Interview preparation roadmap · Data
Statistics and experiments, machine learning, SQL and Python, and product or business cases.
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- 01Interview foundations
- 02Foundations
- 03Machine learning
- 04Product & experiments
- 05Portfolio & design
- 06Company & final prep
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01Interview foundations
What every interviewer sees first.
01.1Resume
A one-page resume you can defend line by line.
What this covers
- One-page format — Reverse-chronological, clean fonts, no photos or tables that confuse parsers.
- Project bullets — Action + what you built + a number (users, speed, accuracy).
- Skills section — Only skills you can answer questions on for two minutes.
- ATS readability — Export a text-based PDF and check it with a parser.
- Working links — GitHub, LinkedIn and a live project link that open.
- Tailoring — Reorder bullets to match the job description.
What to practise
- Run your PDF through a parser test
- Rewrite each bullet as action + result
- Remove any skill you can't explain for 2 minutes
Common questions
- Walk me through your resume.
- Which project here are you most proud of, and why?
- Why is this skill on your resume?
Free resources
- OpenResume — Free ATS-friendly resume builder and parser test (Free)
- Jake's Resume (Overleaf) — The LaTeX resume template most SDE freshers use (Free)
- Resume Worded — Resume and LinkedIn scoring (Free plan)
- Jobscan — Match your resume against a job description (Free plan)
Free courses
- Professional Resume Writing — Alison (Free course)
- Resume, Networking, and Interview Skills — Fullbridge (Free to audit)
01.2Tell me about yourself
A 60–90 second answer: present, past, why this role.
What this covers
- Present — Who you are now: degree, year, focus.
- Past — One or two proof points: a project, internship or result.
- Future — Why this role and this company next.
- Length — 60–90 seconds; stop before you list everything.
- Delivery — Calm pace and eye contact, not a memorised script.
What to practise
- Write it, then say it out loud 5 times
- Record yourself and cut anything over 90 seconds
Common questions
- Tell me about yourself.
- Why should we hire you?
- Where do you see yourself in a few years?
Free resources
- Google Interview Warmup — Free spoken mock interview that prints your transcript (Free)
- BBC Learning English — Structured free English lessons and podcasts (Free)
Free courses
- English@Work: Basic Job Interview Skills — The Hong Kong Polytechnic University (Free to audit)
- Job Interview Preparation For Tech Professionals — University of Cape Town (Free to audit)
01.3Behavioral & STAR
Stories about teamwork, conflict and failure, told with STAR.
What this covers
- STAR structure — Situation, task, action, result. Spend most of the time on action.
- Story bank — 6–8 real stories you can reuse across questions.
- Teamwork & conflict — Disagreeing respectfully and what you changed.
- Failure & weakness — A real one, what you learned, what you do now.
- Ownership — Times you took initiative without being asked.
- Common HR questions — Why us, why you, strengths, relocation.
What to practise
- Write 6 stories in STAR form
- Map each story to 2–3 common questions
- Practise one story with a friend
Common questions
- Tell me about a time you failed.
- Describe a conflict in a team and what you did.
- What is your biggest weakness?
Free resources
- Tech Interview Handbook — Free guide to coding and behavioral interviews (Free)
- Google Interview Warmup — Free spoken mock interview that prints your transcript (Free)
- Pramp / Exponent peer mocks — Free peer-to-peer mock interviews (Free plan)
Free courses
- Master Behavioral Interviews (for Software Engineers) — freeCodeCamp (Free video)
- Behavioral Interview Practice for Computer Science Students — CodeSignal (Free to audit)
- Soft Skills For Tech Professionals — University of Cape Town (Free to audit)
02Foundations
Maths and code.
02.1Statistics & probability
The statistics data interviews commonly test, explained in plain words.
What this covers
- Descriptive statistics — Mean, median, variance, percentiles.
- Probability — Conditional probability and Bayes' rule.
- Distributions — Normal, binomial, Poisson and when they apply.
