VCE Data Analytics units 3 and 4

KK = Key Knowledge. All KK are examinable (as are all terms in the glossary that are relevant to this course)
If a KK uses the word "including", all of the following items are examinable.
If a KK uses the words "for example" or "such as", the following items are for clarity, and are not directly examinable.
Any bullet points shown below have been inserted by me for clarity, and are not in the original VCAA study design. (Which is available locally here.)
Hover your mouse of a red asterisk to see the official VCAA glossary definition of a term.

links to a (big) slideshow if the icon has a label beside it

links to a webpage on this site.

Check out my the postmortem of VCAA's 2020 sample DA exam with lots of exam tips.

Techniques for efficient and effective data collection, including methods to collect

• census [data],
• Geographic Information Systems (GIS) data,
• sensor [data],
• social media [data] and
• weather [data]

[Note: I have edited the KK punctuation to clarify its ambiguous punctuation. Let me know if you read it differently.]

General Data collection techniques

Geographic Information Systems

Factors influencing the integrity of data, including

• accuracy,
• authenticity,
• correctness,
• reasonableness,
• relevance and
• timeliness

Data integrity

Timely?

Dictionary: 1. Done or happening at the appropriate or proper time. 2. Before a time limit expires

Methods for referencing primary and secondary sources, including

• American Psychological Association (APA) referencing system

Primary and secondary data

APA referencing system summary

Characteristics of Data types

Data types

Methods for documenting a problem, need or opportunity

Problem statements

Software solution requirements (SRS)

Naming conventions

Database structure naming

File Naming

A methodology for creating a database structure:

• identifying entities,
• defining tables and fields to represent entities;
• defining relationships by identifying primary key fields and foreign key fields;
• defining Data types and field sizes,
• normalisation to third normal form

Design tools for representing databases, spreadsheets and Data visualisations, including

• data dictionaries,
• tables,
• charts,
• input forms,
• queries and
• reports

Database design tools

Entity relationship diagram

Data dictionary

Mockups

Design Principles

Design principles that influence the functionality and appearance of

• databases,
• spreadsheets and
• Data visualisations

Design Principles

Design Factors

Design Elements

Software functions, techniques and procedures to efficiently and effectively

• validate,
• manipulate and
• cleanse data including files, and

applying Formats and Conventions

Data Validation

Formats and conventions (mainly web)

Data visualisations

Formats and conventions (mainly web)

Methods and techniques for Testing

• databases,
• spreadsheets and
• Data visualisations

Testing

User Acceptance Testing

Data visualisations

The Importance of data to organisation

Hardware & software

Hardware

Data Security

Primary and secondary data sources and methods of collecting data, including

• interviews,
• observation,
• querying of data stored in large repositories and
• surveys

Primary and secondary data

Data Acquisition

Querying of data repositories

Refers to searching datasets to extract useful information from the masses of data.
May query primary data (original data collected personally by the researchers) or secondary data (data collected by other people, e.g. government departments like CSIRO)
Many providers of secondary data provide an API (application programming interface) to allow outsiders controlled access to their datasets.
E.g. the Australian Bureau of Meteorology lets developers create apps that extract weather forecast data from its databases.

Queries may use SQL or QBE

SQL = Structured Query Language. A standardised format for selecting data from databases. E.g.

SELECT * FROM Book WHERE price > 100.00 ORDER BY title;

QBE = Query By Example. The user types what they want to find in the field they want to find it.

Techniques for

• searching,
• browsing and
• downloading

data sets

Quantitative data - usually numeric. It is fact-based, measures of values or counts - e.g. this printer produces 15 pages per minute

e.g.

From the ABS website, 2022-02-28

Quantitative data can be verified for accuracy.

Qualitative data - descriptive, judgemental, non-stastical, unstructured, opinion-based data expressed in words rather than number - e.g. "I think this page layout is attractive." It can be in the form of text, video, photos, audio recordings, art, music, body language, facial expressions... anything that can't be measured and given a numeric value.

Arbitrary categories are also qualitative, e.g. fashionable vs unfashionable clothes, social class, conservative vs liberal politician.

Whenever possible and appropriate, prefer quantitative data. It's hard to argue with.

Use qualitative data if you have to, especially regarding human feelings that cannot be measured. e.g. "That logo is cute. The other one is yucky - urghh"

Example:

SEX = quantitative - must be male, female, intersex - according to Australian law. It can be scientifically established.

vs

GENDER = qualitative -  it depends on an individual's emotions or judgement, and cannot be externally proved or disproved.

