Data science
105 bites tagged Data science: interview questions with model answers, and 60-second explainers.
Test yourself: Top 30 Data science interview questions →Multiple choice, with the correct answer and why it is correct on every question. Free, no sign-in.Proxy Metrics: Estimate Long-Term Impact Now
A proxy metric uses a model to estimate a slow, long-term outcome, like annual revenue. It lets you quickly judge an A/B test's impact without waiting months for the true result. The footgun is trusting a biased model or ignoring its error, giving you false.
Counterfactual Fairness: What if You Were Different?
Asks "what if?" for fairness: would your model's decision change if only a sensitive attribute like race were different? It's used to audit models for hidden bias in areas like hiring.
Federated Learning: Train Models on Decentralized Data
Federated learning trains a shared model by sending the model to the data, not the other way around. It's used for training on sensitive, decentralized data like phone keyboards. The main footgun is that non-uniform data across clients can skew the model.
Differential Privacy: Anonymize Data with Math
Differential Privacy adds mathematical noise to data queries, making it impossible to know if one person's data is included. Tech giants use it to learn from user behavior without seeing individual activity.
Fairness Metrics: Auditing Your AI for Bias
Fairness metrics are statistical checks to see if your model's decisions are biased against certain groups. They're crucial for automated systems in hiring or loan approvals.
AI Transparency: Explaining the Black Box's 'Why'
AI transparency means seeing the 'why' behind an algorithm's decision, not just its code. It's vital for high-stakes systems like credit scoring or news feeds. The footgun is thinking open-sourcing the model is enough; true transparency explains the logic.
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.
Data Drift: Why Good Models Go Bad
Data drift is when a model's accuracy decays because the real-world data it was trained on has changed. This happens in fraud detection as scams evolve or in e-commerce as trends shift.
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.
Network Visualization: Making Sense of Connections
Network visualization turns abstract relationships into a 2D map. It's used to see structure in social networks or biological pathways. The footgun is that layout choices can create misleading patterns; a pretty graph isn't always a true one.
Interactive Data Viz: Let Users Explore the Data
Interactive data viz turns a static report into a conversation, giving users controls to ask their own questions. It's key for BI dashboards and research tools. The footgun is overwhelming users with too many options, creating confusion instead of clarity.
Pre-attentive Attributes: Your Brain on Autopilot
Pre-attentive attributes are visual properties your brain processes in milliseconds, before you consciously focus. They're the workhorses of data visualization, making outliers and patterns pop out of a dashboard. The biggest mistake is using too many at once.
Audience Analysis: Translate Data for Your Listener
Don't just show data; translate it for your audience. Audience analysis means tailoring communication to who's listening, based on their knowledge and needs. The biggest mistake is assuming your audience shares your context and jargon—they rarely do.
Data Storytelling: Using Narrative to Drive Insight
Structure your data presentation like a story—a journey with rising tension and a clear resolution. This guides stakeholders from a problem to a solution in reports.
Markov Decision Process: A Map for Sequential Decisions
A Markov Decision Process models sequential choices with uncertain outcomes. Think of it as a game with states, actions, and rewards, but where your next move is probabilistic.
Named Entity Recognition: Finding the 'Who, What, Where' in Text
Named Entity Recognition (NER) is a smart highlighter for text, automatically finding and tagging nouns like people, places, and organizations. It powers search and extracts structured data from news or support tickets.
Transfer Learning: Don't Train Models from Scratch
Transfer learning means not training a model from zero. You start with a model pre-trained on a large, general dataset, then fine-tune it for your specific task. This is common in image recognition, using a general model to learn a niche classification.
Word Embeddings: Turning Words into Math
Word embeddings turn words into vectors, where distance equals a difference in meaning. They power features like search relevance and text classification by letting algorithms 'understand' context.
Artificial Neural Networks: Learning from Examples
Think of an ANN as a digital brain that learns from examples, not explicit code. It's a network of simple nodes that adjust their connections to spot patterns. They power image recognition and language translation, but are only as good as their training data.
Causal DAGs: A Map for Cause and Effect
A Causal DAG is a map of your assumptions about what causes what. It helps you spot hidden "confounder" variables that create misleading correlations. Use it before an analysis to decide which variables to control for, ensuring you measure a true effect.
Propensity Score Matching: Simulating a Randomized Trial
Propensity Score Matching creates a 'fair' comparison from observational data, mimicking a randomized trial. It's used to estimate a treatment's effect by matching treated individuals with similar untreated ones.
Statistical Power: Is Your Test Strong Enough to Find a Real Effect?
Statistical power is your experiment's ability to detect a real effect. A low-power test is like a fishing net with holes too big—the fish swims right through. This is critical for A/B tests. The footgun is concluding "no effect" from a weak test.
A/A Testing: Sanity-Checking Your Experiments
A/A testing is a fire drill for your A/B testing system. You run two identical versions of a page to ensure your tools are working correctly before a real experiment. The biggest footgun is panicking at a false positive; 1 in 20 tests will show.
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