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Full Curriculum

Our Subject Areas

Comprehensive coverage from foundational concepts to cutting-edge research — Mathematics, Statistics, Biostatistics, Programming, AI, and Data Science at every level.

Mathematics

The language of science and technology. We teach mathematics with clarity and rigour — building intuition alongside formal understanding, from basic arithmetic all the way to postgraduate research.

School Level

Grades 6–12 Mathematics

  • Number systems, fractions & decimals
  • Algebra: equations, inequalities, polynomials
  • Geometry: shapes, proofs, coordinate systems
  • Trigonometry: ratios, identities, wave functions
  • Introductory statistics & probability
  • Pre-calculus: functions, limits, sequences
  • Exam preparation (GCSE, A-Level, SAT, IB)
University Level

Undergraduate & Postgraduate Mathematics

  • Single & multivariable calculus
  • Linear algebra & matrix theory
  • Ordinary & partial differential equations
  • Real & complex analysis
  • Probability theory & stochastic processes
  • Abstract algebra: groups, rings, fields
  • Numerical methods & optimization
  • Topology & measure theory (postgrad)
Research Level

Mathematical Research Consultation

  • Mathematical modelling for research problems
  • Optimization theory & convex analysis
  • Statistical methodology for research design
  • Applied mathematics in engineering/physics
  • Proof writing & formal verification
  • Literature review & problem formulation
  • Dissertation & thesis support

Statistics

The science of data, uncertainty, and decision-making. We teach statistics with clarity and purpose — from introductory descriptive stats right through to graduate-level inferential methods and research design used by academics and industry professionals.

School Level

Introductory Statistics

  • Descriptive statistics: mean, median, mode, variance
  • Data collection, types & sampling methods
  • Charts, histograms, box plots & scatter plots
  • Basic probability: events, rules, tree diagrams
  • Normal distribution & z-scores
  • Introduction to correlation & simple regression
  • Exam prep (AP Statistics, IB Math, provincial)
University Level

Inferential & Applied Statistics

  • Probability distributions: Binomial, Poisson, Normal
  • Statistical inference: confidence intervals & p-values
  • Hypothesis testing: t-tests, ANOVA, chi-square
  • Simple & multiple linear regression
  • Non-parametric methods: Mann-Whitney, Kruskal-Wallis
  • Time series analysis & forecasting
  • Statistical software: R, SPSS, SAS, Python (SciPy)
Research Level

Advanced Statistical Consulting

  • Experimental design & power analysis
  • Multivariate analysis: PCA, factor analysis, MANOVA
  • Generalised linear models (GLMs) & mixed models
  • Bayesian inference & probabilistic programming
  • Causal inference: IV, DID, RDD, propensity scores
  • Survey design & complex sampling
  • Statistical review for publication & grant writing

Biostatistics

Specialised statistical methods for biology, medicine, public health, and clinical research. Whether you're analysing clinical trial data, studying epidemiology, or working on health data science, we offer expert guidance tailored to life sciences research.

School Level

Biology & Health Data Basics

  • Statistics in biology and scientific experiments
  • Reading and interpreting scientific graphs
  • Basic probability in genetics (Mendelian genetics)
  • Introduction to population health concepts
  • Data literacy: reading research papers critically
  • Introduction to R for biological data
  • Science fair & IB Biology statistical methods
University Level

Biostatistics & Epidemiology

  • Study design: RCTs, cohort, case-control studies
  • Measures of association: RR, OR, ARR, NNT
  • Survival analysis: Kaplan-Meier & Cox regression
  • Logistic regression for binary health outcomes
  • Repeated measures & longitudinal data analysis
  • Diagnostic tests: sensitivity, specificity, ROC curves
  • Statistical software: R (survival, lme4), SAS, STATA
Research Level

Clinical & Translational Research Support

  • Phase I–III clinical trial design & sample size
  • Adaptive trial designs & interim analysis
  • Meta-analysis & systematic review methodology
  • Genomic data analysis: GWAS, RNA-seq basics
  • Bayesian clinical trial models
  • Health technology assessment & decision modelling
  • Regulatory statistics (ICH E9, FDA guidance)
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Programming & Computer Science

From writing your very first line of code to designing scalable systems and contributing to open-source software. We teach programming with real projects and industry-grade practices.