The Australian Bureau of Statistics website defines qualitative data as "measures of 'types' and may be represented by a name, symbol, or a number code. Qualitative data are data about categorical variables (e.g. what type)."

(But what would they know?)

Note - neither quantitative nor qualitative data is always better or worse than the other. Their value depends on appropriateness to a particular use. They are often used together to fully describe a situation.

e.g. when researching students, it is found that 32% show elevated stress levels using data about their blood pressure, blood cortisol levels etc. That is valuable quantitative data, but it does not explain the reasons for the stress. That qualitative data - which can vary from person to person - can only be gathered through other means such as observation, interviews, surveys etc.

e.g. When buying a printer, quantitative speed measurements are valuable. When judging the layout of a book cover, qualitative data about people's reactions and emotions is more important.

Quiz: which data types are qualitative and which are quantitative?

• He had 15 years of formal education
• He is well-educated.
• He is smart.
• He went to a good school.
• He was a popular student.
• Of his cohort, he got the top exam result (96%).
• He is tall.
• He weighs 80kg.

Answers: highlight the next blank line to see the qualitative attributes

HIGHLIGHT FROM HERE

• He is well-educated.
• He is smart.
• He went to a good school.
• He was a popular student.
• He weighs 80kg.

Quiz 2 (thanks to https://www.g2.com/articles/qualitative-vs-quantitative-data for the idea)

A bookcase is described as follows. Which descriptors are qualitative and which are quantitative?

• Costs $1500
• Is made of oak
• Has golden knobs
• Is 100cm long
• Was built in Italy
• Has a smooth finish
• Weighs 50kg
• Has 3 shelves
• Is deep brown

HIGHLIGHT FROM HERE to see the qualitative descriptors

• Is made of oak
• Has golden knobs
• Was built in Italy
• Has a smooth finish
• Is deep brown

TO HERE

Data Types

Data Structures - records & files in databases

APA referencing

Criteria to check the integrity of data, including

• accuracy,
• authenticity,
• correctness,
• reasonableness,
• relevance and
• timeliness

Data integrity

Consistency (not examinable, but important)

For data to have integrity, it should be consistent. Inconsistency occurs within a single data source or between data sources when conflicting versions of data appear in different places. Data inconsistency is unwelcome because it means that data have become unreliable. If the ‘true’ value cannot be determined, the entire data source loses integrity and becomes tainted. Any information drawn from it becomes untrustworthy.

Examples:

• a date of birth in one database table does not match an age calculated in another table
• in one survey question people say they’ve never used drugs, but later answers say they used marijuana more than zero times
• data stored in one location (e.g. a local database) does not match corresponding data in another location (e.g. a linked website database).

Above: Excel tries to spot inconsistencies and warn the user

Common cures:

• normalise to third normal form (3NF) to ensure proper synchronisation of table data, and prevent duplicate data. Any data stored in more than one place immediately invites the risk of inconsistency. If employee addresses are stored in the STAFF database and also the CONTACTS database, they could easily become inconsistent if one table is updated and the other is not.
• enforce referential integrity in databases to ensure key fields cannot be deleted without also deleting or modifying related fields. If the ‘Sales’ department is deleted from the ‘Departments’ table, everyone working in ‘Sales’ need to be either deleted or moved to another department, otherwise their ‘Department’ data becomes meaningless.
• ask the same question again in different ways to check the consistency of answers in surveys and questionnaires. This is important for issues about which people tend to lie or bend the truth to avoid embarrassment or boost their egos. Question 7 may ask “Do you gamble?” and question 37 may ask “Last year, how often did you bet on a horse, buy a lottery ticket, or use poker machines?” People often forget they fibbed in an earlier question and accidentally tell the truth later!
• Use electronic checking to detect inconsistent answers. e.g. IF (NumberOfChildren = 0 and AgeOfOldestChild > 0) then Consistency Error!
• Use a single master-copy data source (‘Single Point of Truth’ or ‘SPOT’) instead of multiple data clones that are sluggishly synchronised. If all data was read from a single master copy rather than a copy that is only updated occasionally, the data is guaranteed to be up to date.
• use consistent data formatting and validation rules to enforce consistent data entry. If people can enter a birthdate in either of two places in a database, use the same data format in each location. Don’t expect “YYYY-MM-DD” on one form and “D MMM YY” on the other form.
• use an input mask to force a particular data entry format. (e.g. XXXX-XXXX-XXXX-XXXX for credit card numbers)
• use fuzzy logic to handle trivial differences in data e.g. accept ‘Robert’ if he’s also known as ‘Bob’; treat “St Kilda” the same as “Saint Kilda.”
• standardise data so they are in a consistent, predictable and comparable format
  • store family names in all capitals so - when doing comparisons and queries - “Smith” is not overlooked as a match for “smith” or “SMITH”
  • strip spaces from phone number data, and the beginnings and ends of all text data.
  • when dates are stored as text, they always use the same format

“YYYY-MM-DD” is the international ISO standard format for dates. e.g. currentyear-01-02 means 2 January currentyear. Can you suggest why is there an international standard for date formats?

