CMCE10002 Foundations of Business Analytics
The Complete Exam Bible · S2 2026CMCE10002 Overview
- The University of Melbourne
- Semester 2 2026
- First-year commerce subject
- Taught in R and Posit Cloud
- 10 hours per week expected
Foundations of Business Analytics at the University of Melbourne
CMCE10002 Foundations of Business Analytics is a first-year subject in which students build the skills to read data and draw business decisions out of it.
- Assessed by in-class quizzes, a mid semester test, a group assignment and a two-hour hand-written final examination
- The one condition to plan around the subject guide requires a pass in the final exam, so a strong semester mark does not cover a failed paper
- Hardest step predicting what a table looks like after grouping, reshaping or joining, without a computer in front of you
- How to prepare cover the output, predict it, then run the code; both timed assessments are sat without a console
How CMCE10002 is assessed
| Component | Weight | Format |
|---|---|---|
| In-class quizzes, held during workshops | 10% | Individual, weeks 4 to 12. Each week is worth one mark, half for attending your workshop and half for contributing, with randomised preparation spot checks. Nine quizzes run and the best seven count. |
| Mid Semester Test | 20% | Individual, week 6. One hour with no reading time, sat in person as a Canvas quiz under the Respondus LockDown browser, 25 questions mixing multiple choice and short answer, covering the first five weeks. |
| Group Assignment | 20% | Group, week 12. An analytics project on a business dataset, reported as an Analytics Team Memo, written for a peer or manager who will check the work, and a short Corporate Brief, written for an executive who has to decide something. |
| 2-hour end of semester examination · hurdle | 50% | Individual, during the exam period. Hand written, drawing on lectures, workshops and the assigned readings: 20 multiple-choice questions worth 40 per cent, alongside roughly four short-answer questions that carry subparts and the remaining 60 per cent. The subject guide states that a pass in this exam is required to complete the subject. |
The four weights published in the subject guide for Semester 2, 2026 sum to 100 per cent. The pass condition printed with the final examination row is the only one stated for any component; no other component is described as carrying one, and the subject site holds the current wording. Two official statements about the group differ: the subject guide describes groups of four while the week 6 lecture slides describe groups of three to four registered in the same class, so confirm the current rule before your group is finalised.
Assessment structure
Current CMCE10002 dates
| Date | Item | Control |
|---|---|---|
| Friday 4 September 2026, 17:30 to 18:30 AEST | Mid semester test | One hour with no reading time, sat in person as a Canvas quiz under the Respondus LockDown browser. |
| 5 pm each Friday, weeks 4 to 12 | Weekly quiz due | Nine weekly quizzes run in total and the best seven contribute to the final grade. |
| Monday of week 9 | Group assignment released | Groups are finalised by the Friday of week 9. |
| 23:59 on the Friday of week 12 | Group assignment due | Late submission without an approved extension or special consideration receives zero marks. |
| During the examination period | Final examination | The subject guide places the exam in the examination period; confirm your own sitting on the University timetable. |
Dates are as published in Confirm current dates and operational instructions on the live subject site.. Confirm exact deadlines and submission settings in the live LMS.
What CMCE10002 covers
Foundations of Business Analytics teaches analytics as a chain of decisions: a business question becomes an analytical plan, the plan is implemented in R, and the output is read back as something a manager can act on.
The chapters follow the twelve lecture weeks, from the three kinds of analytics question through visualisation, wrangling, reshaping and joining, to causal and predictive work and the two written products of the group assignment.
