Chapter 10 summary
Data can be thought of as values that represent concepts, and it can be represented by numbers, text, readings from machines, and values of parameters. Data values can be numeric, textual, or symbolic. This module offered examples of data and it said sometimes that data leads to information and information leads to knowledge, that sound like a quote from Yoda. A dataset is an organized collection of data; I often like data to be organized alphabetically.
Valid data is data that can be agreed upon by objective measures to meaningfully represent the concept that it purports to represent. I believe an example of valid data would be if you were looking for a number and got a number. However, it would be invalid if you got a letter. Metadata is data about data; it seems to me a spreadsheet could be useful for this. Structured data is data that is highly organized. A database is highly organized collection of data arranged as tables, reports, views, queries, and other constructs capable of supporting complex questioning of the data. I really enjoy this type of because I like organization.
Unstructured data is data that is not organized or is very poorly or loosely organized, this would bother my OCD. Data cleansing is the name given to the process of finding and fixing corrupt data from a dataset. That process sounds like defragmenting on a PC. Data anonymization sounds like someone wants to remain anonymous, but why? Data aggregation is the process of compiling information from multiple datasets into a single dataset or database for the purpose of gaining additional insights and information on the entities and concepts described by the data; this sounds useful for solving problems.
Data structures and stacks remind me of those old change holders where they have a slot to put different type of change like pennies dimes quarters and nickels. The Example off the binary tree reminds me off a strand of DNA. Reading about Facebook data policy was informative. It is creepy, and not uncommon, when you verbally talk about something then it appears on your Facebook feed. After reading module 10 I have better knowledge of different Data terminology and know more about data policies on social networking websites. Module 10 was my favorite chapter so far, and the most confusing.