- Sampling & CLT — Why sample means behave predictably.
- Hypothesis testing — p-values, confidence intervals, errors.
- Correlation vs causation — Confounders and how to argue carefully.
What to practise
- Explain a p-value to a non-technical friend
- Solve 20 probability questions
Common questions
- What is a p-value?
- Difference between correlation and causation?
- When would you use the median instead of the mean?
Free resources
- Khan Academy: Statistics and probability — Free statistics course with practice (Free)
- StatQuest — Statistics and ML explained clearly, in videos (Free)
- Khan Academy — Maths and science from scratch (Free)
Free courses
- Descriptive statistics | Probability and Statistics | Khan Academy — Khan Academy (Free video)
- Inferential statistics | Probability and Statistics | Khan Academy — Khan Academy (Free video)
- Random variables and probability distributions | Probability and Statistics | Khan Academy — Khan Academy (Free video)
02.2Python for data
pandas and NumPy for analysis and interview exercises.
What this covers
- pandas basics — Selecting, filtering, groupby, merge.
- NumPy — Arrays and vectorised operations.
- Visualisation — matplotlib or seaborn basics.
- Notebooks — Clear, re-runnable analysis.
- Performance — Avoiding slow loops.
What to practise
- Redo one SQL analysis in pandas
- Publish one clean notebook
Common questions
- How do you merge two DataFrames?
- Difference between apply and vectorised operations?
Free resources
- Kaggle Learn — Short practical courses: Python, Pandas, ML, SQL (Free)
- StrataScratch — Real SQL and Python data interview questions (Free plan)
- Google Colab — Free notebooks with a GPU (Free plan)
Free courses
- Data Analysis with Python and Pandas — Sentdex (Free video)
- Data Analysis w/ Python 3 and Pandas — Sentdex (Free video)
- Pandas & Python for Data Analysis by Example – Full Course for Beginners — freeCodeCamp (Free video)
02.3SQL for analytics
Business questions answered in SQL, from joins to window functions.
What this covers
- Joins & filters — Inner, left, anti-joins and their traps.
- Aggregation — GROUP BY, HAVING, conditional sums.
- Window functions — RANK, LAG, running totals, moving averages.
- CTEs & subqueries — Breaking hard questions into steps.
- Dates — Grouping by week or month, cohorts.
- NULLs & duplicates — Handling messy data correctly.
What to practise
- Solve 30 interview-style SQL questions
- Write a monthly retention query from scratch
Common questions
- Find the second-highest salary per department.
- Calculate 7-day rolling average sales.
- Difference between WHERE and HAVING?
Free resources
- Mode SQL tutorial — SQL for analysis, from basics to window functions (Free)
- DataLemur — SQL and analytics interview questions (Free plan)
- StrataScratch — Real SQL and Python data interview questions (Free plan)
- SQLZoo — SQL by doing, in the browser (Free)
Free courses
- SQL and Relational Databases 101 — IBM (Free course)
- CS50's Introduction to Databases with SQL — Harvard University (Free video)
03Machine learning
The core technical rounds.
03.1Machine learning fundamentals
Core ML concepts asked in data scientist and ML interviews.
What this covers
- Supervised learning — Regression, classification, common algorithms.
- Bias–variance — Underfitting, overfitting, regularisation.
- Evaluation — Train/test splits, cross-validation, precision, recall, ROC.
- Feature engineering — Encoding, scaling, leakage.
- Trees & ensembles — Random forests and gradient boosting.
- Unsupervised learning — Clustering and dimensionality reduction.
What to practise
- Train and evaluate a model on a public dataset
- Explain precision vs recall with your own example
Common questions
- What is overfitting and how do you prevent it?
- When would you use precision over recall?
- How does a random forest work?
