Intermediate everything in Data Science & Analytics, page 4
Design a rate-limited REST API data collection script
Tests client-side throttling discipline versus reactive 429 handling. Strong answers proactively pace calls using rate-limit headers, cap concurrency, and apply exponential backoff with jitter. Red flag: tight-loop retries or ignoring headers.
Convert string timestamps to datetime and extract day of week
This tests pandas datetime parsing and accessor fluency. A strong answer uses pd.to_datetime, assigns the result, then extracts the day via .dt.day_name() or .dt.dayofweek. Red flag: manual string splitting or Python loops instead of vectorized ops.
Calculate total and average sales per region in pandas
Tests split-apply-combine fluency. A strong answer groups by Region then calls agg with a dict or named aggregation to return sum and mean of Sales_Amount together. Red flag: chaining separate groupby calls or looping rows manually.
What is vectorization in NumPy and pandas?
Tests if you know why NumPy operations beat Python loops via contiguous memory and C-level SIMD. A strong answer defines vectorization as array-wide operations without explicit loops, contrasts a ufunc to a for-loop, and cites interpreter overhead removal.
How would you combine customer and transaction DataFrames and describe join types?
This tests relational merging and join semantics in pandas. Answer: use pd.merge on customer_id, then groupby sum; describe inner, left, right, and outer joins by key preservation. Red flag: proposing concat without keys or conflating inner and left joins.
Why is 99% accuracy misleading with 1% disease prevalence?
Tests class imbalance intuition. A strong answer notes that an all-negative classifier hits 99% accuracy, then names precision, recall, F1, and AUC-PR to expose false negatives and false positives. Red flag: claiming accuracy is fine after rebalancing.
Describe the bias-variance tradeoff and how model complexity affects bias and variance
More complexity cuts bias but boosts variance via overfitting; test error forms a U.
Explain the Central Limit Theorem and its importance for hypothesis testing
This tests whether you know why sample means from skewed populations tend toward normal as size grows, enabling tests. A strong answer covers mean convergence to normal and standard error. Red flag: claiming the CLT works for small samples or single points.
How do you frame high-value customer identification as classification versus regression?
Tests mapping a business goal to a defensible target. Outline: define value and action, then contrast regression predicting spend versus classification predicting tiers. Red flag: picking models before fixing the label or the campaign action.
How would you recommend launching a checkout flow with mixed A/B metrics?
This tests multi-metric trade-offs. A strong answer tags conversion as success and AOV as a guardrail, estimates net revenue impact, and frames decision as a risk-managed business choice. A red flag is demanding all metrics win or ignoring business context.
How do you define churn for a subscription service?
This tests operationalizing a business metric into a data definition. A strong answer separates voluntary from passive churn, picks a moment, and aligns to the billing cycle. Red flag: counting all cancellations as churn while ignoring grace periods.
ML Model Registry: Source of Truth for Production Models
A model registry is version control for trained models, not just code. It tracks which artifact is running in production, who approved it, and how it was built. Skip it and you get untracked files in S3 with no way to reproduce a production model.
Spark Structured Streaming: Unify Batch and Stream
Spark Structured Streaming treats a live stream as an unbounded DataFrame. It unifies batch and streaming ETL on Kafka, but the footgun is confusing event time with processing time without watermarks, which silently drops late data.
Vectorization: Ditch the Python Loop
Vectorization means issuing one batch command to C-backed arrays instead of looping in Python. Use it for million-row DataFrames or matrix math. The footgun is treating apply() as vectorized, or silently materializing giant temporaries that exhaust RAM.
ML Pipeline: Systematic Model Delivery
A machine learning pipeline is the systematic workflow that carries models from data labeling through deployment inside MLOps. It keeps the AI lifecycle repeatable rather than ad hoc. The footgun is treating a one-off notebook as a production pipeline.
ETL: The Three-Phase Data Pipeline
ETL is a three-phase pipeline: extract from sources, transform, and load into containers. It supports many sources and destinations and runs as automated software, manual jobs, or scheduled batches. The footgun is manual execution of recurring jobs.
The Big Idea: Your Presentation's Single-Sentence Core
The "Big Idea" is a single sentence distilling your presentation's core message. It must state your point of view and what's at stake, telling your audience what to know and what to do.
How a SQL SELECT Query Actually Runs
A SQL SELECT query runs in a different order than you write it. It first builds the dataset with FROM/JOINs and filters it with WHERE, only then computing the final columns in SELECT. This is crucial for debugging.
Data Sonification: Hearing Your Data's Story
Data sonification is data visualization for your ears, mapping data points to sound properties like pitch or volume. It helps find patterns in complex datasets, like network traffic, where visuals fail.
Dashboard Design: Guide, Don't Overwhelm
A good dashboard guides users to an insight, not just displays charts. Place your key takeaway in the top-left and limit views to 2-3 to maintain focus. The biggest mistake is including too many views, which clutters the message and slows down the dashboard.
We are hiring for this. Every open role lists the topics its interview covers, so you can prepare for the real thing rather than guessing.
See open roles