1. 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.

2. CS Principles
U2L13 Cleaning Data

3. U2L13 Cleaning Data Objectives
SWBAT:
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.

4. Discover a Data Story
Being able to look at large sets of data and use visualization as a tool for discovery is a common task that many people who work with data do on a daily basis.
A computer scientist should have decent facility with using tools opening and browsing large datasets, and doing some cursory exploration to see what’s there.
The computer scientist should be familiar enough with the tools to, over time, develop some instincts about data, how it’s collected, the kinds of formats it comes in, and how that affects what can or cannot be done to visualize it.

5. Quick Investigation of a sample dataset
For today’s work there are several datasets for you to choose from.
We’re going to take 5 minutes to poke around in one of the datasets to see how it’s structured.
Then we’ll come back together to get some terms straight before discovering further.
Go to Code Studio
Find the link to the “Personality” dataset and open the folder.
Find and open the README file.
Find and open the rawData.csv file.
Find and open one other .csv file - there are a few.

6. Answer the following questions in your notes:
“What’s the README file?”
“What’s the rawData.csv file?”
“What’s in the other CSV files?”

7. “What’s the README file?”
Most datasets, when you download them, contain a README file.
The README file is just a plain text document that gives some background information about the dataset, how it was collected, and what the column headings mean.
The README is a good first stop when trying to understand exactly what a dataset contains.

8. “What’s the rawData.csv file?”
For the datasets we provide, each folder contains a “raw” dataset, which is the original data, as it was collected.
Recall that .csv stands for “comma-separated values.” CSV is a common, plain text format for distributing datasets.

9. “What’s in the other CSV files?”
The other files are what we call “summary tables.“
These are tables that were created by running some computations on the raw data to do things like count, average, sum, compare, and categorize the data in interesting ways.
It is likely that these summary tables will be the data you use to create your visualizations.

10. Discover a Data Story
Go to code studio and open the file: Activity Guide - Discover a Data Story
Find a partner to explore the data with.
With your partner explore the datasets and choose one you’d like to learn more about. Make sure you
Read the README to understand the raw data that was collected
Look at the summary tables provided for your dataset.
Repeat these steps with additional datasets
Choose one to explore more deeply.

11. Activity Guide - Discover a Data Story
The activity guide asks you to:
Pick a dataset
Use visualization tools to “discover a data story”
Prepare one (or two) to present
Respond to prompts