Course Sections

Ontario Curriculum-Aligned STEM Tutoring and Future-Ready Computational Learning

Each course is organized around concepts, practice, worksheets, exam readiness and clear learning outcomes. The goal is to support school success while preparing motivated students for advanced STEM pathways.

01

Physics

Grades 7–12 · including senior physics support

Core Topics

  • Motion in one and two dimensions
  • Forces, Newton’s laws and applications
  • Work, energy, momentum and collisions
  • Electric fields, magnetic fields and circuits
  • Waves, light, optics and modern physics foundations

Student Outputs

  • Unit-by-unit formula sheets
  • Graph interpretation and free-body diagram practice
  • Problem-solving notebooks
  • Lab-report and data-analysis support
  • Mock test and exam review sessions

Best For

  • Grade 9–10 science support
  • Grade 11 Physics preparation
  • Grade 12 Physics preparation
  • Students planning engineering, science or computing pathways
02

Chemistry

Grades 7–12 · concept, calculation and lab reasoning

Core Topics

  • Properties of matter and atomic structure
  • Elements, periodic trends and bonding
  • Chemical compounds, names and formulas
  • Reactions, acids, bases and stoichiometry
  • Organic, inorganic and biochemistry foundations

Student Outputs

  • Periodic table and reference-sheet practice
  • Step-by-step stoichiometry method sheets
  • Reaction balancing drills
  • Lab safety and lab report guidance
  • Unit review and exam preparation

Best For

  • Middle-school science strengthening
  • Grade 9–10 chemistry units
  • Senior chemistry readiness
  • Students who need confidence in calculation-based chemistry
03

Mathematics

Grades K–12 · foundations to calculus and vectors

Core Topics

  • Arithmetic, algebra and equations
  • Polynomials, factoring and functions
  • Linear relations, slopes and graph modelling
  • Geometry, trigonometry and measurement
  • Calculus, vectors, matrices, probability and statistics

Student Outputs

  • Skill checklists for each unit
  • Worked-example notebooks
  • Calculator and graphing support
  • Exam review sheets
  • Contest-style enrichment problems

Best For

  • Students needing stronger fundamentals
  • Grade 9–10 transition support
  • Senior functions, calculus and vectors
  • Students preparing for physics, coding and ML
04

Python Programming

Grades 6–12 · beginner to project-ready

Core Topics

  • Variables, loops, conditionals and functions
  • Lists, dictionaries and data structures
  • Debugging and clean code habits
  • Files, plotting and numerical examples
  • NumPy, Pandas and starter data analysis

Projects

  • Math visualizer
  • Physics calculator
  • Quiz or flashcard app
  • Data plotting dashboard
  • Mini portfolio project

Best For

  • Students new to coding
  • Students preparing for AI/ML
  • Students who enjoy math/science projects
  • Students building a STEM portfolio
05

AI & Machine Learning

Grades 9–12 · Python and math-based pathway

Core Topics

  • Data science foundations
  • Regression and classification
  • Model training and validation
  • Gradient descent intuition
  • Neural networks and deep learning concepts

Math Connection

  • Functions and graphs
  • Vectors and matrices
  • Derivatives and optimization
  • Error/loss functions
  • Data interpretation and uncertainty

Best For

  • Students who know basic Python
  • Students learning calculus or advanced functions
  • Students curious about AI research
  • Students wanting guided ML projects

Academic Support System

What students receive during tutoring

PySTEM tutoring is designed around a structured academic support system rather than one-off explanations.

Unit Calendars

Planned sequence of topics, review days, practice sessions and test-prep checkpoints.

Worksheets

Practice sets organized by difficulty: foundation, school level, challenge and exam review.

Reference Sheets

Formula, unit, graphing, periodic-table, calculator and coding-reference support.

Lab & Project Help

Guidance for lab reports, data analysis, scientific communication and Python/AI projects.

Study Skills

Exam planning, note-making, spaced review, mistake tracking and confidence-building strategy.

Parent Feedback

Progress summaries and next-step recommendations after learning cycles.

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