MIT 6.1010: Fundamentals of Programming
6.1010, formerly 6.009 (the number much of the internet still uses), is MIT's second programming course, where Python fluency from 6.100A gets turned into real software through substantial weekly labs: audio processing, image filters, graph search, interpreters. It's the bridge between knowing Python and engineering with it.
Fennie is independent and not affiliated with MIT. This is an unofficial study guide.
What makes it hard
Each lab is a working program of real size, graded by test cases that probe edge cases mercilessly. Recursion and graph search are the conceptual peaks, and the course's refusal to hand you scaffolding (you design the structure) is exactly the adjustment students coming from fill-in-the-blank intro courses struggle with.
What you'll cover
- • Program design and decomposition
- • Recursion and recursive structures
- • Graph search algorithms
- • Higher-order functions and closures
- • Object-oriented design
- • Interpreters and environments
The 6.1010 study guide
How to study for MIT 6.1010, step by step.
- 1
Design on paper before typing
6.1010 labs reward structure: sketch the data representation and function decomposition before writing code. Students who start typing immediately spend the saved minutes many times over in restructuring.
- 2
Write your own test cases first
The graders probe edge cases, so beat them to it: empty inputs, single elements, cycles in graphs. Testing your understanding of the spec is half of each lab.
- 3
Make recursion reps a weekly habit
Recursive thinking carries the hardest labs, including the interpreter. Beyond the lab itself, write small recursive functions weekly until call trees feel as natural as loops.
- 4
Debug with hypotheses, not print storms
State what you believe, find the smallest input that breaks the belief, and fix the model. The discipline matters more here than in any intro course because the programs are finally big enough to hide bugs.
Today
Today's 6.1010 plan
What a Fennie Daily Plan looks like for 6.1010. 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 6.1010 the same as 6.009?
Yes. 6.009 became 6.1010 in MIT's 2022 EECS renumbering. Online discussion and older materials mostly use 6.009; the course lineage is the same.
Is 6.1010 hard?
The labs are substantially bigger than intro-course psets and the test cases are unforgiving. Students who design before coding and write their own tests find the workload steady rather than spiky.
What should I take before 6.1010?
Python fluency at the 6.100A level: comfortable with functions, lists, dictionaries, and basic OOP. It pairs with or precedes 6.1200, and leads into 6.1020 and 6.006.
More MIT courses
6.100A: Introduction to Computer Science Programming in Python
6.100A, formerly 6.0001 (the number most search results still use), is MIT's half-semester introduction to programming in Python for students with little or no experience. The 6.0001 lectures on OpenCourseWare are among the most popular free programming courses anywhere.
6.006: Introduction to Algorithms
6.006 is MIT's core algorithms class, covering sorting, hashing, trees, graph algorithms, shortest paths, and dynamic programming, with emphasis on both rigorous analysis and Python implementation. Its OpenCourseWare lectures are a global standard for learning algorithms and prepping technical interviews.
6.046J: Design and Analysis of Algorithms
6.046J, renumbered 6.1220 in MIT's current catalog but still searched overwhelmingly by its old number, is the advanced algorithms course following 6.006: divide and conquer, randomized algorithms, amortization, network flow, approximation, and complexity. The OCW lectures are a staple for advanced self-study.
6.042J: Mathematics for Computer Science
6.042J, now numbered 6.1200J, is MIT's discrete math course for CS: proofs, induction, number theory, graph theory, counting, and discrete probability. Its OCW versions, with full lecture videos and the famous free textbook, make it one of the most-used discrete math resources in the world.