School Level

Introduction to Programming

  • Block-based coding with Scratch
  • Python basics: variables, loops, functions
  • Introduction to HTML & CSS
  • Basic algorithms: sorting, searching
  • Logical thinking & problem decomposition
  • Introduction to databases and spreadsheets
  • Computing coursework & project support
University Level

Advanced Programming & CS

  • Data structures: trees, graphs, heaps, hash maps
  • Algorithm design & complexity analysis (Big-O)
  • Object-oriented & functional programming
  • Operating systems & memory management
  • Databases: SQL, NoSQL, query optimisation
  • Web development: React, Node.js, REST APIs
  • Software engineering & design patterns
  • C/C++ systems programming
Research Level

Research & Systems Consultation

  • Distributed systems & cloud architecture
  • Compiler design & language theory
  • Advanced algorithm research
  • High-performance computing & parallelism
  • Research code review & optimisation
  • Open-source contribution guidance
  • Technical paper writing & peer review prep

Artificial Intelligence

AI is reshaping every industry and academic field. Whether you're learning the fundamentals or developing novel models for your research, we cover the full spectrum with precision and depth.

School Level

Introduction to AI Concepts

  • What is artificial intelligence? History & overview
  • How machines learn — intuitive explanations
  • Simple rule-based systems & decision trees
  • Introduction to Python for AI
  • Ethical considerations in AI
  • Fun AI projects: image classification, chatbots
  • AI in everyday life: recommendations, search
University Level

Machine Learning & Deep Learning

  • Supervised, unsupervised & reinforcement learning
  • Linear/logistic regression, SVMs, decision forests
  • Neural networks: architectures & backpropagation
  • Deep learning with PyTorch / TensorFlow / Keras
  • Convolutional neural networks (CNNs)
  • Recurrent networks, LSTMs, Transformers
  • Natural language processing (NLP) fundamentals
  • Computer vision: detection, segmentation
Research Level

Advanced AI & Research Consultation

  • Large language models (LLMs) & fine-tuning
  • Generative models: GANs, VAEs, diffusion
  • Reinforcement learning from human feedback (RLHF)
  • Model interpretability & explainability (XAI)
  • Custom model design & training pipelines
  • AI for scientific research & domain applications
  • Research paper writing & experiment design

Data Science

Turn raw data into insight and action. We cover the full data science pipeline — from data collection and cleaning to sophisticated statistical modelling and communicating findings to stakeholders.

School Level

Introduction to Data & Statistics

  • Basic statistics: mean, median, variance
  • Data collection, organisation & cleaning
  • Charts & graphs: reading and creating
  • Introduction to spreadsheets (Excel / Google Sheets)
  • Probability fundamentals
  • Interpreting data in news and research
  • First steps in Python for data analysis
University Level

Data Science & Analytics

  • Python for data science: NumPy, Pandas, SciPy
  • Data wrangling, cleaning & feature engineering
  • Statistical inference & hypothesis testing
  • Data visualisation: Matplotlib, Seaborn, Plotly
  • SQL & relational databases for analysis
  • Machine learning pipelines with scikit-learn
  • Time series analysis & forecasting
  • R programming for statistics
Research Level

Advanced Analytics & Research Support

  • Bayesian inference & probabilistic programming
  • Advanced statistical modelling (mixed models, GLMs)
  • Causal inference & experimental design
  • Big data tools: Spark, Hadoop, cloud pipelines
  • Scientific data analysis & domain applications
  • Dashboard design & stakeholder communication
  • Academic publication & reproducibility support

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