DAT - Data Analysis (Module Overview)
Data Analysis
Introduction
The advent of computers has allowed the storage of massive amounts of data. In this module, you will explore how large data files provide knowledge, connections, trends. We will examine the storage of the data and how the storage or presentation of data may tell different stories.
Essential Questions
- How are vastly different kinds of data, physical phenomena, and mathematical concepts represented on a computer?
- How does abstraction help us in writing programs, creating computational artifacts, and solving problems?
- How can computational models and simulations help generate new understanding and knowledge?
- How can computation be employed to help people process data and information to gain insight and knowledge?
- How can computation be employed to facilitate exploration and discovery when working with data?
- What considerations and trade-offs arise in the computational manipulation of data?
- What opportunities do large data sets provide for solving problems and creating knowledge?
- How have computing innovations impacted other fields?
- What are the computing innovations beneficial and harmful effects?
Key Terms
Cell - the data found at the intersection of a row and a column.
Column - all of the data found in one vertical line of cells.
Data Mining - a process of companies to collect and analyze data of their customers to find trends, patterns, and other relationships between data elements.
Depiction - how data is displayed.
Horizontal Axis - data labeling running from side to side.
Label - the description of the data in a horizontal line or a vertical line.
Row - all data found in one horizontal line of cells.
Spreadsheet - a system of data organization in rows, columns, and cells.
Vertical Axis - data labeling running up and down.
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