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Data and Information Lecture Notes

by: Mei Lin

Data and Information Lecture Notes INFO-I101

Marketplace > Indiana University > Information technology > INFO-I101 > Data and Information Lecture Notes
Mei Lin
GPA 3.38

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About this Document

INFO-I 101 Lecture portion notes.
Introduction to Informatics and Computing
Nina Onesti and Dan Richert
Class Notes
Informatics, INFO-I101, I101, data, info
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This 2 page Class Notes was uploaded by Mei Lin on Friday March 25, 2016. The Class Notes belongs to INFO-I101 at Indiana University taught by Nina Onesti and Dan Richert in Spring 2016. Since its upload, it has received 6 views. For similar materials see Introduction to Informatics and Computing in Information technology at Indiana University.

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Date Created: 03/25/16
Lecture Notes Monday, March 283:44 PM • Pros and cons of data collection ○ Pros:  EX: Kroger's reward card for discounts ○ Cons:  EX: lots of knowledge is collected about user --> ads targeted to you specifically • Where is the data? ○ Data can be altered or can be wrong ○ Security and privacy issues on data • Information Hierarchy ○ Data  Difficult to not be in any database (social media, personal technology, etc.)  3 Types □ Unstructured □ Semi-structured □ Structured ○ Information  "Processed" data □ Organized, selected, analyzed, mined  Meaningful to recipients (companies, mostly) ○ Knowledge  2 Types □ Implicit/Tacit  Implied through the data. Almost guessing □ Explicit  EX: directions on a map  Visualization ◊ Charts, maps, and Google Charts • Prior to Databases ○ Pre 1970  Had to go directly to the source to find out their data  Had to gather data manually ○ 1970 onwards.  E.F. Codd -- The Relational Model □ Created database concept where elements are linked together • KDD (Knowledge Discovery in Database) ○ Average Life of a Fortune 500 Company used to be ~45 years ○ Businesses in Europe and Japan lasted ~12.5 years ○ CAUSE: Data gathered was being misused since company didn't understand the data collected --> business fails due to mistakes ○ Who uses this?  Stores like Wal-Mart --> uses collected data to find out what products sells best at various times --> stock up on those products so they don't run out  Long distance companies  Credit card companies --> company track purchases of customers to check for fraud  Drug manufacturers  Sport teams --> uses stats to improve team ○ Incentives for KDD  Money --> increase profits  Retain customers --> build loyalty  New markets  Product development  Forecasting Data and Information Page 1  Forecasting ○ Non-Linear KDD Process  Problem Statement  Get data  Clean the data □ Null values  EX: customer refuses to give phone number/email/zipcode to company during shopping transaction □ Duplicate data □ Known "wrong" data  EX: fill in random zipcode when customer doesn't want to give theirs during a transactions □ Outliers  Data that doesn't make sense  Transform the data □ Discretize  Grouping similar answers  EX: age 18-25, 26-35, 36-45, etc. □ Change tuple format  Tuple: a record or entry in a database  Mine the data □ 2 examples of data mining algorithms  Association rules ◊ Used to determine what "things" indicate the presence of other "things" ◊ Also known as Market Basket Analysis when applied to purchases ◊ EX: people who bought bread usually also bought milk and eggs, people who bought diapers also buy beer in the same transaction ◊ Grocery store layouts use this data to separate the items usually bought together in order to make people walk around the entire grocery store to encourage impulse buys  Classification ◊ Creates a decision tree from data already in the database ◊ New instances are then placed into a category based on their attributes ◊ NOTE: Categories/trees have 2 properties ◊ Categories must be:  Mutually exclusive – Can only belong to a single category  Collectively Exhaustible – Everything must belong to a category – There cannot be an instance that does not belong to either categories  Knowledge is produced  Take action Data and Information Page 2


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