Math, statistics, and analytics careers use numbers to find real answers and make better decisions — in business, science, sports, and beyond. This course covers the math these roles depend on, why pairing it with computing makes you employable, and how to prove your skills. It builds on the STEM Foundations course.

Math plus computing plus a real-world domain — the analytics combination.

Foundations for Math, Statistics & Analytics Careers

Analytics careers run on a clear math core:

- **Calculus and linear algebra** — the language of change and of data with many dimensions.
- **Probability** — reasoning about uncertainty and chance.
- **Statistics** — collecting data and drawing honest conclusions from it.

These let you answer questions like "how likely is this?", "is this difference real or just luck?", and "what should we expect on average?" — the daily work of statisticians, actuaries, and operations-research analysts.

The math toolkit behind statisticians, actuaries, and analysts.

Calculus, probability, and statistics

The PDF makes a sharp point: **employability rises when math is paired with computing and a real domain.** Math alone is powerful; math plus programming plus knowledge of an actual field (finance, healthcare, logistics) is what employers pay for.

- **Computing** lets you handle real, messy datasets and build models.
- **Domain knowledge** tells you which questions matter and whether an answer makes sense.

So the winning combination isn't just "be good at math" — it's "use math, with code, to solve a real problem someone cares about."

Why math plus code plus a domain beats math alone.

Pairing math with computing

Calculus, probability, statistics, and pairing math with computing.

Thinking in Numbers

Routes into analytics careers vary by role:

- Most start with a **four-year degree** in math, statistics, data science, or a related field; research roles often add a **graduate degree**.
- **Actuaries** are special: you build the credential by passing a **series of professional exams**, which rewards steady, disciplined study and pays well.

The common thread is that you can demonstrate rigorous quantitative reasoning — through a degree, exams, or a strong body of analytical work.

From four-year degrees to the actuarial exam series.

Degrees, exams, and credentials

Analytics evidence is **analysis other people can inspect**:

- **Models and dashboards** built from real, public datasets.
- **Write-ups** that show your question, method, result, and limitations.
- **Competitions** (like data-science challenges) and internships.

**Try it:** find a free public dataset about something you care about, ask one clear question of it, make a simple chart that answers it, and write two sentences on what you found and what you're unsure about.

Show your analysis, not just your grades.

Evidence: models, dashboards, and competitions

Degrees, professional exams, and evidence.

Foundations for Math, Statistics & Analytics Careers

About this course

Course content

Thinking in Numbers

Proving Your Skills