data exploration

Question 1

data does not

Answer 1

• speak for itself
• it can be biased and is not objective (based on how selected)
• the people behind it interprets the data

Question 2

answers/results depend on..

Answer 2

question to solve and perspective

Question 3

types of data sets

Answer 3

• cross-sectional
• time-series
• panel

Question 4

cross-sectional

Answer 4

• many subjects/variables, one point in time
• eg sales, expenses, profit

Question 5

time-series

Answer 5

• one subject/variable, many points in time
• eg sales over time

Question 6

panel

Answer 6

• many subjects/variables, many points in time
• eg sales, expenses, profit over time

Question 7

dimensions of data quality

Answer 7

• completeness
• consistency
• conformity
• accuracy
• integrity
• timeliness

Question 8

completeness

Answer 8

comprehensive and meets expectations

Question 9

consistency

Answer 9

across all systems/sourced from different places reflects the same information

Question 10

conformity

Answer 10

follows set of standard data definitions like data type, size and format

Question 11

accuracy

Answer 11

correctly reflects the real world object OR an event being described

Question 12

integrity

Answer 12

all in a database can be traced and connected to other data

Question 13

timeliness

Answer 13

information is available when it is expected and needed

Question 14

first two steps of data cleansing/processing

Answer 14

• sourcing raw data
• technically correct data

Question 15

sourcing raw data

Answer 15

What do we want and need to achieve?
What data will support this outcome?
How can we source it and ensure it is of a high quality?

Question 16

technically correct data

Answer 16

• when can be directly recognised as belonging to a certain variable
• is stored in a data type that represents the value domain of the real-world variable

Question 17

data issues

Answer 17

• formatting/data type
• missing values
• outliers

Question 18

formatting/data type

Answer 18

• sex; Male, M, Boy
• month; January, 1-Jan, 1

Question 19

missing values - listwise deletion

Answer 19

remove records with missing values in any variable

Question 20

missing values - mode/median/mean imputation

Answer 20

• mean for continuous variables
• median for skewed continuous variables
• mode for categorical variables

Question 21

missing values - model imputation

Answer 21

• interpolate/extrapolate
• use regression model to predict missing value

Question 22

outliers - drop outlier record

Answer 22

completely remove record to avoid severe skewness

Question 23

outliers - winsorisation

Answer 23

• cap your outliers data
• limit extreme values in statistical data to reduce effect of possibly spurious (false) outliers

Question 24

outliers - imputation

Answer 24

• assign a new value
• mean or regression

Question 25

data privacy

Answer 25

claim of individuals, groups, and institutions to determine for themselves, when, how, and to what extent information about them is communicated to others

Question 26

data privacy principles

Answer 26

• notice
• choice and consent
• use and retention
• access
• protection
• enforcement and redress

Question 27

notice

Answer 27

inform users about privacy policy/protection procedures

Question 28

choice and consent

Answer 28

consent from individuals about collection, use, disclosure, and retention of information

Question 29

use and retention

Answer 29

data is retained/protected according to law or business practices required

Question 30

access

Answer 30

provide access to individuals to review, update, and modify data about personal information

Question 31

protection

Answer 31

data is used only for purpose stated

Question 32

enforcement and redress

Answer 32

provide channels for individuals to report, provide feedback, or complain

Question 33

ethics of data security

Answer 33

• managing quality personnel to address ethical issues
• perceived potential conflict of interest also exists relative to ethical behaviours and technical knowledge

Question 34

Australian Security Principles protects against

Answer 34

• misuse
• interference
• loss
• unauthorised access, modification, disclosure