Free resources
- Machine Learning Crash Course — Google's free introduction to machine learning (Free)
- StatQuest — Statistics and ML explained clearly, in videos (Free)
- Kaggle Learn — Short practical courses: Python, Pandas, ML, SQL (Free)
- Machine Learning Interviews book — Free book on ML interview questions and process (Free)
Free courses
- Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018 — Stanford University (Free video)
- Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati) — Stanford University (Free video)
- Stanford CS229: Machine Learning I Spring 2022 — Stanford University (Free video)
03.2Deep learning
Neural networks, training and modern architectures.
What this covers
- Neural network basics — Layers, activations, backpropagation.
- Training — Loss functions, optimisers, learning rates.
- Regularisation — Dropout, batch norm, data augmentation.
- CNNs — Convolutions for images.
- Transformers — Attention and why it works.
- Frameworks — PyTorch or TensorFlow basics.
What to practise
- Train a small CNN and explain its errors
- Implement a tiny neural net from scratch
Common questions
- What is backpropagation?
- Why do we need activation functions?
- What is attention?
Free resources
- fast.ai — Practical deep learning, top-down (Free)
- Andrej Karpathy — Zero to Hero — Build a neural net and a GPT from scratch (Free)
- Hugging Face Learn — NLP, LLM, agents and diffusion courses (Free)
- DeepLearning.AI short courses — Short, focused LLM and agent courses (Free plan)
Free courses
- Stanford CS224N: Natural Language Processing with Deep Learning | Winter 2021 — Stanford University (Free video)
- Stanford CS224N: Natural Language Processing with Deep Learning Course | Winter 2019 — Stanford University (Free video)
- MIT 6.7960 Deep Learning, Fall 2024 — Massachusetts Institute of Technology (Free video)
03.3Programming fundamentals
One language you know well: syntax, memory, complexity.
What this covers
- One language deeply — C++, Java or Python: syntax, standard library, quirks.
- Time & space complexity — Big-O of loops, recursion and common operations.
- Recursion — Base cases, the call stack, when it overflows.
- Memory basics — Stack vs heap, references vs values, garbage collection.
- Strings & arrays — Immutability, copying and slicing costs in your language.
- Debugging — Reading errors, dry runs, using a debugger.
What to practise
- Solve 10 easy problems without an IDE's help
- Explain the complexity of each solution aloud
Common questions
- What is the time complexity of your solution?
- What happens in memory when you call a function?
- Difference between an array and a linked list?
Free resources
- Harvard CS50x — The best first CS course there is (Free)
- HackerRank — Where many campus tests actually run (Free)
- GeeksforGeeks — Company-wise questions and theory (Free plan)
Free courses
- CS50's Introduction to Programming with Python (CS50P) 2022 — Harvard University (Free video)
- MIT 6.100L Introduction to CS and Programming using Python, Fall 2022 — Massachusetts Institute of Technology (Free video)
- Programming in Modern C++ — IIT Kharagpur (Free course)
03.4Data structures & algorithms
The core problem-solving topics commonly assessed in software interviews.
What this covers
- Arrays & hashing — Frequency maps, prefix sums, duplicates.
- Two pointers & sliding window — Pairs, subarrays and substrings in O(n).
- Stacks & queues — Brackets, monotonic stacks, BFS queues.
- Linked lists — Reversal, cycle detection, merging.
- Trees & BST — Traversals, height, LCA, validation.
- Graphs — BFS, DFS, topological sort, shortest paths.
- Binary search — On sorted arrays and on the answer.
- Dynamic programming — 1D and 2D DP, from climbing stairs to knapsack.
What to practise
- Follow one list end to end, not five
- Say the brute force first, then improve it
- One timed problem a day
Common questions
- What is the difference between BFS and DFS?
- How does a hash map handle collisions?
- Find the first non-repeating character in a string.
