How to Prepare for a Data Science Interview in 2026 (An Honest Guide)

# How to Prepare for a Data Science Interview in 2026 (An Honest Guide) Let's get the obvious thing out of the way: I tutor Python and data science for a living, so you might expect me to tell you that the answer to interview prep is "buy more tutoring." It isn't. Most people I talk to are already working hard — they're just working hard on the wrong things. So here's the actual answer: **a data science interview in 2026 mostly tests whether you can pull the right data, manipulate it without making errors, reason about a model out loud, and connect all of that to a business question — and almost everything candidates over-prepare lives outside that core.** The flashy stuff (memorising gradient-descent derivations, grinding leetcode-hard dynamic programming) rarely shows up. The boring stuff (SQL fluency, explaining a tradeoff clearly, noticing that a column is full of garbage) shows up constantly, and it's where people lose offers. Below is what actually matters, what's overhyped, what's quietly under-prepared, and a realistic plan to get ready. I'll be honest about the parts that are genuinely a coin-flip depending on the company. ## What actually gets tested After years of interviewing and teaching people through this process, the same handful of skills come up again and again. None of them are exotic. ### 1. SQL fluency — non-negotiable If I could make you practise one thing, it would be this. Almost every data role — analyst, data scientist, ML-adjacent — gates on SQL, and it's the single most common reason a candidate flames out early. Not because the questions are hard, but because people can write SQL slowly and can't write it *live*, under mild pressure, while talking. You need to be genuinely comfortable with joins (including the difference between a `LEFT JOIN` that preserves rows and an `INNER JOIN` that silently drops them), `GROUP BY` with `HAVING`, and window functions. Window functions are the dividing line between "studied SQL" and "uses SQL." A classic prompt is "find the second-highest value per group": ```sql SELECT department, employee, salary FROM ( SELECT department, employee, salary, DENSE_RANK() OVER ( PARTITION BY department ORDER BY salary DESC ) AS rnk FROM employees ) ranked WHERE rnk = 2; ``` If that snippet feels comfortable — and you can explain why `DENSE_RANK()` behaves differently from `RANK()` and `ROW_NUMBER()` when there are ties — you're ahead of most candidates. If it doesn't, this is the highest-return thing you can fix before any interview. ### 2. pandas and data manipulation The second-most-tested skill, and the one that splits the room in take-homes. You'll get a messy dataset and be asked to clean it, reshape it, and pull something meaningful out. The work is unglamorous: handling missing values deliberately (not just `.dropna()` and hoping), grouping, merging frames, reshaping between long and wide, and dealing with dates and duplicates. The trap is that people memorise pandas like a phrasebook — they know `groupby` exists but freeze when the actual data doesn't match the tutorial. What interviewers want to see is whether you *interrogate* the data before you trust it: ```python df.isna().mean().sort_values(ascending=False) # where are the holes? df.duplicated().sum() # silent duplicates? df["amount"].describe() # does the range make sense? ``` That habit — look before you leap — is worth more than knowing twenty methods. I'll come back to this, because reading messy data is one of the most under-prepared skills there is. ### 3. Core ML concepts, including when models fail You need the fundamentals solid: bias-variance, overfitting and how you'd detect it, cross-validation, regularisation, the difference between classification and regression metrics, and why accuracy is a misleading metric on imbalanced data. You should be able to talk about a handful of models — linear and logistic regression, trees and gradient boosting, maybe k-means — and explain *when you'd reach for each and when you wouldn't*. Here's the honest part: the most valuable thing you can say in an ML interview is often **why a model would fail**. Data leakage. Train/test contamination. A target that's correlated with something you won't have at prediction time. Distribution shift between training and production. Candidates who can only describe how models work sound junior. Candidates who can describe how models *break* sound like they've shipped something. You don't need to have shipped a model to learn this — you just need to study the failure modes as deliberately as the success cases. ### 4. Statistics and experimentation — p-values done right This is the area where confident wrongness costs the most. You'll likely get questions on A/B testing, hypothesis testing, confidence intervals, and the dreaded p-value. The danger isn't not knowing the definition — it's reciting a slightly wrong one with total conviction. So, plainly: a p-value is the probability of seeing a result at least as extreme as the one you observed, *assuming the null hypothesis is true*. It is **not** the probability that the null is true, and it is **not** the probability your result happened by chance. Interviewers ask this specific thing on purpose, because the wrong version is so common. Beyond definitions, be ready to reason about sample size, statistical power, what happens when you peek at an experiment early, and why running twenty tests and celebrating the one that "worked" is how you fool yourself. Good statistics in an interview is mostly about intellectual honesty under pressure. ### 5. The take-home or case study Many companies replace whiteboard puzzles with a take-home or a live case that mirrors real work: here's a dataset and a vague business question, show us how you think. This is the most predictive part of the whole process precisely because it's the least gameable. What gets rewarded is *not* the fanciest model. It's framing the problem, stating your assumptions, doing sane exploratory work, picking a reasonable approach, being honest about limitations, and writing it up so a human can follow it. I've seen clean, well-explained logistic regressions beat elaborate neural networks, because the simple submission answered the actual question and the fancy one was a showpiece with no narrative. If you're given a take-home, the README and your commentary matter as much as the code. ### 6. Behavioural and communication Easy to dismiss, genuinely decisive. You'll get asked about a project you owned, a time you were wrong, how you handled a disagreement, how you explained something technical to a non-technical stakeholder. This isn't filler — for a role that's half communication, it's a real signal. Have two or three projects you can talk about from problem to impact, and practise saying them out loud until they're tight. "I built a model" is weak. "Marketing couldn't tell which customers to prioritise, so I framed it as a ranking problem, and here's the tradeoff I chose and why" is strong. ## What candidates over-prepare Now the contrarian part — the stuff people pour weeks into that mostly doesn't pay off. **Memorising obscure algorithms.