Easy everything in Data Science & Analytics, page 2
First steps to identify and handle missing values
Tests systematic diagnosis before imputation. Strong answers visualize nulls, classify MCAR/MAR/MNAR, and contrast mean imputation with KNN, weighing bias versus complexity.
Star schema vs snowflake schema: differences and trade-offs
Tests dimensional modeling: star schemas flatten dimensions for fast joins and simple queries, while snowflakes normalize them to cut redundancy at the cost of extra joins. Red flag: praising snowflake storage savings without admitting query overhead.
Describe the difference between ETL and ELT and when to choose each
Tests transform timing and compute location. ETL cleans data before loading via external engines; ELT loads raw data first, then transforms in the warehouse. Pick ETL when pre-load cleansing is needed, ELT when warehouse compute is cheaper.
Why avoid one-hot encoding for high cardinality and what are alternatives?
This tests dimensionality explosion and encoding alternatives. A strong answer notes one-hot creates many sparse binary columns, increasing memory and compute costs, then names strategies such as target encoding or feature hashing and explains leakage safeguards.
Min-Max scaling vs Z-score standardization: differences and algorithm preferences
Tests if you know how feature scaling works and can pair a scaler with algorithmic assumptions. Contrast [0,1] Min-Max against mean-zero Z-score, then defend standardization for PCA or gradient descent.
What is robots.txt, why respect it, and consequences of ignoring it?
It disallows crawler paths via the Robots Exclusion Protocol; honoring it prevents server strain, legal risk, and broken trust.
How do you fetch JSON from a REST API and parse it?
This tests practical fluency with HTTP mechanics and JSON deserialization. A strong answer names the method, URL, and headers; checks the status code; then parses with r.json() or json.loads. A red flag is skipping error handling or confusing GET with POST.
Find customers who have not placed any orders
Tests SQL anti-join logic. Great answers show two paths: LEFT JOIN plus IS NULL on orders.customer_id, or NOT EXISTS, and mention NULL safety with NOT IN. Red flag: INNER JOIN with DISTINCT, which silently drops customers without orders.
Most efficient way to convert list of dicts to pandas DataFrame
Tests knowledge of DataFrame construction versus repeated row-wise assembly. Answer: pass the list directly to pd.DataFrame(data); it aligns missing keys as NaN. Red flag: recommending iterative DataFrame building.
Describe strategies for handling missing values in pandas DataFrames
Tests practical judgment on cleaning trade-offs. Good answers contrast dropna when data is abundant against fillna imputation to preserve rows, noting bias risk. Red flag: prescribing one fix without asking why values are missing or what the model needs.
What is a p-value? Interpret p = 0.03 at alpha = 0.05.
Tests frequentist testing and p-value misinterpretations. Define p-value as the probability of data this extreme under the null; since 0.03 < 0.05, reject the null at 5%. Never say it is the probability the null is false or the result is due to chance.
What are your null and alternative hypotheses for this A/B test?
This tests translating a directional business question into statistical hypotheses. A strong answer states H0 as no improvement (or a point-null equality for a specified test) and H1 as green outperforming blue. A red flag is framing H0 as 'blue is better' or changing the tail after seeing data.
How would you validate that search users are more likely to purchase?
This tests correlation versus causation in product analytics. A strong answer defines the purchase window, matches searchers to similar non-searchers, and picks a statistical test. A red flag is running a t-test without controlling for user intent or time.
What metrics track e-commerce user engagement and how do you prioritize them?
Track product discovery, add-to-cart, checkout, purchase, repeat purchase, and engagement signals; prioritize by conversion, revenue, retention, and customer value.
Disparate Impact: When Fair Rules Aren't Fair
Disparate impact is when a neutral rule causes a discriminatory outcome, regardless of intent. This is key in ML fairness, where an algorithm might deny loans to one group more than another, even with objective rules.
Algorithmic Bias: When Code Creates Unfair Outcomes
Algorithmic bias is a mirror reflecting flawed human data, leading to systematically unfair outcomes. It appears in hiring tools favoring one gender or loan systems denying certain groups. The footgun is assuming tech is neutral; the bias is in the data.
Model Cards: The Nutrition Label for AI
A Model Card is a nutrition label for an ML model, detailing its performance, biases, and intended use. It's vital for high-stakes systems to ensure fairness, like in health or legal predictions. The footgun is deploying a model without one, risking misuse.
Model Serving: Turning Trained Models into Live APIs
Model serving wraps a trained machine learning model in an API, making it a live service that can generate predictions. It's how you power features like real-time fraud detection or product recommendations.
Model Versioning: Git for Your ML Models
Think of model versioning as "Git for data." It tracks large models and datasets alongside your code without bloating your Git repo. Use it to reproduce old experiments or roll back to a better-performing model. The footgun is versioning only code, not data.
Chartjunk: Less is More in Data Visualization
Chartjunk is visual noise in a graph that doesn't help the user understand the data, like 3D effects or decorative backgrounds. It's common in reports where aesthetics are prioritized over clarity.
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