Applied Computing Unit 1 Exam

AOS 1 + AOS 2

r > 0.5

strong, positive

r between 0.3 and 0.5

moderate, positive

r between 0 and 0.3

weak, positive

r = 0

no correlation

r between 0 and -0.3

weak, negative

r between -0.3 and -0.5

moderate, negative

r < -0.5

strong, negative

perfect positive

perfect negative

strong positive

strong negative

weak positive

weak negative

no correlation

Pros and Cons of Qualitative data

PROS:
- provides depth
- can probe and follow a theme

CONS:
- small sample size, not random
- narrative descriptive results
- results end up being specific to a small group of people, not easy to draw a conclusion

quantitative data

measurable and specific, easy to chart, uses statistics, and data is largely numerical

qualitative data

about qualities and attributes, harder to measure. can be gathered through focus groups, video footage, observation. Needs to be recorded accurately and transcribed at a later date. patterns and themes are required to be found to analyse data

Pros and cons of quantitative data

PROS:
- more willing participants
- can capture a large random sample
- provides statistical validity, helps accurately reflect characteristics of population

CONS:
- no depth
- can be superficial

focus groups

meeting a small group of individuals, guide through questions, loosely structured to allow ideas to flow

interviews

used to elicit people's opinions and beliefs

observation

using senses when participants are in their own environment, can be selective to data chosen to find most important

open questions

do not limit responses, needed to be worded so responses can be interpreted (qualitative data)

closed questions

limit responses, yes/no, multiple choice, scales, agree/disagree (qualitative data)

referencing

- tells audience if the source is reliable or not
- help them find the original source
- acknowledges the intellectual property of the author, protection against plagiarism

quality of data characteristics

relevance, accuracy, bias, reliability, integrity

relevance

must be relevant, data must be processed while it is current, cannot have outdated data

accuracy

must be accurate, otherwise insights will be poor and decisions made from this data may cause integrity issues, must verify against original source or other secondary sources.

bias

can make information unreliable, vested interest, timing and small sample sizes can lead to ____

reliability

data must be gathered form a reliable sites

integrity

refers to the quality of data. the more errors, the lower the ______

data types examples

Text: character / string

Numeric: Integer, floating point (double/float), date/time

Boolean: True/False, yes/no

integer

whole numbers

floating point / float / double

number with a decimal point

string

series of alphanumeric characters (letters, numbers, etc)

boolean

yes or no, true or false

consent forms

ways to collect data legally, informed consent

APP 2

non-identification of individuals; anonymity and pseudonymity - individuals must have the option of dealing anonymously or by pseudonym with an APP entity.

ethical dilemmas

moral standards that govern the way in which we behave

> conflict between 2 principles about which one is more important
> right and wrong based on a person's opinion

categories of data visualisations

explanatory - to set out a point of view (clearly explained, simply laid out, based on known facts)

exploratory - allows users to select value or category to explore connections and relationships

functional requirements (usability + accessibility)

what the data visualisation should do (function/ behaviour)

usability:
- robustness
- flexibility
- ease of use

accessibility
- navigation
- error tolerance

non-functional requirements

alignment, repetition, contrast, space, balance

constraints

economic: time and budget
technical: hardware and software available for the project
usability constraints: how easy people with disabilities or other specific features can use the solution
social, legal constraints: areas other than the hardware or software

scope of the solution

outlines the boundaries or parameters of the solution so all stakeholders are aware of what the solution will contain, planned in the analysis stage.

design tools

appearance:
- layout diagrams
- story boards
- annotated diagrams and mockups

functionality:
- IPO charts (input, process, output charts)
- flow charts

mock up

shown in colour should look like intended representation

formats and convention definition

format - related to font type, size, background colour
conventions - general tules that are followed when using a particular format.

spreadsheets and data bases definition

spreadsheets are not databases.
spreadsheets can lead to redundant / dupllicate data

databases have multiple tables, no storage limit, and allow for easy/effective retrieval of data

validation of data

Ensures data integrity by checking existence, type, range, and format

functional requirements

directly related to what the solution will do, they typically involve calculations, data processing, data manipulation. THE INPUT REQUIRED

non-functional requirements definition

what the client would like the solution to have, but does not affect what the solution does. often tied to constraints of a system. can be categorised into terms of:
USABILITY (how easy it is to use)
RELIABILITY (how much it can be depended upon)
PORTABILITY (how easily it can be used in different operating systems)
ROBUSTNESS (how well it responds to errors)
MAINTAINABILITY (how easy it is to look after when it is in use)

pseudocode

code that designs algorithms in clear, human-readable formats

record and fields (difference)

record: basic structure for collection of related elements consists of fields (can be or not be the same data type, related elements)

fields: fixed, has its own data type, single data item, each has their own name and data type

naming conventions

camel case, Hungarian notation

camel case

first word lowercase, following words capitalised. ‘tableName’, ‘helloWorld’, ‘nameHungarianNotation’