What Business Analytics Is: Three Kinds of Question
Lecture week 1. Descriptive, predictive and causal questions; reading the verb in a request; the evidence each type demands; the four layers from raw record to a narrative; bias, rights and transparency as constraints.02Reproducible Workflows in Posit Cloud and Quarto
Lecture week 2. Posit Cloud as the shared environment; console, script and Quarto document; loading packages; rendering from a clean session; reading the first error rather than the last.03Reading R Code: Objects, Tibbles and Subsetting
Lecture week 2 and workshop week 3. Object, vector, tibble, package and argument; the symbol set and the sentence each makes; row and column positions in subsetting; establishing the grain before computing.04Visualising Business Data with ggplot2
Lecture week 3. The retail pricing question the demonstration is built on; data, mapping, geometry and polish; why the canvas starts empty; mapping a column to colour against setting a constant; matching a geometry to the question.05Why Summary Statistics Alone Can Mislead
Lecture week 3. Datasets that share a mean and a spread while differing completely; what an average can and cannot establish; reading a table of group averages; separating a spurious pattern from a hidden spread.06Wrangling One Table with dplyr
Lecture week 4. Wrangling as transformation fit for one purpose; row, column and group operations; filtering before ranking; turning a dollar total into a comparable rate; selecting by name, pattern and type.07The Pipe and Grouped Business Summaries
Lecture week 4. The pipe against nested calls; grouping as a label that changes nothing visible; summarising as the step that changes the grain; carrying a count beside every average; releasing the grouping afterwards.08Tidy Data and Reshaping with Pivots
Lecture week 5. The three tidy rules; why tidy data lets code survive more data; lengthening when a variable sits in a column heading; widening when a variable name sits in a cell; predicting the reshaped row count.09Joining Tables on a Shared Key
Lecture week 5. The key as a statement about what a row represents; matched, left-only and right-only keys; what each of the four joins keeps; why order matters for two of them; matching grain before joining.10Variation in Cross Section, Time Series and Panel Data
Lecture week 6. Three readings of one table; what one observation is in each; grouping by the unit against grouping by the period; why a table with one column per period must be lengthened first.11Causal Analytics and Randomised Experiments
Lecture week 8. Spotting a causal question; potential outcomes and the one that is never observed; omitted variables; model-based adjustment against randomised design; comparability and stability.12Predictive Analytics, Stored Data and APIs
Lecture weeks 9 to 11. Forming a rule and evaluating it as separate jobs; holding back later rows; comparing against a trivial alternative; describing the slice taken from a store; keeping a live data request reproducible.13Writing the Analytics Memo and Corporate Brief
Group assignment. The memo and the brief as documents for two readers; leading with the finding in the brief; replacing adjectives with measurements; the so-what test; the group-work rules on documentation and contributions.It takes a technical, code-based approach, working through the fundamentals of structuring, transforming, visualising, analysing and interpreting data in R, and applies those skills across finance, accounting, marketing, economics and supply chain management.
No prior programming experience is expected.
Three dimensions of analytics
The subject organises the field into descriptive analytics, which establishes what happened; predictive analytics, which anticipates what happens next; and causal analytics, which uncovers cause-and-effect relationships.
Hands-on case studies apply these to real business problems, and the subject places the ethics of working with sensitive information inside the method rather than beside it: recognising and mitigating bias, balancing insight against individual rights, and communicating findings transparently.
How the subject is taught
Lectures run in weeks 1 to 12 and are built as live demonstrations rather than displays of finished code: a business question is translated into a plan, the plan is implemented, and results are checked for plausibility.
Workshops begin in week 2 and run as collaborative coding sessions in which groups work through prepared questions and report back.
Everything runs in Posit Cloud, and the prescribed text is R for Data Science (second edition).
What this guide covers
Thirteen chapters follow the twelve lecture weeks, from the three kinds of analytics question through reproducible workflows, visualisation, wrangling, reshaping and joining, to causal and predictive analytics and the two written products of the group assignment, with a drill chapter and a final-week plan.
Current deadlines, venues and any changes are published on the subject site.
Reading a table of group averages without overreaching
- 1State what the table does support.
- 1Give the two separate reasons it cannot answer the question asked.
- 1Name the next analytical step.
Key terms
- Business analytics
- The practice of turning a business decision into a question a dataset can answer, and reporting the answer with its limits attached.
