Vocabulary byte - The technical term for 8 bits of data. The standard fundamental unit (or “chunk size”) underlying most computing systems today measured in “megabytes”, “kilobytes”, “gigabytes”, etc. Image - A type of data used for graphics or pictures. metadata - is data that describes other data. For example, a digital image my include metadata that describe the size of the image, number of colors, or resolution. pixel - short for "picture element" it is the fundamental unit of a digital image, typically a tiny square or dot which contains a single point of color of a larger image. RGB - the RGB color model uses varying intensities of (R)ed, (G)reen, and (B)lue light are added together in to reproduce a broad array of colors. Lossless Compression - a data compression algorithm that allows the original data to be perfectly reconstructed from the compressed data. Lossy Compression - (or irreversible compression) a data compression method that uses inexact approximations, discarding some data to represent the content. Most commonly seen in image formats like .jpg. Abstraction - Pulling out specific differences to make one solution work for multiple problems.

U2L9 Check Your Assumptions CS Principles U2L9 Check Your Assumptions

U2L9 Check Your Assumptions Objectives SWBAT: 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

U2L9 Content: Assumptions about data We (all people) make assumptions when looking at data. Some of these assumptions lie hidden beneath the surface and we want to shed some light on them by looking at some examples from the news. This is a useful mode of reflection that will help when doing reflective writing on the performance tasks. Analyzing and interpreting data will typically require some assumptions to be made about the accuracy of the data and the cause of the relationships observed within it. When decisions are made based on a collection of data, they will often rest just as much on that set of assumptions about the data as the data itself. Identifying and validating (or disproving) assumptions is therefore an important part of data analysis. Furthermore, clear communication about how data was interpreted should also include an account of the assumptions made along the way.

U2L9: Prompt 1 Watch the Video: Google Trends Video, then respond to the following prompt. Thinking Prompt: What are the potential beneficial effects of using a tool like Google Flu Trends?

U2L9 Prompt2 Read one of the articles http://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/ http://bits.blogs.nytimes.com/2014/03/28/google-flu-trends-the-limits-of-big-data/ http://www.nature.com/news/when-google-got-flu-wrong-1.12413 http://time.com/23782/google-flu-trends-big-data-problems/ https://hbr.org/2014/03/google-flu-trends-failure-shows-good-data-big-data/ Then respond to the following prompt: “Why did Google Flu Trends eventually fail? What assumptions did they make about their data or their model that ultimately proved not to be true?“

Prompt Discussion: Google flu trends Google Flu Trends worked well in some instances but often over-estimated, under- estimated, or entirely missed flu outbreaks. A notable example occurred when Google Flu Trends largely missed the outbreak of the H1N1 flu virus. Just because someone is reading about the flu doesn’t mean they actually have it. Some search terms like “high school basketball” might be good predictors of the flu one year but clearly shouldn’t be used to measure whether someone has the flu. In general, many terms may have been good predictors of the flu for a while only because, like high school basketball, they are more searched in the winter when more people get the flu. Google began recommending searches to users, which skewed what terms people searched for. As a result, the tool was measuring Google-generated suggested searches as well, which skewed results.

U2L9 Activity Open U2L9 - Digital Divide and Checking Assumptions - Activity Guide Part 1: The digital Divide This activity guide begins with a link to a report from Pew Research which examines the “digital divide.” Students should look through the visualizations in this report and record responses to the questions found in the activity guide. Discuss: In small groups or as a class, students should discuss the answers they have recorded in their activity guides. Key points for the following discussion include: Access and use of the Internet differs by income, race, education, age, disability, and geography. As a result, some groups are over- or under-represented when looking at activity online. When we see behavior on the Internet, like search trends, we may be tempted to assume that access to the Internet is universal and so we are taking a representative sample of everyone. In reality, a “digital divide” leads to some groups being over- or under-represented. Some people may not be on the Internet at all. Part 2: Checking Your Assumptions Students should complete the second half of the activity guide. They are presented a set of scenarios in which data was used to make a decision. Students will be asked to examine and critique the assumptions used to make these decisions. Then they will suggest additional data they would like to collect or other ways their decision could be made more reliably.