A curated, no-fluff list of the best resources for landing data and tech roles.
Covers data science, analytics, data engineering, ML, software, product, and more. Real questions, real interview loops, and the frameworks that actually win the room.
Maintained by Dataford · MIT License · Contributions welcome
Most interview prep is scattered. You bookmark a SQL tutorial here, a system design video there, and a behavioral PDF you never open again.
We built this list to fix that. It is the map we wish we had when we were prepping.
Everything here is curated. We favor resources that are specific, current, and close to what you actually face in a real loop. We keep self-promotion light and clearly labeled, so you can trust the list.
Maintained by the team at Dataford. Contributions welcome. See CONTRIBUTING.md.
- How to use this list
- Prep by role
- Prep by skill
- Prep by company
- Practice and mock interviews
- Books
- Newsletters and blogs
- YouTube channels
- Study plans
- Contributing
- License
Do not read it top to bottom. Use it like a menu.
- Find your role in Prep by role. That is your anchor.
- Pull the matching company guide from Prep by company. Know the exact loop before you study anything.
- Drill your weakest skill from Prep by skill.
- Practice out loud with mock interviews, not just in your head.
- Pick a timeline from Study plans and stick to it.
The candidates who land offers are not the ones who study the most. They are the ones who study the right thing for the loop in front of them.
Each role below links to a full Dataford guide with the real loop, the question patterns, and the evaluation criteria. Browse all guides for 50+ roles across 5,000+ companies.
| Role | What gets tested | Start here |
|---|---|---|
| Data Scientist | Product sense, applied stats, SQL, experimentation, behavioral | Meta DS guide |
| Data Analyst | SQL, dashboarding, storytelling, stakeholder communication | Data Analyst guide |
| Data Engineer | SQL, Python, ETL, data modeling, distributed systems | Meta DE guide |
| ML Engineer | ML theory, coding, ML system design, deployment | ML Engineer guides |
| Research Scientist | Algorithms, ML depth, research deep dive, system design | Meta RS guide |
| Software Engineer | Data structures, algorithms, system design, behavioral | SWE guides |
| Product Manager | Product sense, execution, estimation, behavioral | PM guides |
| Account Executive | Discovery, value selling, objection handling, closing | AE guides |
| AI Engineer | LLM systems, prompting, retrieval, ML fundamentals | Browse AI roles |
The skill that fails more strong candidates than any other. Get fast and clean under pressure.
- Dataford question bank - real SQL questions from top companies, run against live datasets with instant AI feedback.
- Mode SQL Tutorial - free, practical, analyst-focused.
- SQLBolt - interactive lessons that start from zero.
- PostgreSQL Mastery Cheat Sheet - SELECT to window functions in one reference.
For data roles the bar is clean, correct, readable code. For software roles it goes deeper into algorithms.
- NeetCode - the coding patterns roadmap, free and well structured.
- LeetCode - the standard practice bank. Filter by company and difficulty.
- HackerRank - good for timed drills and SQL crossover.
Tested heavily for data science and quant roles. Focus on intuition, not memorized formulas.
- StatQuest with Josh Starmer - the clearest stats and ML explanations on the internet.
- Khan Academy: Statistics and Probability - solid free foundation.
- Seeing Theory - visual, intuitive probability.
Know the fundamentals cold. Be ready to explain trade-offs, not just name algorithms.
- Designing Machine Learning Systems by Chip Huyen - the modern ML systems reference.
- Chip Huyen's blog - sharp writing on ML in production.
- StatQuest ML playlist - decision trees, boosting, and more, explained simply.
The differentiator for product data science. Most candidates underprepare here.
- Trustworthy Online Controlled Experiments by Kohavi, Tang, and Xu - the definitive book on A/B testing.
- Dataford mock interviews - practice experimentation cases graded by AI.
As AI absorbs more execution work, this is where offers are won. Learn to turn vague problems into clear data questions.
- Dataford mock interviews - timed product and business cases with per-question feedback.
- Lenny's Newsletter - product thinking from operators.
- Stellar Peers - product case frameworks and examples.
Required for software, ML, and data engineering loops at scale.
- System Design Primer - the classic open-source starting point.
- Designing Data-Intensive Applications by Martin Kleppmann - essential for data engineering and ML systems.
Underrated and very coachable. Use a tight structure and real stories.
- STAR method overview - the standard framework for structuring answers.
- Amazon Leadership Principles - the template many companies quietly borrow from.
- Dataford behavioral question bank - real behavioral prompts with model frameworks.
Know the exact loop before you study. Each hub has the real process, salary data, role coverage, and the questions candidates actually reported.
- Meta
- Amazon
- Apple
- OpenAI
- Salesforce
- Adobe
- Capital One
- JPMorganChase
- Lyft
- Instacart
- Browse all 5,000+ companies
Reading about interviews does not make you better at them. Reps do.
- Dataford question bank - 1,000+ SQL, Python, and behavioral questions with AI evaluation and instant feedback.
- Dataford mock interviews - timed simulations graded by AI, with detailed per-question notes.
- Pramp - free peer-to-peer mock interviews.
- LeetCode - company-tagged practice sets.
The short list worth your time.
- Ace the Data Science Interview by Nick Singh and Kevin Huo - data science end to end.
- Cracking the Coding Interview by Gayle Laakmann McDowell - the software interview classic.
- Designing Data-Intensive Applications by Martin Kleppmann - data systems at scale.
- Trustworthy Online Controlled Experiments by Kohavi, Tang, and Xu - A/B testing done right.
- Designing Machine Learning Systems by Chip Huyen - ML in the real world.
Stay current. The market moves fast.
- Dataford Blog - interview prep, SQL, and career strategy.
- The Pragmatic Engineer by Gergely Orosz - software industry and hiring.
- Lenny's Newsletter - product and growth.
- Chip Huyen's blog - ML systems and careers.
- StatQuest with Josh Starmer - stats and ML, made simple.
- Dataford - role and company interview walkthroughs.
Pick the runway that matches your timeline. Then protect the time.
4 weeks (you have an interview scheduled)
- Week 1: Pull your exact company and role guide. Map the loop. Drill SQL daily.
- Week 2: Your weakest core skill (stats, ML, or system design). Two hours a day.
- Week 3: Product and business cases out loud. Record yourself. Get feedback.
- Week 4: Full mock interviews, behavioral stories, and rest before the loop.
8 weeks (you are getting ready to apply)
- Weeks 1 to 2: Foundations. SQL and Python fluency until they feel automatic.
- Weeks 3 to 4: Statistics, experimentation, and ML fundamentals.
- Weeks 5 to 6: Product sense and case practice. Build a story bank of 8 to 10 examples.
- Weeks 7 to 8: Company-specific prep and repeated mock interviews under time pressure.
The goal is not to finish the list. It is to be ready for the loop in front of you.
Found a great resource that belongs here? We would love to add it. Read CONTRIBUTING.md first, then open a pull request.
We keep the bar high. Resources should be specific, current, and genuinely useful. Quality over quantity, always.
MIT. Use it, share it, build on it.
Maintained with care by Dataford - ace your next tech interview.
If this list helped you, a star helps others find it.