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Quantitative Foundations

Algebra, functions and the math comfort you need before Math for AI & ML

6 phases. 20 lessons. 20 labs. 1 capstone. Built for adults who haven't done real math in years, not high schoolers — variables as labeled boxes, negative numbers understood geometrically, functions and graphs, exponents and logarithms, unit-circle trigonometry, and rate of change, each motivated by a real AI-flavored question before any formal notation. Sits directly before this platform's Math for AI & ML course.

6 phases · 20 lessons · 20 labs · 1 capstone

No prerequisites — this is a starting track.

Outcomes you will have by the end

• A translated, solved, and verified growth model — A real word problem translated into notation by hand, solved by hand (including using logarithms), and verified with a working Python model.
• Real comfort with the math AI courses assume — Functions, exponents and logarithms, unit-circle trigonometry, and rate of change — not proofs, but the working comfort Math for AI & ML's own opening lessons assume you already have.
• Verified certificate of completion — Issued by DeVenture Academy, tied to your completion record. Lists the algebra, functions, and quantitative reasoning skills you built.

What you will be able to do

Algebraic Reasoning · Functions & Graphs · Exponents & Logarithms · Trigonometric Intuition · Rate of Change · Mathematical Translation

Every phase, every lesson, every project

• Numbers, Variables & Expressions (3 lessons) — free — Variables as labeled boxes, order of operations, negative numbers as geometric rotation, solving equations by undoing steps.
  • Why Letters in Math?
  • Negative Numbers, Geometrically
  • Simplifying and Solving
• Functions & Graphs (4 lessons) — free — Functions as predictable rules, reading and building graphs, slope as rise over run, when a straight line stops being enough.
  • What Is a Function?
  • Reading and Building Graphs
  • Linear Functions and Rate
  • When Functions Aren't Straight Lines
• Exponents, Logarithms & Growth (4 lessons) — Exponential growth as repeated multiplication, logarithms as the exact undo, growth/decay, an honest preview of softmax and log-loss.
  • Repeated Multiplication
  • Logarithms: Undoing Exponents
  • Growth and Decay in the Real World
  • Where This Shows Up in AI (Preview)
• Trigonometry & Geometry Intuition (4 lessons) — Distance via the Pythagorean theorem, the unit circle, cos θ as an honest preview of cosine similarity, geometry in high dimensions.
  • Distance and the Pythagorean Theorem
  • The Unit Circle
  • Why cos θ Measures Similarity
  • Geometry in More Than Two Dimensions
• Sequences, Series & Rate of Change (3 lessons) — Arithmetic vs. geometric sequences, moving averages, average rate of change converging into instantaneous rate of change.
  • Sequences: Patterns Over Steps
  • Sums and Averages Over Time
  • From Slope to Rate of Change to a Calculus Preview
• Capstone (2 lessons) — Translating a real growth scenario into notation by hand, solving it, and verifying the solution with working Python.
  • Translate the Problem
  • Solve It, Then Code It

The technologies you will use

Python · matplotlib

Roles this course prepares you for

• Data Analyst (entry-level) ($65k–$90k) — Turn business questions into computable models and interpret quantitative results — the exact comfort with variables, functions, and rate of change this course builds.
• ML/AI Engineer (via Math for AI & ML) ($120k–$200k) — Understand and implement the math behind ML algorithms — this course is the comfort-building step before that course's own linear algebra, calculus, and probability content.

Common questions

I haven't done real math since high school — is this too advanced?

No — this course is built specifically for adults returning to math, not high schoolers. Every lesson opens with a real question and builds visual intuition before any formal notation, following research on how adults actually re-learn math comfortably.

Do I need Programming & CS Foundations first?

No — this is a parallel entry point, not a sequential one. You can take Programming & CS Foundations and Quantitative Foundations in either order, or alongside each other.

Is this the same as Math for AI & ML?

No — Math for AI & ML teaches linear algebra, calculus, and probability by implementing them in code. This course builds the algebra and functions comfort that course assumes you already have, so its opening lessons don't feel like a wall.

Are there exams or grades?

No — every source on adult numeracy is clear that confidence, not rigor-signaling, is what this stage of math education needs. This course has practice and a capstone, never a graded exam.

Key terms in this course

Cosine Similarity · Softmax

Continue your learning path

Programming & CS Foundations · Math for AI & ML

Start the Quantitative Foundations course

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