• use a standardised time setting, such as Greenwich Mean Time, Australian Eastern Summer Time etc.
• use a consistent year numbering scheme:
  • Common Era (e.g. ‘the moon landing in 1969’)
  • Islamic (‘the moon landing in 1347’)
  • Jewish (‘the moon landing in 5729’)
• all currency values are stored in the same units (e.g. $AU, $US, £UK, €)

Features of a research question, including a statement identifying the research question as an information problem

Yes! It's a webpage, not a slideshow.

Also, you need to see this, especially if you're at Hume Grammar.... I've been watching you guys.

Problem Statements

Functional and non-functional requirements, including

• data to support the research question,
• constraints and
• scope

Types and purposes of

• Infographics and
• dynamic Data visualisations

Infographics

Design principles that influence

• the appearance of Infographics and
• the functionality and appearance of dynamic Data visualisations

Design Principles

Infographics

Design tools for representing the appearance and functionality of Infographics and dynamic Data visualisations, including data manipulation and Validation, where appropriate

Design-Elements-Web

design-factors

design-mockups

Design-Tools-Website

Design-Tools

Design Principles

Infographics

Design Ideas

Infographics

Features of Project management using Gantt charts, including

• the identification and sequencing of tasks,
• time allocation,
• dependencies,
• milestones and
• the critical path

Gantt charts

Gantt and PERT charts (PERT is not examinable in Data Analytics, but really nice to know)

Project management

Key Legal requirements for the storage and communication of data and information, including

• human rights requirements,
• intellectual property and
• privacy.

Human Rights and Spam legislation

Privacy Act

Copyright Act

Procedures and techniques for handling and Managing files, including

• archiving,
• backing up,
• disposing of files and
• security

File management

Backups

Infographics

Characteristics of information for educating targeted audiences, including

• age appropriateness,
• commonality of language,
• culture inclusiveness and
• gender

Inclusiveness

Characteristics of efficient and effective

• Infographics and
• dynamic Data visualisations

Infographics

Functions, techniques and procedures for efficiently and effectively manipulating data using software tools

Welp! This KK is a three-year course in itself. It includes nearly everything you consider "IT"

So, let's try to break this down into manageable chunks:

Manipulating data = basically what any software does. If software doesn't manipulate data, it's not software.

Data manipulation = using data to create new data (e.g. create a graph from a table of numbers)

Data modification = changing data to a new value and replacing the original data (e.g. replacing Excel cell E3 with a new value)

How can data be manipulated?

Sorted, e.g. by size, name, age,

Categorised - e.g. by media type, musical genre

Summarised - statistically: to find averages, trends, maximum & minimum values

Converted - to different forms, e.g. from numeric data to a visual graph, from digital data to sound or video. Converting a spreadsheet into a database. Converting a slideshop into a webpage. Reformatting data.

Compressed - e.g. with JPG / GIF / TIFF (images), MP3 / FLAC (audio), RAR / ZIP (files) ,

Encrypted - PGP, WEP / WPA (wifi), HTTPS, SSL / TLS (web pages)

Deleted, merged, inserted

Common data manipulation software tools

Spreadsheet - a flat-file database (though VLOOKUP gives some valuable relational ability)

Relational database - with two or more related tables

Specialist tools - e.g. video editors, web browsers, file managers, text editors. They are very limited in the data they can accept and the manipulation they can do, but they are vital for specific tasks.

SPREADSHEETS

If you only spent an hour in a spreadsheet class, you would have been told about:

- the structure of spreadsheets: sheets, rows, columns, cells, formulas, values

- basic functions - sum, average, maximum/minimum

DATABASES

If you only spent three hours in a database class, you would have been told about:

- the structure of databases: tables, records, fields

- relationships between tables

- queries, reports

- basic functions - sum, average, maximum/minimum

More to come, eventually

2022-04-25 @ 1:20 PM

Techniques for creating

• Infographics and
• dynamic Data visualisations

Infographics

Data Validation

Testing

Test data

Trace Tables, Desk Checking

Techniques for recording the progress of projects, including

• adjustments to tasks and timeframes,
• annotations and
• logs

Strategies for

• evaluating the Effectiveness of Infographics and dynamic Data visualisations Solutions and
• assessing project plans.