Hungarian notation

Three letter abbreviation. ‘qry’, ‘tbl’, ‘lbl’, ‘btn’

problem solving methodology

analysis
- solution requirements
- solution constraints
- solution scope

design
- solution design
- evaluation criteria

development
- manipulation
- validation
- testing
- documentation

evaluation
- strategy
- report

difference between valid and accurate

valid data is reasonable, but accurate data is true

unsuccessful data visualisation

- too complex
- no explanation
- too much data
- too small

processing features of a programming language

control structures, functions, classes, methods

types of control structures

sequences, conditional statements (selections), iteration (loop)

sequence

set of instructions executed line by line, every line is executed in the order that it is written

conditional statements (define and type)

selections, allow the application to make decisions during runtime dependent on given conditions. conditional statements are BOOLEAN, run based on the result of a condition being evaluated as either true or false

alternative execution (IF, ELSE), conditionals with more than one logical expression, CHAINED CONDITIONALS, NESTED CONDITIONALS, switch/ case

iteration (define and types)

loops, repeat sections of a code until a condition is met
while loops, do/while loops, for loops, repeat/until loops

WHILE loops

a section of code that is run when, and for as long as, a condition is met.

useful for when a programmer does not know when the condition will be met or if it will execute at all

the condition within the loop must meet at lease one for the code within it to be executed, if it is always true, it will create an infinite loop.

FOR loops

a section of code that is run a predetermined number of times, will only execute the code inside them if the condition is still being met, if it does not terminate, there is a logical error

can be used to perform an action on every element of an array, or to perform basic search through a set of elements in a data structure.

need 3 pieces of information
1. starting point (counter)
2. the end condition
2. an increment that increases/ decreases the starting point (counter) so that it approaches the end condition

functions

a block of code that is intended to be used repeatedly within an application
provide a RETURN VALUE as a result often assigned to variables, used in an equation

internal documentation

improves code readability, allows others to understand + interpret code. Comments and naming conventions are the most common forms of this.

internal documentation conventions

- header comments: name of file, program description, name, date
- documentation of classes and methods: behaviour, how they are used, any expected inputs and outputs of respective data types
- function and subroutine comments: describe purpose + input and output
- single line/ multi line comments: brief summaries of portions of code or explaining complex algorithms within the code themselves
- info on updates made to the program

primary source + examples

first hand account of information and original data

eg. surveys, interviews, diaries, letters, journals, autobiographies, art, maps photo, film, music

secondary source

summary or collection of existing data, full citation of original data

ex. text book, article reviews, articles about people and events

data collection methods

survey, focus group, interview, observation,

survey

quick way to gather a lot of data, can gather information on what people think is true, need to be carefully designed so participant's responses can provide suitable data

APP 6

information only being held for its primary purpose; entities can only use or disclose personal information for a purpose for which it was collected (known as ‘primary purpose’).

APP 11

security measures used to protect personal informationl; entities must take reasonable steps to protect personal information it holds from misuse, interference and loss, as well as unauthorised access, modification or disclosure.

text

letters, can be further specified as strings or characters (AOS1, data analytics)

numeric (AOS 1)

integers, floating points, date/time (AOS 1)

Database

A collection of logically related data that makes it easier for users to retrieve information quickly.

Flat File Database

A database with a single table, such as an Excel workbook, which can create redundant data.

Relational Database

A type of database that stores data in multiple tables and allows relationships between them. Usually through primary and foreign keys.

Primary Key (PK)

A unique identifier for a record in a table, used to establish relationships between tables.

Foreign Key (FK)

A primary key used in another table, to link the tables together.

One-to-One Relationship

A type of relationship where one record in a table is associated with one and only one record in another table.

One-to-Many Relationship

A type of relationship where one record in a table can be associated with multiple records in another table.

Many-to-Many Relationship

A type of relationship where records in one table can relate to multiple records in another table and vice versa.

This is…

An entity relationship diagram, used to plan the relationships between tables.

Data Visualisation

Graphic representation of data using software tools.

Exploratory Data Analysis

Uncover patterns and anomalies in datasets.

Infographics

Graphical representation of complex data for clarity.

Dynamic Data Visualisations

Visuals that change appearance based on user interaction.

Static Infographics

Single poster format with graphics and text.

Series Infographics

Related information presented in a segmented manner.

Visual Hierarchy

Arrangement guiding viewer through content importance.

Clarity of Message

Clear communication of data insights and findings.

Formats in Visualisation

Choices regarding visual appearance of data.

Use of Colour

Enhances readability and emphasizes important features.

Font Attributes

Typeface and style choices in visualisation. E.g. ‘serif’ and ‘sans serif’

Images and Icons

Visual elements enhancing understanding of data.

Serif (+ example)

Has ‘serifs’ on the ends of letters, like Times New Roman.

Sans Serif (+ example)

Does not have ‘serifs’ on the ends of letters, like Arial or Helvetica.

Benefit of Serif fonts

Good for large paragraphs, enhances readability, and is best for printed text.