Free resources
- NeetCode 150 — A curated ordered list instead of 3000 random problems (Free)
- Striver's A2Z DSA sheet — India's most-followed DSA sheet (Free)
- LeetCode — The default interview practice set (Free plan)
- GeeksforGeeks — Company-wise questions and theory (Free plan)
Free courses
- Data Structures and Algorithms Design — IIT Kanpur (Free course)
- MIT 6.006 Introduction to Algorithms, Spring 2020 — Massachusetts Institute of Technology (Free video)
- Neetcode 150 Course - All Coding Interview Questions Solved — freeCodeCamp (Free video)
04Product & experiments
How models meet decisions.
04.1A/B testing & experiments
Designing and reading experiments, a frequent product and data topic.
What this covers
- Experiment design — Hypothesis, metric, randomisation unit.
- Sample size & power — Why tests need enough users.
- Reading results — Significance, confidence intervals, practical impact.
- Pitfalls — Peeking, novelty effects, multiple tests.
- When not to test — Ethics, small samples, network effects.
What to practise
- Plan a full A/B test for a button change
- Interpret a sample experiment readout
Common questions
- How long would you run this test?
- The test is significant but tiny. Would you launch?
Free resources
- Exponent — Product, data and engineering interview questions and guides (Free plan)
- Khan Academy: Statistics and probability — Free statistics course with practice (Free)
- StatQuest — Statistics and ML explained clearly, in videos (Free)
04.2Analytics case questions
Structured answers to "metric dropped" and "should we launch" questions.
What this covers
- Clarify the question — Definitions, time frame, segments.
- Hypotheses — Internal vs external causes, data issues first.
- Segmenting — Platform, region, new vs returning.
- Sizing & estimates — Back-of-envelope numbers.
- Recommendation — A clear answer with next steps.
What to practise
- Solve five metric-drop cases aloud
- Practise one estimation question a day
Common questions
- Orders fell 15% last week. How do you investigate?
- How would you measure the success of a new feature?
Free resources
- Exponent — Product, data and engineering interview questions and guides (Free plan)
- DataLemur — SQL and analytics interview questions (Free plan)
- PrepLounge — Case partners and practice cases (Free plan)
Free courses
- Optimization Methods in Business Analytics — MITx (Free course)
- Business Analytics & Text Mining Modeling Using Python — IIT Roorkee (Free course)
- Business Analytics & Data Mining Modeling Using R Part II — IIT Roorkee (Free course)
04.3Business metrics
KPIs, funnels and retention: the language of analytics interviews.
What this covers
- KPIs — Revenue, conversion, active users, churn.
- Funnels — Step-by-step conversion and drop-offs.
- Retention & cohorts — Who comes back and when.
- Unit economics — CAC, LTV, margins.
- North-star metrics — One metric that reflects value delivered.
What to practise
- Define 5 KPIs for an app you use
- Build a cohort retention table from sample data
Common questions
- What metrics would you track for a food delivery app?
- Daily active users dropped 10%. What do you check?
Free resources
- Exponent — Product, data and engineering interview questions and guides (Free plan)
- StrataScratch — Real SQL and Python data interview questions (Free plan)
- Mode SQL tutorial — SQL for analysis, from basics to window functions (Free)
Free courses
- Mastering 11 Essential Business KPIs — Alison (Free course)
- Top Five KPIs for Small Businesses — Alison (Free course)
05Portfolio & design
Projects and systems.
05.1Data portfolio projects
Two or three projects that show how you think with data.
What this covers
- Pick real questions — A question someone would pay to answer.
- Show the process — Data source, cleaning, analysis, limits.
- Clear write-up — Summary first, charts second, code last.
- Public links — GitHub, Kaggle or a dashboard link.
- Defending choices — Be ready for "why this method?"
What to practise
- Finish one end-to-end project with a written summary
- Present a project in 5 minutes to a friend
Common questions
- Walk me through a project you are proud of.
- What would you do with more time or data?
