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#EECS#Python#Math#CS

Course

EECS 0

Term

Spring 2026

Schedule

Tue / Thu lecture + Sat recitation

Lessons

3

Course overview

High-level framing for what the course is doing and how to use it.

This tutorial hub is meant to feel like a real first-year EECS course page: the notes live on-site, the math is written out cleanly, and every lesson can branch into a video, recitation sheet, lab, or homework when needed.

  1. Use the lesson notes as the primary source rather than treating the site like a PDF archive.
  2. Treat code, proofs, and examples as three views of the same idea instead of separate subjects.
  3. Move through the schedule from left to right: notes first, then recitation or lab, then homework.

Weekly rhythm

How the class runs

  • Read the lesson page first and work through the examples in order.
  • Open the linked alternate video only if you want a second explanation or a different pace.
  • Do the recitation or lab immediately after the notes so the ideas turn into practice.
  • Attempt the homework last, without jumping straight to solutions.

Course themes

Main intellectual threads

  • Variables, functions, conditionals, loops, and small Python programs.
  • Precise mathematical language: sets, logic, vectors, and clean symbolic reasoning.
  • Translating an idea across prose, notation, and executable code.
  • Testing mathematical claims with short programs instead of only reading them.

Suggested prerequisites

What students should bring

Almost none.

  • Comfort with algebra and basic manipulation of expressions.
  • Willingness to read definitions carefully.
  • Patience for debugging small mistakes without panicking.

Course direction

Extra framing

The goal is not to make you a Python wizard in one week. The goal is to build the early EECS habit of moving cleanly between words, symbols, and code, then checking whether the result actually says what you think it says.

References

Outside notes, textbooks, or course pages worth keeping around.

course inspiration

MIT 6.0001: Introduction to Computer Science and Programming in Python

Solid reference for the programming half: lectures, notes, and problem sets.

math reference

MIT 6.1200J: Mathematics for Computer Science

Proofs, sets, graphs, counting, and discrete probability in one place.

python docs

Official Python Tutorial

Use this whenever you want the canonical explanation of syntax and standard features.