Module 6 Data Science

Question 1

What is the fundamental distinction between different types of data?

Answer 1

The distinction between quantitative and qualitative data is crucial for effective data science.

Question 2

Define quantitative data.

Answer 2

Numerical data that can be measured and compared using mathematical methods.

Question 3

Give examples of quantitative data.

Answer 3

• Age
• Height
• Weight
• Salary

Question 4

Define qualitative data.

Answer 4

Descriptive data that cannot be measured using mathematical methods.

Question 5

Give examples of qualitative data.

Answer 5

• Opinions
• Emotions
• Perceptions

Question 6

What is the primary use of quantitative data?

Answer 6

To draw conclusions and make predictions based on numerical analysis.

Question 7

What is the primary use of qualitative data?

Answer 7

To understand experiences and perspectives.

Question 8

What are the two categories of data collection methods?

Answer 8

• Active
• Passive

Question 9

What is Manual Active data collection?

Answer 9

Involves actively engaging with participants to collect data, such as surveys and interviews.

Question 10

What is Manual Passive data collection?

Answer 10

Involves collecting data without actively engaging with participants, such as observation.

Question 11

What is Computerised Active data collection?

Answer 11

Using technology to actively collect data from participants or customers.

Question 12

What is Computerised Passive data collection?

Answer 12

Collecting data automatically through technology, without engaging with participants.

Question 13

Define primary data.

Answer 13

Data collected by the researcher themselves.

Question 14

Give examples of primary data.

Answer 14

• Diaries
• Original documents
• Government documents

Question 15

Define secondary data.

Answer 15

Data obtained from existing sources.

Question 16

Give examples of secondary data.

Answer 16

• Journal articles
• Textbooks
• Encyclopedia websites

Question 17

What are the four criteria for evaluating data quality?

Answer 17

• Relevance
• Accuracy
• Validity
• Reliability

Question 18

What does relevance refer to in data quality?

Answer 18

The extent to which the data is directly related to the research question.

Question 19

What does accuracy refer to in data quality?

Answer 19

The degree to which the data reflects the true values of the variables being measured.

Question 20

What does validity refer to in data quality?

Answer 20

The degree to which the data accurately measures what it is supposed to measure.

Question 21

What does reliability refer to in data quality?

Answer 21

The consistency of the data over time and across different sources.

Question 22

What are errors in data collection?

Answer 22

Errors can arise in collection, entry, or processing.

Question 23

What is uncertainty in data?

Answer 23

A lack of precision or ambiguity.

Question 24

What are limitations in data?

Answer 24

Inherent constraints of the data, such as sample size or coverage.

Question 25

Define informatics.

Answer 25

The study of the processing and management of information.

Question 26

What is data visualisation?

Answer 26

The interpretation and presentation of data using tools like graphs and infographics.

Question 27

What is structured data?

Answer 27

Data organized into a specific format, such as a table or spreadsheet.

Question 28

Give examples of structured data.

Answer 28

* Numerical data * Categorical data * Dates and times

Question 29

What is unstructured data?

Answer 29

Data that does not have a specific format or structure.

Question 30

Give examples of unstructured data.

Answer 30

* Text * Images * Audio files

Question 31

What is alternative data?

Answer 31

Data types like likes, emoticons, and memes used for feedback.

Question 32

What is blockchain technology?

Answer 32

A distributed ledger that provides secure and transparent data management.

Question 33

What is data management in blockchain?

Answer 33

Each block contains verified transactions that are nearly impossible to alter.

Question 34

What is data verification in blockchain?

Answer 34

Ensured by cryptographic algorithms and consensus among network participants.

Question 35

What are smart contracts?

Answer 35

Self-executing agreements that automate data verification and management.

Question 36

What are the risks of using autofill features?

Answer 36

They pose risks to data privacy if used on public devices.

Question 37

What is big data?

Answer 37

Large and complex datasets that can't be processed by traditional systems.

Question 38

What are the three Vs of big data?

Answer 38

* Volume * Variety * Velocity

Question 39

What is data warehousing?

Answer 39

Centralising and storing large amounts of data in a single repository.

Question 40

What is data mining?

Answer 40

Extracting and analysing large amounts of data to uncover patterns and insights.

Question 41

What does the impact of data scale refer to?

Answer 41

How size and complexity of a dataset affect analysis and interpretation.

Question 42

What are digital footprints?

Answer 42

The accumulation of large-scale datasets raises concerns about sensitive personal information.

Question 43

What are the key ethical considerations in data use?

Answer 43

* Privacy * Consent * Data security * Transparency * Responsibility * Data accuracy * Confidentiality

Question 44

What is the primary legislation regulating data collection in Australia?

Answer 44

The Privacy Act 1988.

Question 45

What are the Australian Privacy Principles (APPs)?

Answer 45

13 principles governing various aspects of handling personal information.

Question 46

What does the Notifiable Data Breaches (NDB) Scheme require?

Answer 46

Notification of data breaches likely to cause serious harm to individuals.

Question 47

What does data sovereignty refer to?

Answer 47

The right of Aboriginal and Torres Strait Islander Peoples to control their data.

Question 48

What is the importance of data literacy?

Answer 48

Essential for interpreting, analysing, creating, and communicating data.

Question 49

What are data swamps?

Answer 49

Disorganised data repositories that can lead to misleading conclusions.

Question 50

What is the role of educating users in data communication?

Answer 50

Promoting ethical and effective data communication and understanding privacy concerns.