Intermediate everything in Data Science & Analytics, page 2
Risks of optimizing recommendations only for CTR
CTR-only invites clickbait, low satisfaction, and long-term churn; add counter-metrics like dwell time, satisfaction, retention, and diversity.
k-anonymity and its limits against linkage attacks
Define k-anonymity via indistinguishable quasi-identifier groups, apply generalization and suppression, then show homogeneity or linkage attacks break it.
Right to be forgotten and machine unlearning
Delete raw data everywhere, then remove influence via full retraining, SISA sharded retraining, or approximate unlearning; note cost and verification.
Why version data and model artifacts, not just code
Code alone cannot reproduce a model; data and artifact versioning enable rollback, debugging, audit.
CPU versus GPU serving: cost, latency, throughput
GPUs win on throughput for batched parallel work but cost more; CPUs suit low-volume or small models.
ML CI/CD versus traditional software CI/CD
Validates code plus data plus the model, auto-trains and evaluates, adds continuous training and monitoring.
What a feature store solves: skew and consistency
Central repository of computed features, one definition serving training and inference, reuse across models.
Detecting and responding to model and concept drift
Define drift, pick a metric like PSI or falling AUC against labels, then investigate, retrain, validate.
Explain k-means user segments to a marketing team
Name each segment, profile its defining traits, show size and value, recommend an action.
Two ways accurate data can still mislead in a chart
Name distortions like truncated axes or cherry-picked ranges, give the fix for each, explain why each misleads.
Reconcile rising sign-ups with falling revenue per user
Reconcile the metrics via total revenue and segment mix, frame the tradeoff, recommend an action.
Spark RDDs, DataFrames, and Datasets
RDDs are low-level typed object collections with no built-in optimization; DataFrames are named columns optimized by Catalyst and Tungsten; Datasets add compile-time type safety in…
Data skew in Spark and salting
Data skew is uneven key distribution sending most rows to one partition and straggler task; salting appends a random suffix to hot keys to spread them across partitions, joining in two…
Spark broadcast join versus shuffle join
A broadcast join sends the small table to every executor so the large table joins locally with no shuffle of its rows; the default sort-merge join shuffles both tables across the network, which is costly.
RL components and how Q-learning works
Agent acts on the environment, observes state and reward, seeking to maximize cumulative discounted reward; Q-learning iteratively updates Q(s,a) toward reward plus discounted best…
Analyzing skewed revenue-per-user experiments
Heavy tails inflate variance and slow significance, and the mean is dominated by whales; mitigate via winsorization or capping, log transforms, CUPED variance reduction, or bootstrap and rank tests.
Random Forest versus Gradient Boosting
Random Forest trains deep trees in parallel and averages to cut variance; boosting builds shallow trees sequentially, each correcting prior errors to cut bias, often higher accuracy but…
How gradient descent and the learning rate work
Gradient descent steps downhill along the negative gradient to minimize cost; the learning rate sets step size; too high diverges or oscillates, too low converges painfully slowly.
Python Virtual Environments
A virtual environment is an isolated Python installation with its own packages, so each project gets the exact dependency versions it needs without conflicting with other projects or the system Python.
Generative Adversarial Network (GAN)
A GAN trains two networks in competition: a generator that fabricates fake samples and a discriminator that judges real versus fake. Their adversarial game pushes the generator toward realistic outputs, enabling image synthesis and data generation without…
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