HKU PMGM7023: Management Analytics — course overview with AskSia
PMGM7023 Management Analytics is a 6-credit elective in HKU Business School's Master of Global Management, a one-year full-time programme of ten 6-credit courses: five core courses including the capstone, and five electives.
Sia generates PMGM7023 practice questions, walks through principles of evidence-based decision-making and the framework of data science step by step, and quizzes you on the material the component that weights most heavily.
Worked example
A regional manager says branch A outperforms branch B because A's mean monthly sales are higher. Following the course's evidence-based framework, what should be checked before acting on the claim?
The course replaces intuition and convenience with explicit use of the best available evidence.
Regression then asks whether branch size, location or season explain the gap.
Best-month comparisons and intuition are exactly the decision rules the syllabus sets out to replace.
The trap: classic slip!
Overview
What PMGM7023 is, and where it sits
PMGM7023 Management Analytics is a 6-credit elective in HKU Business School's Master of Global Management, a one-year full-time programme of ten 6-credit courses: five core courses including the capstone, and five electives.
The syllabus opens from a premise: managers often decide on intuition, chance, preference, convenience or tradition, and need a scientific framework for using the best available evidence conscientiously, explicitly and judiciously. Lectures cover the foundation of decision-making, the principles of evidence-based decision-making, the framework of data science, and applications of management analytics in real or simulated decision scenarios. In-class exercises use R, the open-source software, on descriptive statistics, data visualization, analysis of variance and regression, forecasting, and social network analysis.
The syllabus publishes no course-level assessment split; the programme rule is coursework of 50-100% and examination of 0-50% unless the instructor specifies otherwise, so this page shows no weighting bar.
Always treat your own course outline and the exam timetable as authoritative.
Difficulty & time commitment
Is PMGM7023 hard, and how much time does it take?
PMGM7023 is manageable if you keep a weekly rhythm and treat the back half as the main event. The pattern is consistent: it starts gently and steepens, and the heaviest assessment is the part that separates grades.
The difficulty curve and the assessment weighting point the same way: the back half is harder and worth more. Front-loading effort there is the highest-return decision in the course.
Is this course for you
Who tends to do well, and who tends to struggle
You will likely do well if
- You want to leave the MGM able to run an analysis, not just commission one.
- You can sit with a script that fails and fix it.
- You can state a management decision as a question data could answer.
You may struggle if
- You expect a case-discussion course; a third of the syllabus is R exercises.
- You skip the in-class exercises and try to learn R from notes later.
- You treat regression output as a verdict rather than evidence to be judged.
- Rebuild every in-class R exercise on your own data within the week.
- For each analysis, write the manager's decision it informs and the caveat that goes with it.
- Keep one script that runs descriptive statistics, a plot, a regression and a forecast end to end; it is your revision.
Syllabus
The 10 topics, topic by topic
Topics from the public source. Their order is a content map, not an official weekly timetable or published exam weighting.
T1 · The foundation of decision-making
Lecture topic 1Why intuition, convenience and tradition fail as decision rules.
T2 · Principles of evidence-based decision-making
Lecture topic 2Conscientious, explicit and judicious use of the best available evidence.
T3 · The framework of data science
Lecture topic 3From question to data to model to decision.
T4 · Descriptive statistics in R
R exercise 1Summarising managerial data in R.
T5 · Data visualization in R
R exercise 2Charts that make evidence legible to decision-makers.
T6 · Analysis of variance and regression
R exercise 3Testing differences and modelling relationships in R.
T7 · Forecasting
R exercise 4Projecting a managerial series forward and judging the forecast.
T8 · Social network analysis
R exercise 5Mapping relationships in an organisation or market.
T9 · Applications in decision-making scenarios
Lecture topic 4Real or simulated cases that put the analytics to work.
T10 · Working in R
SyllabusThe open-source software used for every in-class exercise.
Assessment
How this course is assessed
A component-by-component weighting breakdown is not published for this course. Rather than estimate one, we publish only what the course itself states. Check your current course outline on Moodle for the exact percentages.
- MGM regulations: assessment is normally coursework (50-100%) and examinations (0-50%) unless otherwise specified by the course instructor; only passed courses earn credits; candidates need a cumulative GPA of 2.0 or above, and failing more than two courses over the curriculum leads to discontinuation.
- The published programme rule allows an examination of up to 50%; whether this course sets one, and its format, is announced by the instructor. The lecture topics are the framework of evidence-based decision-making and the R exercise topics its tools.
How to actually pass it
A weekly rhythm, two checklists, and the traps to avoid
The course rewards consistency over cramming, and practice over re-reading. Here is the loop that works, then what to have nailed before each exam.
The weekly loop
The mistakes that cost marks
Formula & concept sheet
The vocabulary and formulas you must own
- Evidence-based decision-making
- Using the best available evidence conscientiously, explicitly and judiciously to make a managerial decision.
- Descriptive statistics
- Summaries such as means, spreads and counts that describe a dataset.
- Analysis of variance
- A test of whether group means differ more than chance would produce.
- Regression
- A model of how an outcome varies with one or more predictors.
- Forecasting
- Projecting a time series forward with a stated uncertainty.
- Social network analysis
- Measuring relationships among people or units as a network.
- R
- Open-source statistical software used for the in-class exercises.
- Data visualization
- Presenting data graphically so a pattern can be seen and judged.
Where it fits
Prerequisites, related courses & why it matters
No prerequisites listed; 6 credits; elective in the Master of Global Management (ten 6-credit courses over one year full-time).
Your PMGM7023 study toolkit
Study the course with Sia, not just read about it
Use your permitted course notes with these study tools. Follow your course’s rules for AI and assessed work.
FAQ
Frequently asked questions
Is PMGM7023 hard?
Moderate on the six-factor rubric, level with the HK anchor: the concepts are managerial, but the in-class work is statistics in R up to regression, forecasting and network analysis.
What is the assessment?
The MGM syllabus publishes only a programme-wide range, coursework 50-100% and examination 0-50% unless the instructor specifies otherwise. No course-level split is published, so none is shown here.
Do I need to know R already?
The syllabus lists R as the tool for in-class exercises and does not state a prerequisite; expect to learn it in class.
Which analytics are covered?
Descriptive statistics, data visualization, analysis of variance and regression, forecasting, and social network analysis.
Is it core or elective?
An elective; MGM students take five electives alongside five core courses. It is not one of the ESG-stream electives.
Who teaches it?
The public syllabus does not name the teaching staff.
Study PMGM7023 with Sia
Practise explaining the course concepts and check your reasoning. Confirm assessed-work requirements in your current outline.
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