types and purposes of qualitative and quantitative data
common data sources such as interviews, surveys, sensor data, census data and time-series data
data generated by artificial intelligence systems
characteristics of relevant data types:
text
numeric
Boolean
Data Quality, Privacy and Ethics
factors affecting data quality and information quality:
accuracy
bias
integrity
relevance
reliability
characteristics of data and information:
size
structure
accessibility
clarity
context
techniques for applying the Australian Privacy Principles when using, managing and communicating data
ethical issues such as lack of transparency, inaccurate or incomplete data, ownership and control of data, misuse of personal data, and repurposing data through AI systems
APA referencing for primary and secondary data sources
Requirements and Design
characteristics of functional and non-functional requirements, constraints and scope
design tools for representing the functionality and appearance of databases, spreadsheets and data visualisations:
IPO charts
annotated diagrams
mock-ups
query designs
Database and Spreadsheet Structures
structural characteristics of relational databases:
tables
queries
relationships using primary and foreign keys
structural characteristics of spreadsheets:
rows and columns
cells
Data Manipulation and Analysis
software functions and techniques for efficiently manipulating, validating and testing data
use of SQL to generate queries
spreadsheet functions to calculate descriptive statistics such as average, median, count and standard deviation
Data Visualisations
purposes of data visualisations for informing, educating, entertaining and persuading audiences
types of data visualisations such as infographics, poster series, dashboards, charts, graphs, maps and network diagrams
components of data visualisations:
text and graphics
tables
charts and graphs
formats and conventions suitable for databases, spreadsheets and data visualisations, including naming conventions, colours, fonts, images and icons