MIT Mathematics for Computer Science

MIT Mathematics for Computer Science

This course is a part of the Bridging Module for my Data Science Master's.


Before we begin

Hey, it’s David. I wrote this overview back in 2016. Since then, I’ve become a professional data analyst and created courses for multiple industry-leading online education companies.

Do you want to become a data analyst, without spending 4 years and $41,762 to go to university? Follow my latest 27-day curriculum and learn alongside other aspiring data pros.

Okay, back to the overview.


Course Description

From the course website:

"This course covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include:

  • Formal logic notation, proof methods.

  • Induction, well-ordering.

  • Sets, relations.

  • Elementary graph theory.

  • Integer congruences.

  • Asymptotic notation and growth of functions.

  • Permutations and combinations, counting principles.

  • Discrete probability.

  • Further selected topics may also be covered, such as recursive definition and structural induction, state machines and invariants, recurrences, and generating functions."


Why Take This Course?

A course in discrete mathematics is a requirement for the majority of undergraduate computer science programs. Completing this course, along with the other two courses in my bridging module, means I will have completed a standard first-year computer science curriculum, plus the full mathematical and statistical core.