Start here
Pick the description that sounds like you. Each path gives a first course, first lessons, what to skip, and what comes next.
Start with 6.100L. Do the first three lessons slowly and ignore transcripts/media files.
What to skip for now
Skip old 6.0001 until you can write small Python programs with loops and functions.
Next step
After 6.100L basics, move to 6.0001 or 6.0002 depending on your goal.
Use 6.0001 to strengthen computational thinking, then choose data science or algorithms.
What to skip for now
Do not worry about every exam file first; prioritize lectures and practice exercises.
Next step
Go to 6.0002 for data/modeling or 6.042J before 6.006 for algorithms.
Start 6.042J with propositions, proof patterns, and induction.
What to skip for now
Do not force full calculus first unless your goal is modeling, finance, or AI math.
Next step
Use selected 18.01 modules after proof basics.
Start 6.006 only after Python basics and early 6.042J proof/graph material.
What to skip for now
Do not start here as a complete beginner; the course assumes programming maturity.
Next step
Pair lectures with problem sessions and selected assignments.
Start 6.0002 after Python basics. Use 6.042J probability/counting and 18.01 derivatives as references.
What to skip for now
Do not try to finish every math course before touching modeling; use prerequisites just in time.
Next step
After 6.0002, deepen probability, calculus, and algorithms depending on your path.
Follow the full roadmap. It starts with beginner programming and only adds math, data, and algorithms when they become useful.
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