LeetCode teaches algorithms,
not the domain.
If you are preparing for a data scientist or yield engineer role at ASML, Applied Materials, TSMC, Lam Research, or Intel, standard prep is not working because standard prep was not built for this domain. The questions you will face are not about inverting binary trees. They are about whether you know why a rolling baseline destroys a CUSUM detector, why Leave-One-Chamber-Out validation exists, and whether the model you deployed can be trusted to make the right call at 2 AM when a process engineer is deciding whether to pull a $200,000 lot based on what your code said.
The curriculum map shows a specific ordered path for each of the four starting points above.
What this is
Every component was built around a specific gap between what DS courses teach and what semiconductor companies actually test.
You have the statistics and Python but no semiconductor background. The Learning Path starts with the Interview Prep Manual and pairs each section with the missions that make the concepts stick. Domain depth comes after the offer.
You come from medtech, aerospace, or MEMS. You recognize the patterns but the physical constraints that change which patterns apply are new. The platform flags every place where your existing intuition needs calibration for a fab context.
You know the domain. The risk is overconfidence: strong intuitions but weak ability to articulate the DS framing interviewers now score. The Wafer Journey interview zones and the Round 1 Gauntlet surface exactly where the gap is before your panel.
The job tests execution, not recall. The Field Manual, the Wafer Journey production checklists, and the process-area missions are what you return to in your first year when the data is messier than any training set.