The story
XY Coffee Co. began six years ago when its founder, Theo, sold bags of roasted beans from a single market stall. Today it roasts, ships, runs cafés, sells subscriptions through an app, and is starting to think like a real enterprise — which means it is starting to drown in questions it cannot answer with intuition alone. What price maximises profit? Which customers matter? How risky is next quarter? Is the new roastery worth building? Should we acquire a competitor?
Over the year you grow alongside the company. You start with a single product and the calculus needed to understand how cost, revenue and profit behave and where they peak. As the company scales and data floods in, you learn to make sense of it through descriptive statistics. Because the future is uncertain, you learn probability. Because the company must invest and raise money, you need financial mathematics. And when XY Coffee becomes multi-product and multi-region, you need the linear algebra that describes several dimensions at once — and finally, forecasting.
Prerequisite
Basic notions of pre-algebra and pre-calculus. Week 1 checks it honestly, and a voluntary Leveling Up module on Cyberlearn — videos, a self-test quiz and a blank HES-SO entry exam — is there for anyone who finds it rusty. Doing it in the first fortnight is the best investment available in this course.
Use of generative AI
Authorised, not mandatory, and it must be disclosed in full, including your prompts — say whether it was for form (spelling, reformulation) or for substance (analysis, recommendations). AI is a source and must be referenced as one, and its output must be understood and personalised by you.
The recommendation for this course specifically: do not ask AI for the answer — ask for the path to the answer. Ask for hints, for extra exercises, for alternative explanations. AI can be a sparring partner, but only if you meet it at the same level. Insist on the methodology rather than the result — the exams are closed book, and the methodology is the only thing that will be in the room with you.
What you'll be able to do
The course's official learning objectives. Each is a thing you will be able to do, with the block that teaches it — so you always know where you are on the map.
- Understand why numbers drive decisions. Recognise the quantitative-reasoning workflow — question → model → data → answer → decision — and know which step a meeting has skipped. Fall W1
- Turn business questions into a quantitative formalism. Represent relationships as functions of one variable, and move fluently between words, table, graph and formula. Fall W1–3
- Measure change, margins and growth. Compute and interpret marginal cost, marginal revenue, elasticity and growth rates. Fall W4–7
- Optimize value. Find the maxima and minima of a function, apply first- and second-order conditions, and translate a business goal into an objective function. Fall W8
- Make sense of data in a business environment. Collect it, sample it honestly, summarise it with numerical measures, and see the relationship between two variables. Fall W11–14
- Use basic probability and some probability distributions. Plan under uncertainty, discretely and continuously. Spring W1–5
- Understand the time value of money, and perform project valuation. Interest, present value, annuities, NPV, portfolio return and risk. Spring W6–9
- Elicit the structure behind the data with vectors and matrices. Handle many quantities at once, and apply matrix-vector calculus to management and finance problems. Spring W10–11
- Model the activity into the future. Linear regression, and an honest account of its uncertainty. Spring W12–14
How each one is practisedEvery objective is met three times: the lesson in Part 1 of the session, the exercises in Part 2 worked with the room, and the homework in your own time. If you can do the exercises without the hint, you have the objective.
