All Episodes
Displaying 61 - 80 of 121 in total
Episode 60 — Encoding Categorical Data: One-Hot vs Label Encoding Tradeoffs
This episode explains categorical encoding as a modeling compatibility and meaning-preservation decision, because DataX commonly tests whether you understand how encod...
Episode 61 — Interaction Features: Cross-Terms and When They Actually Help
This episode teaches interaction features as a targeted way to represent conditional relationships, because DataX scenarios often involve effects that change by segmen...
Episode 62 — Linearization Tactics: Log, Exp, and Interpreting the New Scale
This episode focuses on linearization as a pragmatic strategy: transforming variables so relationships become closer to linear and variance becomes more stable, which ...
Episode 63 — Box-Cox and Friends: Transformations for Shape and Variance Control
This episode teaches transformation families like Box-Cox as systematic tools for addressing skewness and heteroskedasticity, which DataX may test through scenario lan...
Episode 64 — Scaling Choices: Normalization vs Standardization vs Robust Scaling
This episode explains scaling as a prerequisite for many models and a common source of subtle errors, because DataX scenarios often test whether you know which scaling...
Episode 65 — Discretization Choices: Binning for Interpretability and Model Stability
This episode covers discretization as an intentional tradeoff: converting continuous values into bins can improve interpretability and sometimes stability, but it can ...
Episode 66 — Feature Reshaping: Ratios, Aggregations, and Pivoting Concepts
This episode teaches feature reshaping as a way to convert raw operational data into variables that reflect meaningful behavior, because DataX scenarios often imply th...
Episode 67 — Geocoding as Enrichment: Location Features With Realistic Expectations
This episode explains geocoding as an enrichment strategy that can add useful location context, while also teaching the realistic expectations and governance constrain...
Episode 68 — Synthetic Data: Why It’s Used, How It’s Sampled, and Where It Misleads
This episode covers synthetic data as a tool for augmentation, privacy, and testing, while highlighting where it can mislead, because DataX scenarios may ask you to we...
Episode 69 — Designing the First Model: Baselines, Assumptions, and Quick Wins
This episode teaches first-model design as a disciplined baseline process, because DataX scenarios often test whether you start with a defensible reference point and b...
Episode 70 — Iteration Loops: From Constraints to Experiments to Better Outcomes
This episode frames iteration as the core workflow of applied data science: you start with constraints, translate them into testable hypotheses, run controlled experim...
Episode 71 — Metric Selection by Goal: Aligning Measures With Business Outcomes
This episode teaches metric selection as a goal alignment exercise rather than a default choice, because DataX scenarios often hinge on whether you can connect busines...
Episode 72 — Training Cost vs Inference Cost: Choosing Models for the Real World
This episode teaches cost thinking as a deployment constraint, because DataX scenarios often test whether you can choose models that fit operational realities, not jus...
Episode 73 — Residual Thinking: Diagnosing What Your Model Still Can’t Explain
This episode teaches residual thinking as a diagnostic discipline, because DataX scenarios frequently test whether you can interpret what remains unexplained after mod...
Episode 74 — Validation Hygiene: Data Splits, Leakage Prevention, and Reproducibility
This episode covers validation hygiene as the backbone of trustworthy performance claims, because DataX scenarios often include “too good to be true” results and ask w...
Episode 75 — Communicating Results: Clear Narratives, Honest Limitations, and Accessibility
This episode teaches communication as a technical skill, because DataX scenarios often test whether you can translate model results into a clear narrative, state limit...
Episode 76 — Documentation Essentials: Data Dictionary, Metadata, and Change Tracking
This episode covers documentation as a reliability and governance requirement, because DataX scenarios often involve teams inheriting models, auditing outcomes, or tro...
Episode 77 — Domain 2 Mixed Review: EDA, Features, and Modeling Outcomes Drills
This episode is a mixed review designed to turn Domain 2 concepts into fast scenario decisions, because the DataX exam often asks for the best next step when data qual...
Episode 78 — ML Core Concepts: Learning, Loss, and What “Optimization” Really Means
This episode defines the core machine learning loop in exam-ready terms: learning is the process of adjusting a model so its predictions improve on a defined objective...
Episode 79 — Bias-Variance Tradeoff: Diagnosing Overfitting and Underfitting by Symptoms
This episode teaches the bias-variance tradeoff as a diagnostic tool, because DataX scenarios often describe symptoms—train/validation gaps, unstable performance, or p...