** You do not need to derive backpropagation by hand or recite the closed-form solution to ridge regression from memory. Knowing *that* regularisation trades a little bias for less variance is useful. Reproducing the matrix algebra on a whiteboard almost never comes up for an applied data science role, and when it does, it's usually a research position that will tell you that's the bar. **Leetcode-hard dynamic programming.** This is the big one. People spend a month grinding hard DP problems because that's what software-engineering prep looks like. For most data science roles, the coding bar is *easier and different*: it's pandas, SQL, and maybe an easy-to-medium Python problem about manipulating data. Grinding hard graph and DP problems is, for a typical DS role, studying for the wrong exam. (Caveat: ML-engineer roles at big tech can lean more software-engineering, so read the job description — more on variance below.) **Deep-learning theory for non-DL roles.** Unless the job is specifically computer vision or NLP, you rarely need transformer internals or attention math. A lot of people study the most intellectually exciting topics rather than the most frequently tested ones. Excitement and frequency are not the same axis. ## What candidates under-prepare Here's where the real edge is, because almost nobody practises these. **Explaining tradeoffs out loud.** Most people prepare by reading silently and nodding along. Then in the interview they're asked "why this model over that one?" and they go quiet, because they've never actually said the words. Knowing a concept and being able to *narrate* it live are different skills, and only one of them is tested. Practise talking through your reasoning, out loud, to another person or even to a wall. **Reading messy data.** Real datasets are full of nulls, duplicates, inconsistent categories, impossible values, and timezone chaos. Tutorial datasets are clean. So people arrive fluent in the clean case and helpless in the realistic one. The skill of looking at an unfamiliar table and quickly going "this column is suspicious, this join will explode the row count, these dates are in two formats" is rare and enormously valued — and you can only build it by working with genuinely messy data, not Titanic for the tenth time. **Business framing.** This is the gap between an analyst and a *good* analyst. Given a problem, can you ask what decision it informs, what the metric should be, and what "good enough" looks like? Companies want people who connect data work to outcomes, not people who model for the sake of modelling. Before every technical answer, the strong candidates spend ten seconds on "what are we actually trying to do here?" — and it changes everything that follows. ## A realistic prep plan I'm wary of rigid week-by-week schedules because everyone starts from a different place. So treat this as an ordered sequence — spend more time on whatever's weakest — rather than a strict calendar. **Weeks 1-2: SQL until it's automatic.** Joins, aggregation, window functions, subqueries. Practise on a real-ish schema and, crucially, *out loud* — narrate what each query does as you write it. Push until the second-highest-salary-per-group type problem is muscle memory. **Weeks 2-3: pandas on messy data.** Don't use clean teaching datasets. Grab something ugly — public data with real nulls and inconsistencies — and practise the full loop: inspect, clean, reshape, summarise. Force yourself to start every exercise by interrogating the data before you touch it. **Weeks 3-4: ML and statistics fundamentals, focused on intuition and failure modes.** For each model, write one honest paragraph: what it's for, when it breaks, what its main tradeoff is. For statistics, drill the p-value definition, A/B testing logic, and power until you can explain them to a sceptical friend without flinching. **Week 4-ish: one realistic case end-to-end.** Take a vague question and a messy dataset and produce a small, clearly-written analysis — framing, assumptions, simple model, honest limitations. Then explain it out loud as if presenting to a stakeholder. This single exercise rehearses about half of what real interviews test. **Throughout: behavioural stories and talking out loud.** Prepare two or three projects, problem-to-impact, and rehearse them verbally. If you can practise with another person who'll ask "why?" and "what would you do differently?", do it — that pressure is the thing you can't simulate by reading. The honest meta-point: the highest-leverage practice is almost always *speaking*, not reading. The skills interviews actually test are performed live, so prepare live. ## The honest caveat: companies vary, a lot I'd be lying if I told you one checklist fits everything. A big-tech ML-engineer role might genuinely include harder coding and system-design questions. A startup might just hand you a take-home and a coffee chat. A research role will care about the math you can otherwise skip. A heavily-experimentation product company will push hard on statistics and A/B testing. So the real first step is reading the job description and, if you can, asking the recruiter what the loop looks like — most will tell you. Then weight your prep toward that company's reality instead of a generic internet checklist. The core in this post is what's *common*; the edges are where you tailor. If you want a second set of eyes on a specific weak spot — mock-interviewing your SQL out loud, tearing apart a take-home, or pressure-testing whether you can actually explain a p-value without fudging it — that's a lot of what I do day to day, and the first session is free so you can see if it helps before committing to anything. But you don't need me to start. Open a messy dataset, write a window function, and explain it out loud. That's the whole game, and almost nobody practises it. Be the person who does.