Introduce students to nodes and graph theory and their use in operations research. Show how Dijkstra’s Algorithm can be used to find the shortest or quickest route between nodes in a network.
Software failures happen all the time. Sometimes it’s a little bug that makes a program difficult to use; other times an error might crash your entire computer. Some software failures are more spectacular than others.
In 1996, The ARIANE 5 rocket of the European Space Agency was launched for its first test flight: Countdown, ignition, flame and smoke, soaring rocket... then BANG! Lots of little pieces scattered through the South American rainforest. Investigators had to piece together what happened and finally tracked down this tiny, irrelevant bug. A piece of software onboard the rocket which was not even needed had reported a value that was too big to be stored. An error was stored instead, but other software interpreted the error as saying the rocket was 90 degrees off course. Thankfully, no one was on board but the failure still caused about $370 million of damage.
Software engineering is all about how we can create software despite this enormous size and complexity while hopefully get a working product in the end. It was first introduced as a topic of computer science in the 1960s during the so-called "software crisis" when people realized that the capability of hardware was increasing at incredible speeds while our ability to develop software is staying pretty much the same.
As the name software engineering suggests, we are taking ideas and processes from other engineering disciplines (such as building bridges or computer hardware) and applying them to software. Having a structured process in place for developing software turns out to be hugely important because it allows us to manage the size and complexity of software. As a result of advances in software engineering, there are many success stories of large and complex software products that work well and contain few bugs. For example, Google's huge projects (Google search, Gmail, etc.) are built by teams of thousands of engineers, yet they still manage to create software that does what it should.
Students use the Google Trends tool in order to visualize historical search data. They will need to identify interesting trends or patterns in their findings and will attempt to explain those trends, based on their own experience or through further research online. Afterward, students will present their findings to ensure they are correctly identifying patterns in a visualization and are providing plausible explanations of those patterns.
Students will be able to:- use Google Trends to identify and explore connections and patterns within a data visualization.- accurately describe what a data visualization of a trend is showing.- provide plausible explanations of trends and patterns observed within a data visualization.
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This lesson asks students to consider carefully the assumptions they make when interpreting data and data visualizations. The class begins by examining how the Google Flu Trends project tried and failed to use search trends to predict flu outbreaks. They will then read a report on the Digital Divide which highlights how access to technology differs widely by personal characteristics like race and income. This report challenges the widespread assumption that data collected online is representative of the population at large. To practice identifying assumptions in data analysis, students are provided with a series of scenarios in which data-driven decisions are made based on flawed assumptions. They will need to identify the assumptions being made (most notably those related to the digital divide) and explain why these assumptions lead to incorrect conclusions.
Students will be able to:- define the digital divide as the variation in access or use of technology by various demographic characteristics.- identify assumptions made when drawing conclusions from data and data visualizations.
In this lesson, students gain more practice using while loops as they develop a simulation that repeatedly flips coins until certain conditions are met. The lesson begins with an unplugged activity in which students flip a coin until they get five heads in total, and then again until they get three heads in a row. They will then compete to predict the highest outcome in the class for each statistic. This activity motivates the programming component of the lesson in which students develop a program that allows them to simulate this experiment for higher numbers of heads and longer streaks.
Students will be able to:- use a while loop in a program to repeatedly call a block of code.- use variables, iteration, and conditional logic within a loop to record the results of a repeated process.- identify instances where a simulation might be useful to learn more about real-world phenomena.- develop a simulation of a simple real-world phenomenon.
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 of weeks.
Students will be able to:- develop a hypothesis about student behavior over time, based on a small sample of data.- describe sources of data appropriate for performing computations.
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 the 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.
Students will be able to:- identify an effective data visualization and give justification.- collaborate to investigate and evaluate a data visualization.- suggest an appropriate visualization for some data.- evaluate a data visualization for the effectiveness of communication.- identify a poor data visualization and give justification.
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.
Students will be able to:- select the appropriate type of data visualization to discover trends and patterns within a dataset.- create a bar, line, and scatter chart from a dataset using a computational tool.- use the settings of a data visualization tool to manipulate and refine the features of a data visualization.
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.
Students will be able to:- collaboratively investigate a dataset.- create a visualization (chart) from provided data.- identify possible trends or connections in a data set by creating visualizations of it.- accurately communicate about a visualization of their own creation.
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.
Students will be able to:- filter and sort a dataset using a spreadsheet tool.- identify and correct invalid values in a dataset with the aid of computational tools- justify the need to clean data prior to analyzing it with computational tools.
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.
Students will be able to:- create summaries of a dataset using a pivot table.- manipulate and clean data in order to prepare it for analysis.- explain the process used to create a visualization.- design a visualization that clearly presents a trend, pattern, or relationship within a dataset.- create visualizations of a dataset in order to discover trends and patterns.- draw conclusions from the contents of a data visualization.
