Cracking the US Data Science & ML Interview

What you'll learn

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Read a job Description

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for what it actually tests and pick the right DA / DS / MLE / AS track
Diagram any company's full loop and know each round's real pass bar
Allocate prep time by cost-effectiveness: clear the gates first, then build differentiators
Pass the phone screen: Python and pandas fluency, plus exactly enough LeetCode with a spaced-review drilling system
Answer probability and statistics questions out loud, deriving sample size on the spot
Design an A/B test that survives probing, and read out a decision you'd sign
Defend classical ML under deep-dive questioning, from derivation to "compared to what"
Handle the LLM questions now embedded in every round, and build one real LLM project
Requirements

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Basic Python: you can read a function and a loop
No GPU, no paid API, and no prior machine learning required
Targeting US DS / DA / MLE / Applied Scientist roles, including interns and new grads
Description

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This course contains the use of artificial intelligence.
Interviews are expensive. A real number: 350 applications produced four to five interviews. You cannot afford to practice on them.
This is not another machine learning theory course. It is organized byinterview round: what the phone screen tests, what signal each of the four onsite rounds is really reading, and what to prepare when. The content comes from one-on-one coaching that continued and kept current, including what has changed lately: whiteboards returning, LLM becoming a resume gate, and interviewers actively screening for candidates reciting answers.
You build four projects on real data: a 480,000-user email A/B experiment where the primary metric "wins" and you still have to veto the launch; a real conversion model; half a million Amazon reviews solved classically and with a transformer, compared on cost and quality; and a timed pandas screen simulation. Every project ships with a verified solution notebook. They go on your resume and they survive the deep-dive.
Every module has a quiz, and every wrong option is a mistake real candidates made.
Two sentences run through the whole course:"if it's on the page, it's fair to ask", every resume line must survive probing, andclear the gates first, then build the highlights.
Module 0 is free. Watch it and you'll know whether this is what you were looking for.
The other three OfferLab courses: NLP to LLMs · The US Tech Interview: Behavioral, Communication, Offer · The ML System Design Interview. Each stands on its own; take them in whatever order fits what you are preparing for.
Who this course is for

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Master's students and new grads targeting US DS/ML roles
Working data analysts and product data scientists who want to convert to MLE
Experienced data scientists with strong modeling but rusty LeetCode and system design
Not for: people who want ML theory for its own sake, or senior MLEs targeting staff loops
Homepage

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