Free resources
- Kaggle Learn — Short practical courses: Python, Pandas, ML, SQL (Free)
- GitHub — Where your projects live (Free)
- Tableau Public — Free Tableau to build and publish dashboards (Free)
Free courses
- Data Analysis Projects — Great Learning (Free course)
- Exploratory Data Analysis Projects — Great Learning (Free course)
- Python Projects for Data Analysis — Great Learning (Free course)
05.2ML system design
Designing ML products end to end; common in experienced ML interviews.
What this covers
- Problem framing — Turning a business goal into an ML task.
- Data & labels — Sources, labelling, leakage.
- Modelling choices — Baselines first, then complexity.
- Serving — Batch vs real time, latency.
- Monitoring — Drift, feedback loops, retraining.
What to practise
- Design a recommendation system on paper
- Design spam detection and discuss metrics
Common questions
- Design a feed ranking system.
- How would you detect fraud in payments?
Free resources
- Machine Learning Interviews book — Free book on ML interview questions and process (Free)
- System Design Primer — Open-source guide to designing large systems (Free)
05.3Technical mock interviews
At least two full coding mocks before the real one.
What this covers
- Thinking aloud — Narrate your approach before and while coding.
- Clarifying questions — Confirm inputs, scale and edge cases first.
- Handling hints — Take them gracefully and adapt.
- "I don't know" — Say what you do know and how you would find out.
- Review the recording — Fillers, pace, unclear explanations.
What to practise
- Do two peer mock interviews
- Record one spoken mock and listen back
Common questions
- Walk me through your approach before you code.
- How would you test this?
Free resources
- Pramp / Exponent peer mocks — Free peer-to-peer mock interviews (Free plan)
- Google Interview Warmup — Free spoken mock interview that prints your transcript (Free)
- Tech Interview Handbook — Free guide to coding and behavioral interviews (Free)
Free courses
- Software Engineering Job Interview – Full Mock Interview — freeCodeCamp (Free video)
06Company & final prep
The last 48 hours.
06.1Company research
The product, the role, the job description and recent news.
What this covers
- The business — What it sells and to whom.
- The role — What the job description really asks for.
- Recent news — Launches, results, announcements.
- Interview format — The rounds, if the company shares them.
- Your fit — Three reasons, matched to the job description.
What to practise
- Write 3 reasons you want this role
- Match 3 JD lines to your experience
Common questions
- Why do you want to work here?
- What do you know about our product?
Free resources
- GeeksforGeeks — Company-wise questions and theory (Free plan)
- Tech Interview Handbook — Free guide to coding and behavioral interviews (Free)
06.2Questions to ask
Two or three good questions for the end of the interview.
What this covers
- About the team — How work is planned and reviewed.
- About growth — Mentoring, learning, the first 90 days.
- About the product — What is hard right now.
- What to avoid — Questions a quick search answers.
What to practise
- Write 5 questions, keep the best 3
- Prepare one question about the team's own product
Common questions
- Do you have any questions for us?
Free resources
- Tech Interview Handbook — Free guide to coding and behavioral interviews (Free)
06.3Final checklist
Logistics, documents, setup and a calm night before.
What this covers
- Logistics — Time, timezone, link or address.
- Documents — Resume copies, ID, certificates if asked.
- Tech check — Camera, mic, internet, charger.
- Night before — Sleep, light revision, no new topics.
What to practise
- Do a 5-minute tech check the day before
- Lay out documents and clothes the night before
Free resources
- Google Interview Warmup — Free spoken mock interview that prints your transcript (Free)
06.4Job & interview safety
Spot fake offers, payment requests and fake interview links.
What this covers
- Payment requests — Real employers don't charge for jobs or training.
- Email domains — Check it matches the company's own site.
- OTPs & logins — Never share them with anyone.
- Fake interview links — Care with unknown chat apps and downloads.
- Reporting — The 1930 helpline and cybercrime.gov.in.
What to practise
- Check one recent offer against these rules
- Confirm the opening is listed on the company's own careers page
Free resources
- National Cyber Crime Portal — Report online fraud and job scams (Free)
- Have I Been Pwned — Check if your email appears in a data breach (Free)