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Foundation Track

Zero to Algorithm: Maths & Stats for Data Science

Make Math Your Competitive Edge in Data Science.

Instead of memorising formulas, you will finally understand what they mean, why they work, and how they power real Data Science and Machine Learning systems.

Duration

10 weeks

Difficulty

Beginner–Intermediate

Ideal For

Math-rusty learners

Talk to a MentorView Curriculum

Designed For

Aspiring Data Scientists who fear math

5 themed modules that build from intuition to application.

Learn with everyday analogies first, then see the formal notation only when you're ready.

Perfect pre-requisite before any serious Data Science or AI program.

Curriculum Overview

From Rusty to Ready — Step by Step.

Each module deepens your understanding while keeping one promise: you always know why you're learning a concept and where it shows up in Data Science.

1

Module 1

THE FOUNDATION

Brushing off the rust and re-learning how to think mathematically.

▼

Key Themes

The Absolute BasicsFunctions & GraphsCoordinate GeometryThe Application
  • •Set theory (Unions, Intersections) and fundamental number systems.
  • •Understanding Linear, Quadratic, Exponential, and Logarithmic functions (Crucial for understanding how data grows and scales).
  • •Plotting equations on a graph, calculating slopes, and understanding distance formulas.
  • •How basic equations form the boundary lines that separate data in simple classification problems.
2

Module 2

THE DIMENSIONS

How data is represented and manipulated by computers.

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Key Themes

Stepping UpMatrix OperationsAdvanced SpacesThe Application
  • •Starting with simple Scalars (single numbers) and Vectors (lists of numbers).
  • •Moving up to Matrices (grids of numbers), Dot products, and Matrix multiplication.
  • •Tensors, Factorization, Eigenvalues, and Eigenvectors.
  • •Understanding the exact math behind Principal Component Analysis (PCA) and Dimensionality Reduction.
3

Module 3

THE OPTIMIZER

How Machine Learning algorithms actually “learn” and improve.

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Key Themes

Calculus 101The Chain RuleAdvanced OptimizationThe Application
  • •What is a limit? Understanding the “Rate of Change” and what a Derivative represents in plain English.
  • •The fundamental calculus behind Deep Learning and backpropagation.
  • •Partial derivatives and understanding multi-dimensional gradients.
  • •A step-by-step mathematical breakdown of Gradient Descent (How models minimize error and reach peak accuracy).
4

Module 4

THE PROBABILITY

The logic of chance, distributions, and predicting the unknown.

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Key Themes

The Rules of ChanceDistributions & SamplingAdvanced BayesThe Application
  • •Understanding independent vs. dependent events. Expected Value. Combinations and permutations.
  • •The Law of Large Numbers. PDF vs. PMF. Mastering key distributions: Normal, Binomial, and Poisson.
  • •Conditional probability and a complete mathematical breakdown of Bayesian Theory.
  • •Understanding the exact mathematical engine behind the Naive Bayes classification algorithm.
5

Module 5

THE STATISTICS

Evaluating data, proving hypotheses, and measuring model accuracy.

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Key Themes

Descriptive MechanicsEstimation & CovarianceInferential StatisticsRigorous Hypothesis Testing
  • •Calculating Measures of Central Tendency (Mean, Median, Mode) and Measures of Dispersion (Variance, Standard Deviation, IQR).
  • •Maximum Likelihood Estimation (MLE), Pearson vs. Spearman correlation, and mapping Covariance Matrices.
  • •The Central Limit Theorem (CLT), calculating Confidence Intervals, and understanding Statistical Power.
  • •Mastering p-values, Z-tests, T-tests, Chi-Square, and ANOVA. Deep dive into Type I vs. Type II Errors (The foundation of Precision and Recall in AI).
Learning Outcomes

By the end of this course, you will:

  • Think mathematically about data, not just follow formulas.
  • Understand the equations behind popular ML algorithms.
  • Read ML blogs, papers, and documentation without getting intimidated by symbols.
  • Be fully prepared for advanced Data Science & AI programs.

Who Is This For?

  • •Students and professionals who feel “rusty” with math but want to enter Data Science.
  • •Engineers and analysts who know tools but want to understand the theory behind them.
  • •Anyone who has feared math in the past but is ready for a fresh, intuitive restart.

You'll work with

Pen & paperDesmos / Geogebra

Ready to stop being scared of math?

If you're serious about Data Science, this is the course that finally makes the equations feel natural, not scary.

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