Statistics programs in Ontario

44 programs across 15 campuses, with published admission ranges starting at 70%. Sorted by the range each one publishes, lowest first, so you can see where you land.

Programs

Campuses

Lowest range

70%

What this leads to

The picture for statistics as of 2026-08-17, not a description of this particular program.

Statistics graduates become data analysts, business intelligence analysts, or research statisticians in government, finance, and tech companies. Some pursue graduate studies in related fields like actuarial science or biostatistics to access higher-paying roles.

In its favour

AI/ML roles are seeing 45%+ demand increase in Canada, and statistics qualifications feed directly into these growing positions
Starting salaries for bachelor's degree holders average $44/hour, well above trades, and statistics offers a path to six-figure earnings with specialization
Statistics is a genuine technical skill that isn't easily outsourced and remains hard to automate entirely—pattern recognition and judgment still require human statisticians

Worth knowing

Entry-level data analyst roles are increasingly crowded—lots of bootcamp graduates and self-taught people competing for the same junior positions
Core statistical work—data cleaning, routine analysis, basic report generation—is already being automated or generated by AI tools
Many employers hire computer science graduates into data roles instead of statistics graduates, leaving pure stats degrees less clearly valued in the job market
You'll likely need a grad degree (Master's in Statistics or Data Science) to move beyond entry-level work, meaning four extra years and tuition after undergrad

How AI is changing this work

AI is already doing the mechanical parts of statistics—running models, cleaning datasets, generating reports. What's growing is the work of defining the right questions, interpreting what the models actually mean, and explaining results to non-technical people. If you graduate in 2031, you won't be competing against AI for the routine analysis work. You'll be directing AI toward useful problems, which means the job exists but looks different: fewer hours running regressions, more hours thinking about whether you're measuring the right thing and what the numbers actually tell you.

Where this came from (6 sources)