uccser/cs-field-guide

# Data and Information

## Overview

- EU 3.1 People use computer programs to process information to gain insight and knowledge.
- EU 3.2 Computing facilitates exploration and the discovery of connections in information.
- EU 3.3 There are trade-offs when representing information as digital data.

## Reading from the Computer Science Field Guide

Start by reading through:

- [Data Representation]('chapters:chapter' 'data-representation')
- [Coding]('chapters:chapter' 'coding-introduction')
- [Compression]('chapters:chapter' 'coding-compression')
- [Encryption]('chapters:chapter' 'coding-encryption')
- [Error Control]('chapters:chapter' 'coding-error-control')
- [Human Computer Interaction]('chapters:chapter' 'human-computer-interaction')

## Learning objectives

The above chapter readings include specific knowledge for EKs marked in bold. Work to include unmarked learning objectives in the CS Field Guide is currently in progress.

### LO 3.1.1 Find patterns and test hypotheses about digitally processed information to gain insight and knowledge.

- EK 3.1.1A Computers are used in an iterative and interactive way when processing digital information to gain insight and knowledge.
- EK 3.1.1B Digital information can be filtered and cleaned by using computers to process information.
- EK 3.1.1C Combining data sources, clustering data, and data classification are part of the process of using computers to process information.
- EK 3.1.1D Insight and knowledge can be obtained from translating and transforming digitally represented information.
- EK 3.1.1E Patterns can emerge when data is transformed using computational tools.

### LO 3.1.2 Collaborate when processing information to gain insight and knowledge.

- EK 3.1.2A Collaboration is an important part of solving data-driven problems.
- EK 3.1.2B Collaboration facilitates solving computational problems by applying multiple perspectives, experiences, and skill sets.
- EK 3.1.2C Communication between participants working on data-driven problems gives rise
to enhanced insights and knowledge.
- EK 3.1.2D Collaboration in developing hypotheses and questions, and in testing hypotheses
and answering questions, about data helps participants gain insight and knowledge.
- EK 3.1.2E Collaborating face-to-face and using online collaborative tools can facilitate processing information to gain insight and knowledge.
- EK 3.1.2F Investigating large data sets collaboratively can lead to insight and knowledge not obtained when working alone.

### LO 3.1.3 Explain the insight and knowledge gained from digitally processed data by using appropriate visualizations, notations, and precise language.

- EK 3.1.3A Visualization tools and software can communicate information about data.
- EK 3.1.3B Tables, diagrams, and textual displays can be used in communicating insight and knowledge gained from data.
- EK 3.1.3C Summaries of data analyzed computationally can be effective in communicating insight and knowledge gained from digitally represented information.
- EK 3.1.3D Transforming information can be effective in communicating knowledge gained from data.
- EK 3.1.3E Interactivity with data is an aspect of communicating.

### LO 3.2.1 Extract information from data to discover and explain connections or trends.

- EK 3.2.1A Large data sets provide opportunities and challenges for extracting information and knowledge.
- EK 3.2.1B Large data sets provide opportunities for identifying trends, making connections
in data, and solving problems.
- EK 3.2.1C Computing tools facilitate the discovery of connections in information within large data sets.
- EK 3.2.1D Search tools are essential for efficiently finding information.
- EK 3.2.1E Information filtering systems are important tools for finding information and recognizing patterns in the information.
- EK 3.2.1F Software tools, including spreadsheets and databases, help to efficiently organize and find trends in information.

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# EXCLUSION STATEMENT (for EK 3.2.1F):
  
Students are not expected to know specific formulas or options available in spreadsheet or database software packages.

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- EK 3.2.1G Metadata is data about data.
- EK 3.2.1H Metadata can be descriptive data about an image, a webpage, or other complex objects.
- EK 3.2.1I Metadata can increase the effective use of data or data sets by providing additional information about various aspects of that data.

### LO 3.2.2. Determine how large data sets impact the use of computational processes to discover information and knowledge.

- EK 3.2.2A Large data sets include data such as transactions, measurements, texts, sounds, images, and videos.
- EK 3.2.2B The storing, processing, and curating of large data sets is challenging.
- EK 3.2.2C Structuring large data sets for analysis can be challenging.
- EK 3.2.2D Maintaining privacy of large data sets containing personal information can be challenging.
- EK 3.2.2E Scalability of systems is an important consideration when data sets are large.
- EK 3.2.2F The size or scale of a system that stores data affects how that data set is used.
- EK 3.2.2G The effective use of large data sets requires computational solutions.
- EK 3.2.2H Analytical techniques to store, manage, transmit, and process data sets change as the size of data sets scale.

### LO 3.3.1 Analyze how data representation, storage, security, and transmission of data involve computational manipulation of information.

- **EK 3.3.1A Digital data representations involve trade-offs related to storage, security, and privacy concerns.**
- **EK 3.3.1B Security concerns engender trade-offs in storing and transmitting information.**
- **EK 3.3.1C There are trade-offs in using lossy and lossless compression techniques for storing and transmitting data.**