Useful links
Unit of study_
QBUS2810: Inferential Data Analytics
2027 unit information
Organisations collect vast amounts of information on markets, customers, financial performance and operations, creating opportunities to extract meaningful insights from complex datasets. The ability to draw reliable conclusions from such data is essential for today’s business professionals. This unit introduces the statistical foundations of inferential data analytics: the process of using data to identify patterns, quantify uncertainty, and make evidence-based conclusions about real-world business phenomena. Students learn how statistical models can reveal relationships within data and how those relationships can be used to inform decisions, evaluate risks, and test business hypotheses. Practical applications are drawn from finance, marketing, accounting, and operations. Using modern software, students learn to build, estimate and interpret statistical models, assess the strength of empirical evidence, and translate quantitative findings into actionable business recommendations.
Unit details and rules
Managing faculty or University school:
Business (Business School)
| Study level | Undergraduate |
|---|---|
| Academic unit | Business Analytics |
| Credit points | 6 |
|
Prerequisites:
?
|
(BUSS1104 or BUSS1020 or ECMT1010 or ENVX1001 or ENVX1002 or DATA1001 or DATA1901) and (QBUS1040 or MATH1061 or MATH1062) |
|---|---|
|
Corequisites:
?
|
None |
| Prohibitions:
?
|
ECMT2110 |
| Assumed knowledge:
?
|
This unit relies on mathematical knowledge at the level of the Maths in Business program, including calculus and matrix algebra. Students who do not meet this requirement are strongly encouraged to acquire the needed mathematical skills prior to enrolling in this unit. |
At the completion of this unit, you should be able to:
- LO1. develop an understanding of the principles of statistical modelling of business-related variables
- LO2. develop a deeper understanding of statistically measuring and analysing relationships between business variables via a range of quantitative models and methods
- LO3. develop proficiency in using relationships between variables and analytic methods to inform and assist business decision making
- LO4. develop introductory skills in how to manage data and in how to extract objective quantitative information from them
- LO5. develop proficiency in a software package, e.g. Python, for analysing and assessing relationships between business variables, and in dealing with large data sets
- LO6. communicate empirical findings using adequate statistical reporting methods and appropriate technical language, as well as layman’s terms.
Unit availability
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
|
Semester 1 2026
|
Normal day | Camperdown/Darlington, Sydney | |
|
Semester 2 2026
|
Normal day | Camperdown/Darlington, Sydney |
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
|
Semester 1 2027
|
Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
|
|
Semester 2 2027
|
Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
|
Find your current year census dates
Modes of attendance (MoA)
This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.
Disclaimer
Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.
To help you understand common terms that we use at the University, we offer an online glossary.