ETB2111 Business data analytics
Faculty of Business and Economics
ETB2111 Business data analytics is a level 2, 6-credit-point, undergraduate unit from the Faculty of Business and Economics, offered in 2024 in Semester 1 at Peninsula. It needs ETW1001, ETX1100, ETB1100, FIT1006, ETC1000, SCI1020, ETF1100 or STA1010 and unlocks 2 units, leading on to 6 units in all.
- Credit points
- 6
- Offered in 2024
- Semester 1
- Peninsula
- Assessment
- Exam 40%
- and 1 other task
- Workload
- 144 hours
- per semester
This is the 2024 handbook entry. See the 2025 entry.
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Requisites
Before ETB2111
Prohibitions
You can't enrol if you have passed any of these.
Prerequisites
Pass these before you enrol.
- ETW1001Introduction to statistical analysisNo reviews yet
- ETX1100Business statisticsNo reviews yet
- ETB1100Business statisticsNo reviews yet
- FIT1006Business information analysisNo reviews yet
- ETC1000Business and economic statisticsNo reviews yet
- SCI1020Introduction to statistical reasoningNo reviews yet
- ETF1100Business statisticsNo reviews yet
- STA1010Statistical methods for scienceNo reviews yet
After ETB2111
2 units list ETB2111 as a prerequisite or corequisite.
Enrolment rules
To be successful in this unit, background knowledge and application of maths is required at the equivalent of VCE Year 12 level. You may have satisfied this by completing relevant prerequisite unit/s, or you have covered relevant topics in your final years of secondary study. You should self-assess your maths competency prior to enrolling in this unit.
Overview
Data is collected with an intended purpose for analysis and can provide the “why” behind patterns identified through data analytics. This unit further develops statistical concepts covered in ETB1100 Business Statistics and centres on the analysis of data that is readily accessible in businesses across all sectors.
You will learn tools relevant across the whole process of data analysis from appropriate sample size calculations and collection, to mining data for high level business insights, through to deep dive analytics where inferences are drawn and tested for significance, and relevant predictive models are identified, applied and validated.
Specifically, this unit covers data visualization for numeric and categoric data; sampling theory and design; statistical inference as a means to identifying significant findings around means and proportions; using multiple regression to analyses relationships amongst variables; using classification and regression trees to make predictions.
Emphasis throughout is on translating results into readily digestible, actionable insights in context of the business needs at hand. Widely available software such as Excel will be used, with an introduction to R and RStudio which focuses on application rather than coding.
Offerings in 2024
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Peninsula | Flexible |
Assessment
- Within semester assessment60%
- Examination40%
Learning outcomes
When you finish this unit, you should be able to:
- 1
develop effective visualisations to uncover and understand relationships within data sets
- 2
learn how to identify and collect a statistically valid, representative sample of data that satisfies its intended purpose
- 3
demonstrate an understanding of the importance of statistical inference in business, specifically, be able to determine and interpret significant differences required for decision making
- 4
demonstrate the ability to conduct and understand regression analyses and classification and regression trees
- 5
effectively interpret and communicate the results of your investigations to the appropriate stakeholders in order to support data driven decision making in business.
Workload and teaching
- Lectures24 hours
- Tutorials24 hours
- Teaching approachProblem-based learning
- Teaching approachActive learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.
This unit includes problem-based learning approaches, where you engage in research, integrate theory and practice and apply knowledge and skills to develop viable solutions in response to a problem or set of problems.
This unit engages you in actively applying your knowledge, skills and attributes in interactive, collaborative and reflective activities.
Learning resources
Technology resources
Laptop required. Loan laptops are available https://msa.monash.edu/services/msa-library/borrowing/gadgets/
Microsoft Excel is required for this unit.
Contacts
- Chief Examiners
- Ms Geraldine Roberts
Common questions
What are the prerequisites for ETB2111?
You need ETW1001, ETX1100, ETB1100, FIT1006, ETC1000, SCI1020, ETF1100 or STA1010 before you enrol. Enrolment rules also apply.
What can I take after ETB2111?
ETB2111 is a prerequisite or corequisite for 2 units, including ETF3231 and ETF5231. Those lead on to 6 units in all.
When is ETB2111 offered?
In 2024, ETB2111 runs in Semester 1 at Peninsula.
How much work is ETB2111?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ETB2111 have an exam?
Yes. The exam is worth 40% of the final mark, alongside 1 other task.