RECORD THE LECTURE
DATA TYPES AND DATA SYSTEMS
COMP2420/COMP6420 INTRODUCTION TO DATA MANAGEMENT, ANALYSIS AND SECURITY
Copyright By Assignmentchef assignmentchef
WEEK 6 LECTURE 1 Monday 28 March 2022
of Computing
College of Engineering and Computer Science
Credit: (previous course convenor)
HOUSEKEEPING
Midsemester Exam
Timetable released
Thursday 21 April at 1pm (Canberra
15 mins reading 7me and 90 minutes wri7ng 7me
Screen capture set-up required (test it well in advance e.g. on sample exam)
Check sample exam on course site
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Assignment 1
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Census date
31 March is the HECS census date. Last date to drop a course without penalty.
Upcoming public holidays
Easter Monday 18 April
ANZAC Day Monday 25 will be no lectures. A make-up session will be arranged on the corresponding Tuesday at 2pm instead to record a lecture, we will run it live and students are welcomed to join in.
Learning Outcomes
Describe various data types and their 01 differences
Describe what a database is and the various existing database models
03 Explain what data abstraction levels are
Recall what a database management 04 system does and the different possible
architectures
Explain what different database languages are and their use
Aribuon:Slideshare
The data landscape
A schema defines how data is organised
STRUCTURED
Characteristics (structured data)
Organized
Conforms to a format
Machine readable
Easy to store/search/query/analyse
Examples (structured data)
Spreadsheets(debatable,depends) Databases
Censusrecords
Librarycatalogues
SEMI STRUCTURED
Characteristics (semi-structured data)
No formal data model
Has some organisa7onal proper7es (uses metadata)
Self-describing structure
Easier to catalogue/search and analyze than unstructured
Examples (semi-structured data)
XML and JSON documents HTML
NoSQL databases
UNSTRUCTURED
CharacterisFcs (unstructured data)
No associated data model
Usually some minimal structure (mostly free-form)
Most data in real life is unstructured
Examples (unstructured data)
Web content
Social media data
Satellite images
Photographs
Quantitative vs Qualitative data
(revision)
QuanBtaBve Discrete
-ConBnuous
Quantitative vs Qualitative data
(revision)
Qualitative Nominal
-Ordinal (categorical)
A usually large collection of data organized especially for rapid search and retrieval (as by a computer) Merriam-Webster dictionary
Attribution:Database configuration 23
Database Configuration
Database Models
Defines the logical structure of a database and determines the manner in which data can be stored, organized and manipulated.
There are many different types of database models.
Flat Model
Attribution:Flat 25
Hierarchical Model
Attribution:Hierarchical
Network Model
Attribution:Network 27
Relational Model
Attribution:Relational 28
Object-oriented Model
Attribution:Object-oriented 29
Graph Model
Attribution:Graph 30
Data Abstraction Levels
31 Aribuon:Data Abstracon Levels
Database Management System (DBMS)
Consistsofinterrelateddataand software for analysing the data.
Enablesthedefinition,creation, updating, querying and administration of databases
Allowsforsecuredataaccess
MySQL,Postgres,EnterpriseDB, MongoDB, Microsoft SQL Server, Oracle, SQLite and IBM DB2
Architecture 2 tier
Aribuon:2 Tier 33
Architecture 3 tier
34 Attribution:3 Tier
Architecture N tier
35 Attribution:N Tier
Database Languages
Data-definition language
Data-manipulation language
Data-definition language
Specifyadatabaseschema
Addi7onalproper7esofdata
Datastorageandaccessmethods
Consistencyconstraintsonthedata
Domain constraints Referen7al integrity Asser7ons
Authoriza7on
Data- manipulation language
Used to access/manipulate data. Types of access are:
Retrieve, Insert, Delete and Modify
Two basic types:
Procedural
Need to specify what data is required and how to get that data
Declarative
Just specify what data is needed. Not how to get that data
A query is used to retrieve information from a database. It is specified using a query language. The most widely used query language is SQL.
What type of language is SQL Procedural or Declarative?
Data Management intro part
Visualisation notes
01 What is visualisation
02 Why does it matter in
presenting data?
Data types and how it influences visualisation types
04 Bad plots
Attribution:Wikipedia
Internet Partial Map 2005
INTRODUCTION
What is visualization?
Technique to create images, diagrams or animations to communicate a message.
Communication with visual imagery has been used from very beginning to communicate both abstract and concrete ideas.
Cave paintings Hieroglyph Maps
Cave painting
Why does visualization matter?
Large size of data makes it necessary to provide summaries
People prefer to look at pictures rather than numbers
Aids model construction, checking plausibility of model assumptions
Attribution:
Need for visualizaFon
Communicate information
Data presentation visualization Convincing other people it is true
Support reasoning about data
Data exploration visualization Exploring what is true
Communicate information
Attribution:Tufte, Beautiful evidence, pg. 123 s Napoleon map
Support reasoning about data
On January 28, 1986, the space shuttle Challenger exploded because two rubber O-rings leaked due to the very cold temperatures at launch day.
This potential problem was discussed the day before the launch:
Engineers opposed launching based on data from previous launches, and provided 13 charts to NASA to support their case.
However, it is difficult to assess the relationship between temperature and O-ring damage based on these charts.
(One) culprit: what refers to as chartjunk
(Another) culprit: what Tufte refers to as the cognitive style
of powerpoint
A visual display of the data from the investigation after the launch was provided. The poor design and use of chart junk makes it difficult to assess the relationship between temperature and O-ring damage.
Aribuon:TuHe, Visual explanaons, pg 46
Support reasoning about about data
Attribution:Tufte, Visual explanations, pg 45
DATA TYPES
Data types
Nominal: categorical data, no ordering Example Fruits{Apple, Oranges, Grapes}
Operations =, !=
Ordinal: categorical data, ordered Example Ratings{Poor, Ok, Good} Operations =, !=, >, <, >=, <=Interval: numerical data, zero has no meaningExample – latitude and longitude Operations – =, !=, >, <, >=, <=, +, -Ratio: numerical data, zero has special meaningExample – weight of person Operations – =, !=, >, <, >=, <=, +, -, /, * Titanic Dataset VISUALIZATION TYPESBasic plot typesWell now discuss some basic plot types1D – bar chart, histogram2D – scatter plot, line plot, box and whisker plot, heatmap3D+ – scatter matrix, bubble chartWhich plot is appropriate depends on:univariate or bivariate data discrete/categorical or continuousMost plots are easy to create (hard to make them aesthetically elegant) ScaGer plotBubble plot Color scaGer plot3D scaGer plot 3D scatterChart junkchartjunk: unnecessary graphics on visualizations. Doesnt convey additional information but distracts from the pointThe chart on the left is mostly chartjunk Attribution: To keep in mindVisualiza7ons should enhance understanding, not create confusionS7cking to the basic plot types is beneficial and enhances understandingIf a visualiza7on doesnt give you any new informa7on, ask yourself – Do I need some other visualiza
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