The Ultimate 21-Day Analytical Interview Prep Plan

This intensive 21-day plan is structured to build your skills from the ground up, moving from core technical abilities to strategic product thinking and, finally, to interview execution. Each day includes a primary focus, a specific “Do This” action, and targeted resources.

Week 1: Core Skills - SQL and Statistics Mastery

Goal: Build a rock-solid, practical foundation in the two most critical technical areas.

Day Topic Focus Do This Handbook Reference External Resources
1 SQL Fundamentals Complete SQLBolt’s interactive tutorials from start to finish. Focus on understanding JOIN logic. SQL & Data Manipulation SQLBolt
2 Intermediate SQL Solve 5 medium-difficulty problems on DataLemur involving GROUP BY, HAVING, and subqueries. SQL & Data Manipulation DataLemur Questions
3 Advanced SQL: Window Functions Read Mode’s guide on Window Functions. Then, solve 3 problems on StrataScratch that specifically require RANK(), LAG(), or ROW_NUMBER(). SQL & Data Manipulation Mode’s Window Functions Guide, StrataScratch
4 Applied Statistics: Probability & Distributions Watch the StatQuest videos on Probability, Binomial, and Poisson distributions. For each, write down one product-related example. Statistics & Probability StatQuest: Probability, Binomial, Poisson
5 Applied Statistics: Hypothesis Testing Read the “Trustworthy Online Controlled Experiments” book summary. Explain p-values, confidence intervals, and statistical power out loud. Hypothesis Testing Summary of “Trustworthy Online Controlled Experiments”
6 Python for Data Analysis Complete a full data cleaning and exploration project using Pandas to load, clean, and analyze a dataset. Programming (Python/R) Kaggle: “Data Cleaning Challenge”
7 Week 1 Review & Synthesis Write a SQL query using a window function. Then, use Python to calculate the statistical significance of a hypothetical A/B test result. Foundational Knowledge Use your own project or a Kaggle dataset

Week 2: Product Thinking & Experimentation

Goal: Shift from how to analyze data to what to analyze and why. This week is about product sense, metrics, and A/B testing.

Day Topic Focus Do This Handbook Reference External Resources
8 Developing Product Sense Pick a feature on Facebook or Instagram. Deconstruct it: What user problem does it solve? Who is the target user? How does it help the business? Analytical Reasoning/Product Sense Lenny’s Newsletter: “How to Develop Product Sense”
9 Metrics Frameworks (HEART/AARRR) Read about the HEART and AARRR frameworks. Apply one of them to the feature you analyzed on Day 8. Analytical Reasoning/Product Sense Google’s HEART Framework, AARRR Framework
10 Case Study Framework Learn a structured approach for case studies. Outline a response to: “User engagement on Instagram Reels has dropped by 5%. How would you investigate?” Analytical Execution/Case Study Exponent’s Guide to Data Science Case Studies
11 A/B Testing Deep Dive: Design Read Airbnb’s blog post on experimentation. Design an A/B test: form a hypothesis, choose success/guardrail metrics, and estimate sample size. Experimental Design Airbnb: “Experimentation & Measurement”
12 A/B Testing Deep Dive: Analysis Read Netflix’s blog on interpreting A/B test results. Analyze a hypothetical result: What if your primary metric improves but a guardrail metric declines? Hypothesis Testing Netflix: “Interpreting A/B test results”
13 Product Case Study Practice Complete one full case study walk-through. Record yourself speaking or write out a detailed document. Analytical Execution/Case Study StrataScratch’s Product Sense Questions
14 Week 2 Review & Synthesis Propose a new feature for a product you use daily. Define its North Star metric, design an A/B test, and explain how you would analyze the results. Analytical Reasoning/Product Sense Apply concepts from this week

Week 3: Execution, Storytelling & Mock Interviews

Goal: Synthesize all your skills and practice delivering your analysis under pressure.

Day Topic Focus Do This Handbook Reference External Resources
15 Behavioral Interview: STAR Method Prepare 3 stories using the STAR method: 1) A complex project, 2) Dealing with ambiguity, 3) Influencing a decision with data. Behavioral Interview Preparation The STAR Method Guide
16 Data Storytelling Watch a talk on data storytelling. Create a 3-slide presentation for your project from Day 6: 1) Problem, 2) Analysis & Insight, 3) Recommendation. Behavioral Interview Preparation Brent Dykes: “Winning The Insights War”
17 Timed SQL + Python Challenge Give yourself 45 minutes. Complete one hard SQL question and one medium Python/Pandas question back-to-back. Technical Skills Interview LeetCode or HackerRank
18 Full Mock Interview 1 (Technical) Conduct a mock technical interview with a peer or on a platform. Focus on thinking out loud and explaining your code. Technical Skills Interview Pramp (free peer-to-peer mocks)
19 Full Mock Interview 2 (Product/Case) Conduct a mock product sense or case study interview. Focus on asking clarifying questions and presenting a structured conclusion. Analytical Execution/Case Study Pramp, or record yourself
20 Review, Refine & Rest Review your notes from the mock interviews. Identify your single biggest area for improvement and do one focused exercise on it. Then, rest. Your own notes  
21 Final Polish & Mindset Review Meta’s core values. Re-read your 3 STAR stories and align them with those values. Do a light 30-minute review of key concepts. Behavioral Interview Preparation Meta’s Core Values

Key Success Metrics

  • Week 1: Master SQL fundamentals and statistical concepts
  • Week 2: Develop product intuition and experimentation skills
  • Week 3: Perfect interview execution and storytelling

Additional Helpful Resources

You might also find these to be helpful practices:

Daily Commitment

  • Time Investment: 2-3 hours per day
  • Focus Areas: Technical skills → Product thinking → Interview execution
  • Practice Method: Active learning with real projects and mock interviews
45 mins Intermediate