Harvard CS50: Introduction to Computer Science
CS50 is Harvard's famous intro to computer science, taught by David Malan. Through CS50x on edX, it is almost certainly the most-taken and most-searched college course in the world. It moves from C through data structures, memory, and algorithms to Python, SQL, and web development, ending with a final project.
Fennie is independent and not affiliated with Harvard University. This is an unofficial study guide.
What makes it hard
The difficulty curve is front-loaded and steep: weeks of C (pointers, memory management, segfaults) before anything forgiving like Python appears. Problem sets take far longer than newcomers expect, often 10-20 hours, and self-learners on CS50x stall most often around weeks 3-5 when the scaffolding drops away.
What you'll cover
- • C programming and memory
- • Arrays, pointers, and data structures
- • Algorithms and Big-O basics
- • Python
- • SQL and databases
- • Web development with HTML, CSS, JavaScript, and Flask
- • Final project
The CS50 study guide
How to study for Harvard CS50, step by step.
- 1
Watch the lecture, then the shorts, before touching the pset
CS50's shorts break each week's concepts into focused pieces the lecture moves through quickly. The order works the same for enrolled students and for anyone self-pacing through CS50x.
- 2
Budget 10-20 hours per problem set
The psets take far longer than newcomers expect, and underestimating them is how people quit. Block the time across several days, because debugging needs sleep between sessions.
- 3
Treat the C weeks as the boss fight
Weeks 3-5 (pointers, memory, segfaults) are where most CS50x learners stall. Slow down there deliberately: use debug50 and valgrind, draw memory diagrams, and rubber-duck your code line by line.
- 4
Re-explain each week's concepts before moving on
Before starting a new week, explain the previous week's ideas out loud or in writing without notes. CS50 builds cumulatively; a shaky week 4 makes week 7 miserable.
Today
Today's CS50 plan
What a Fennie Daily Plan looks like for CS50. Yours is built from your own syllabus and adapts every day to your deadlines and progress.
First plan free, no card required. Fennie is independent and unaffiliated with your school.
FAQ
Is CS50 hard?
Yes, especially the C weeks. Pointers and memory management humble most beginners. It's designed to be completable with no experience, but expect 10-20 hours per week on problem sets.
How long does CS50x take to finish?
Self-paced learners typically take 3-6 months alongside work or school. The course has roughly 10 problem sets plus a final project, and consistent weekly hours matter more than intensity.
Is CS50x the same as Harvard's CS50?
The lectures and problem sets are the same material, published free on edX with a verified certificate option. The difference is you self-pace without sections, office hours, or Harvard credit.
More Harvard courses
CS 51: Abstraction and Design in Computation
CS 51 is the standard course after CS50 for Harvard CS concentrators, teaching functional programming in OCaml alongside design principles: abstraction, modularity, and multiple programming paradigms. It's where students go from making code work to making it well-designed.
CS 124: Data Structures and Algorithms
CS 124 is Harvard's algorithms course, covering divide and conquer, greedy algorithms, dynamic programming, graph algorithms, hashing, and NP-completeness. It combines rigorous analysis with programming assignments, and it's a core theory requirement for CS concentrators and a known interview-prep powerhouse.
CS 61: Systems Programming and Machine Organization
CS 61 is Harvard's systems programming course, covering C and C++, assembly, memory, caching, process control, and concurrency. It's one of the two standard follow-ons to CS50 for CS concentrators. Its course site publishes lecture notes and problem sets publicly, so it also draws self-learners looking for a systems sequel to CS50.
CS 109A: Data Science 1: Introduction to Data Science
CS 109A, cross-listed as Stat 109A, is the first half of Harvard's data science sequence: data wrangling, exploratory analysis, regression, classification, and model evaluation in Python. Past course materials are published openly on the teaching team's site, giving it a large self-study audience beyond enrolled students.