second half of this unit. Students will take the survey for the first time and be shown what the results will look like. To close the class, students will make predictions of what they will find when all the data has been collected in a couple weeks.

This is a pretty fun lesson that has two main parts. First students warm up by reflecting on the reasons data visualizations are used to communicate about data. This leads to the main activity in which students look at some collections of (mostly bad) data visualizations, rate them, explain why a good one is effective, and also suggest a fix for a bad one.

In the second part of class students compare their experiences and create a class list of common faults and best practices for creating data visualizations. Finally, students review and read the first few pages of **Data Visualization 101: How to design charts and graphs** to see some basic principles of good data visualizations and see how they compare with the list the class came up with.

Now that students have had the chance to see and evaluate various data visualizations, they will learn to make visualizations of their own. This lesson teaches students how to build visualizations from provided datasets. The levels in Code Studio provide a detailed walkthrough of how to use Google Sheets to create several different kinds of charts. While this lesson focuses on the Google Sheets tool, other tools may be substituted at the teacher’s discretion, and MS Excel support is coming soon to the lesson.

The main activity teaches students to build different chart types (scatter, line, and bar charts) from a single data set. It should be emphasized to students that the purpose of this lesson is to explore and experiment with creating different types of visualizations, not to build the perfect chart. Students will have a chance to create and customize their own charts. At the end of class, students compare their custom visualizations with those of their classmates.

In this lesson, students will collaboratively investigate some datasets and use visualization tools to “discover a data story.” The lesson assumes that students know how to use some kind of visualization tool - in the previous lesson we used the charting tools of a basic spreadsheet program. Students should be working with a partner but without much teacher hand-holding. Most of the time should be spent with students poking around the data and trying to discover connections and trends using data visualization tools. It is up to them to discover a trend, make a chart, and accurately write about it.

In this lesson, students begin working with the data that they have been collecting since the first lesson of the chapter in the class "data tracker." They are introduced to the first step in analyzing data: cleaning the data. Students will follow a guide in Code Studio, which demonstrates the common techniques of filtering and sorting data to familiarize themselves with its contents. Then they will correct errors they find in the data by either hand-correcting invalid values or deleting them. Finally they will categorize any free-text columns that were collected to prepare them for analysis. This lesson introduces many new skills with spreadsheets and reveals the sometimes subjective nature of data analysis.

In this lesson students learn how create their own summary tables from raw data. A summary table typically represents one or more aggregations (groupings of items) and computations that are performed on the raw dataset. In most spreadsheet programs, a summary table is called a pivot table. In the lesson, students learn how to make pivot tables in Google Sheets using a provided dataset. Then students turn to the data they’ve collected as a class and, with their partner, use pivot tables to investigate it further.

For this Practice PT students will analyze the data that they have been collecting as a class in order to demonstrate their ability to discover, visualize, and present a trend or pattern they find in the data. Leading up to this lesson, students will have been working in pairs to clean and summarize their data. Students should complete this project individually but can get feedback on their ideas from their data-cleaning partner.

**Note**: This is NOT the official AP® Performance Task that will be submitted as part of the Advanced Placement exam; it is a practice activity intended to prepare students for some portions of their individual performance at a later time.