- Descriptive analytics
- Summarising what already happened in a record, using only rows that exist.
- Predictive analytics
- Anticipating a value that has not been observed yet, judged on data the rule never saw.
- Causal analytics
- Establishing whether one thing produced another, which requires a comparison with what would otherwise have happened.
- Grain
- What a single row of a table represents, such as one customer, one order or one firm-year.
- Tidy data
- A table in which each column is a variable, each row is an observation and each cell holds one value.
- Pipe operator
- A connector that passes the result on its left into the first argument of the function on its right.
- Grouped summary
- A table reduced to one row per group, each carrying statistics computed over that group.
- Join key
- The column or set of columns shared by two tables that identifies what a row represents in both.
- Panel data
- Observations on many units across many periods, one row per unit and period.
- Random assignment
- Allocating units to treatment or control by chance, so unmeasured differences spread evenly across both.
- Corporate brief
- The short document aimed at an executive who has to decide something on the strength of a finding.
CMCE10002 FAQ
How is the subject assessed, and is any component a pass condition?
Four components are published: in-class quizzes during workshops worth 10 per cent across weeks 4 to 12, a mid semester test worth 20 per cent in week 6, a group assignment worth 20 per cent due in week 12, and a two-hour end of semester examination worth 50 per cent. The subject guide states that successful completion requires a pass in the final exam, and that condition is printed with the examination row alone.
What does the mid semester test cover and how is it sat?
It runs for one hour with no reading time and covers material from the first five weeks. It is sat in person as a Canvas quiz using the Respondus LockDown browser, and mixes multiple choice with short answer, including questions that ask students to write and interpret code. Installing and testing the browser beforehand is the students' own responsibility.
What is the structure of the final examination?
Two hours, hand written, drawing on lectures, workshops and the assigned readings. The subject guide describes 20 multiple-choice questions carrying 40 per cent of the paper, alongside roughly four short-answer questions that carry subparts and the remaining 60 per cent, some of which may ask students to write code on paper and say what it does.
What does the group assignment ask students to produce?
An analytics project on a business dataset with a clear research question and a reproducible analysis using descriptive, causal or predictive methods. The findings are communicated in two formats: an Analytics Team Memo, written for a peer or manager who will check the work, and a short Corporate Brief, written for an executive who has to decide something.
Which software is used, and does anything need to be installed?
All exercises and assignments run in R and RStudio through Posit Cloud, a browser-based environment that requires no installation and saves work online so it can be reopened from any device. The University covers the usage fee associated with the subject.
Is prior programming experience needed to take this subject?
It is not expected. The subject describes itself as technical while stating that students with basic numeracy, curiosity about how data and technology are changing business, and a willingness to engage in structured problem-solving are well positioned to succeed.
How may generative tools be used across the semester?
The subject runs a learn-first policy. Before the mid semester test these tools may be used to explain concepts but not to generate or debug code. After it, code use is permitted with documentation, which means citing the tool, including the prompts and output, and explaining in your own words what the generated code does. They are not permitted at any point during the quizzes or the mid semester test.
How to study for the exam
Both timed assessments in this subject are sat without a console, so the examinable skill is predicting what a table looks like after an operation rather than running one and reading the result. Build revision around that: cover the output, write down the row and column count you expect, then run the code and compare.
Work the weekly question sets, because they were designed to prepare both the multiple-choice and the short-answer components. In the final fortnight, sit the first mock paper cold and timed, sort its errors into recall failures and reasoning failures, and give the last three teaching weeks one deliberate pass, since nothing before the examination tests them.
Your AI Statistics tutor for CMCE10002
Stuck on a hard CMCE10002 question? Sia is AskSia’s AI Statistics tutor — ask any CMCE10002 Foundations of Business Analytics question and get a clear, step-by-step explanation grounded in how the course is actually taught and assessed. Read this whole study guide free, then take your hardest questions to Sia.
