The programs you write in this course — loops, arrays, methods, searches — are the same building blocks used in the research that is reshaping medicine, security and communication. The difference is scale and question. A researcher is someone paid to work on a problem nobody has solved yet, which means most of the job is designing experiments, reading other people's results, writing code to test an idea, and finding out it does not quite work. This lesson looks inside five active fields, what a working day in each involves, and the hard questions each one raises. Then it turns the interest into a plan: which Grade 12 courses keep doors open, how co-op works, and what to put in a portfolio.
Machine learning: programs that learn from data
In ordinary programming you write the rules. In machine learning you supply examples — thousands of labelled photos, transcripts or transactions — and a training algorithm adjusts millions of internal numbers until the model's outputs match the examples. The model is then tested on data it never saw during training, because doing well on the training data alone proves nothing. Researchers spend much of their time not on clever models but on the data: collecting it, cleaning it, labelling it and checking what it leaves out. That is where bias comes from. A model learns whatever patterns its data contains, including unfair ones. The 2018 Gender Shades study by Joy Buolamwini and Timnit Gebru found that commercial face-analysis systems misclassified darker-skinned women far more often than lighter-skinned men, a gap widely linked to who was, and was not, well represented in the data used to build and test such systems. Privacy is the other pressure: training data often comes from people's photos, messages or health records, and in Canada the use of personal information by businesses is governed by federal privacy law (PIPEDA). Canada is a major centre for this research, with institutes such as the Vector Institute in Toronto, Mila in Montréal and Amii in Edmonton.
Four more frontiers, and what researchers do in each
| Field | What researchers actually do | The hard question | Ontario connection |
|---|---|---|---|
| Quantum computing | Design algorithms for qubits, build and control the hardware, and invent error correction so fragile quantum states survive long enough to compute | Shor's algorithm could break today's public-key encryption on a large enough machine, so data must move to post-quantum methods first | Institute for Quantum Computing at the University of Waterloo; Xanadu in Toronto |
| Cybersecurity | Find vulnerabilities before attackers do, analyse malware, design cryptographic protocols, and investigate incidents | How to protect systems and privacy without blanket surveillance | University and college security programs; the federal Canadian Centre for Cyber Security |
| Human-computer interaction and accessibility | Run studies watching real people use systems, prototype and test interfaces, and design for screen readers, switches and voice | Who gets excluded when software is designed only for its designers | The AODA requires many Ontario organizations to make websites meet WCAG accessibility guidelines |
| Bioinformatics | Write programs to align DNA sequences, assemble genomes, predict protein structure, and find patterns in huge biological datasets | Who controls genetic data, and how consent and privacy are protected | Hospital and research institutes such as SickKids and the Ontario Institute for Cancer Research |
Why are so many of these fields growing now? For decades, Moore's law — the observation that the number of transistors on a chip doubled roughly every two years — delivered faster computers almost automatically. That doubling has slowed as transistors approach the size of a few atoms, so progress now comes from doing things differently: specialized chips for machine learning, quantum hardware for problems classical machines handle badly, and smarter algorithms. Quantum computers are not simply faster ordinary computers. A register of n qubits is described by 2ⁿ numbers at once, which is why 50 or so good qubits can be hard to simulate classically, but only certain problems — factoring, simulating molecules, some optimization — are expected to benefit. Meanwhile, standards bodies have already published post-quantum encryption algorithms, such as the standards the United States National Institute of Standards and Technology released in 2024, so that systems can switch before large quantum machines exist.
Routes into the field from Ontario
| Route | Typical Grade 12 requirements | What it involves | Leads toward |
|---|---|---|---|
| University computer science | ENG4U, MHF4U and MCV4U are commonly required; MDM4U is often recommended; ICS4U helps but is not always required | Four years, theory plus programming; co-op versions alternate study terms with paid work terms | Software development, research, machine learning, security, graduate study |
| University software or computer engineering | ENG4U, MHF4U, MCV4U, SPH4U and SCH4U are typical | Accredited engineering program, often five years with co-op | Systems and hardware work, and a route toward professional engineer status |
| College programming, cybersecurity or IT | Grade 12 English, and often a Grade 12 math such as MAP4C, MCT4C or a U-level math | Two or three years, hands-on labs, often with a work placement | Developer, analyst, network and security roles; transfer agreements into degrees |
| Secondary school options now | Co-op credits, a Specialist High Skills Major (SHSM) in information and communications technology where offered | Real workplace experience and sector certifications before graduating | References, experience and a clearer idea of what you enjoy |
A portfolio and a skills plan
- Two or three finished projects beat ten abandoned ones. Each should run, solve a real problem and be small enough to explain in a minute.
- Publish the code in a public repository with a README that says what the project does, how to run it, and what you would improve next.
- Show process, not only results: test cases, a design sketch, and a commit history that shows the project grew in sensible steps.
- Choose at least one project that touches an emerging field — a small data analysis, a password-strength checker, an accessible interface, a DNA-sequence counter.
- Record evidence of teamwork and communication too: a group project, a club, a competition such as a programming contest, or a co-op placement.
| When | Courses and learning | Experience and portfolio |
|---|---|---|
| Rest of Grade 11 | Finish ICS3U strongly; keep the university-preparation math sequence | Build one small project of your own outside class and publish it |
| Grade 12 | ICS4U, MHF4U, MCV4U and ENG4U; MDM4U for data-heavy fields | Co-op or SHSM if available; a second project in a field that interests you |
| Applications | Check each program's prerequisites and admission averages early in the fall | Portfolio link, a short description of each project, and the skills it shows |
| Postsecondary | Core programming, data structures, discrete math, statistics | Co-op terms, research assistant roles, open-source contributions |