Infographics

Networks - wired, wireless, mobile

Network hardware

Threats to data and information

Physical and software security controls for

• preventing unauthorised access to data and information and for
• minimising the loss of data accessed by authorised and unauthorised users

Data security

Penetration Testing, White Hat / Ethical hacking

Cloud computing

Characteristics of data that has integrity, including

• accuracy,
• authenticity,
• correctness,
• reasonableness,
• relevance and
• timeliness

Data integrity

Importance of data and information

Data security

Data integrity

Key legislation that affects how organisations control the collection, storage, communication and disposal of their data and information:

• the Health Records Act 2001,
• the Privacy Act 1988 and
• the Privacy and Data Protection Act 2014

Privacy Act

Ethical dilemmas

Ethical dilemmas

Reasons to prepare for disaster and the scope of disaster recovery plans, including

• backing up,
• evacuation,
• restoration and
• test plans

Data Disaster Recovery Plans

Data Backups

Data Security

Threats to Data Security

A summary from the Security-Threats PPT:

• Loss of valuable data that can't be replaced at all, or only with huge effort and cost
• Competitors finding out your secrets
• Damage to or loss of expensive equipment
• Financial loss through misuse of credit cards or bank accounts
• Unwitting participation in illegal actions such as spamming or DDOS attacks
• Damage to of reputation through negligently letting customer information go public
• Penalties by the tax office for not having proper GST or tax records
• Prosecution under the Privacy Act if sensitive information is not properly protected.
• Loss of income when unable to do business due to system failure
• Total failure of the organisation after catastrophic data loss
• Organisational death.

Criteria for evaluating the effectiveness of data and information security strategies.

Point 1 - Effectiveness relates to how well the job is done, regardless of time, money or effort is expended. Efficiency refers to speed, cost and labour. Everything else is effectiveness.

Point 2 - Criteria are the relevant factors used to measure the value of something. e.g. the criteria of a cat are: fluffiness, purriness, jumpiness. Not how well they guard the house, or lead the blind..

Point 3 - Data: the raw, unprocessed numbers gained from a sensor, survey, census, or other data-gathering system. Data have potential data, but are unorganised, bulky, and their meaning is unclear,

Point 4 - Information: is processed from data to create meaningful and understandable knowledge. e.g. A national census may contain the incomes of 15 million people (which is data) but only when those 15 million incomes are added up or averaged will useful information be derived from them.

e.g.:

• DATA = crude oil. Unprocessed, may be useful.
• INFORMATION = petrol. Processed. Useful.

So - back to the Key Knowledge.

How does one evaluate how well different methods protect the security of data and information?

We're not worried about what the different methods are. They might involve armed guards, virus scanners, ceiling-mounted laser cannons. It matters not.

What we're worried about is what rules we use to judge the quality of the different methods.

Now it gets easier.

What criteria would you use to judge to evaluate the effectiveness of a CAR? speed, reliability, running costs, safety, seating capacity, cup holders.

What criteria would you use to judge to evaluate the effectiveness of a DOG? woofiness, friendliness, size. OK. I don't understand dogs.

In case you're getting bored, here's a picture of a dog.

You use very different criteria when judging cats and dogs.

And you use different vriteria for evaluating the effectiveness of data and information security strategies.

CRITERIA:

- Speed of response to the threat. Nope! That is an efficiency criterion, not effectiveness as the KK asks. Same applies to the cost, or amount of labour required. Be careful!

- Accuracy. How many false negatives (real attacks that were not detected) or false alarms (harmless events that were wrongly judged as being harmful) were recorded?

- Usefulness. If a strategy gave useful information about the source of the threat, the type or nature of the threat, how to counteract the threat etc, it would be a jolly good strategy. It would give quality information.

- Recoverability - yes, it's an ugly word (and not examinable) but a quality data security strategy would offer a quick and easy way to get back lost or damaged data quickly and accurately. You could also call it resilence - the ability to get back to normal after a setback.

- Reliability- can the strategy be trusted? Is it dodgy?

- Comprehensiveness- Does the strategy detect all relevant threats? A security scanner that completely ignores trojan horses or rootkits, for example, may leave a system open to exploitation..

- Basically - ANY means of judgement that does NOT involve speed, cost, or labour.

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This page was created on 2022-01-17
Last modified on Saturday 29 October, 2022 15:23