All Episodes
Displaying 41 - 60 of 121 in total
Episode 40 — Parametric vs Non-Parametric Survival: When Assumptions Help or Hurt
This episode contrasts parametric and non-parametric survival approaches, focusing on assumption tradeoffs that the DataX exam may probe in scenario-based questions. Y...
Episode 41 — Causal Thinking: Correlation vs Causation and Why the Exam Cares
This episode builds causal reasoning as a disciplined mindset, because DataX questions often test whether you can tell the difference between patterns in data and clai...
Episode 42 — Causal Tools: DAGs as a Way to Explain “What Drives What”
This episode introduces directed acyclic graphs as a practical tool for expressing causal assumptions, clarifying variable relationships, and communicating “what drive...
Episode 43 — Difference-in-Differences: Detecting Change When You Can’t Randomize
This episode explains difference-in-differences as a quasi-experimental method for estimating effects when randomization is not feasible, which is a realistic business...
Episode 44 — A/B Tests and RCTs: Treatment Effects, Validity, and Common Pitfalls
This episode covers randomized experiments as the gold standard for causal inference, focusing on what A/B tests and randomized controlled trials estimate, what makes ...
Episode 45 — Domain 1 Mixed Review: Statistics and Math Decision Drills
This episode is a mixed review designed to convert Domain 1 knowledge into fast, reliable decisions, because the DataX exam rewards candidates who can select the right...
Episode 46 — EDA Mindset: What You Look For Before You Model Anything
This episode establishes the exploratory data analysis mindset as a structured diagnostic phase, because DataX scenarios often test whether you know what to confirm be...
Episode 47 — Feature Types: Categorical, Ordinal, Continuous, Binary, and Why Choices Change
This episode teaches feature types as decision drivers, because many DataX questions hinge on whether you correctly identify variable type and choose preprocessing, mo...
Episode 48 — Univariate Analysis Narration: Distributions, Outliers, and “Typical” Behavior
This episode teaches you to narrate univariate analysis clearly without visuals, because DataX scenarios may require you to reason about distribution shape, outliers, ...
Episode 49 — Multivariate Analysis Narration: Relationships, Interactions, and Confounding
This episode teaches you to reason about multivariate relationships in spoken form, focusing on interactions and confounding because DataX questions often require you ...
Episode 50 — Chart Literacy Without Charts: What Patterns Sound Like in Words
This episode trains “chart literacy without charts,” a skill that supports audio learning and also maps directly to the DataX exam’s underlying requirement: recognizin...
Episode 51 — Data Quality Problems: Missingness, Noise, Duplicates, and Inconsistency
This episode covers core data quality failure modes and the correct responses the DataX exam expects you to prioritize, because many scenario questions are designed to...
Episode 52 — Sparse Data and High Dimensionality: Symptoms and Mitigations
This episode explains sparse data and high dimensionality as structural challenges that affect similarity, generalization, and stability, because DataX scenarios often...
Episode 53 — Nonlinearity in Data: Detecting It and Knowing When Linear Models Fail
This episode teaches you to detect nonlinearity conceptually and to know when linear models are likely to underfit, because DataX questions often probe whether you can...
Episode 54 — Non-Stationarity Beyond Time Series: Drifting Patterns in Real Systems
This episode expands non-stationarity beyond classic time series by explaining drift as a real-world property of systems, users, and environments, which DataX scenario...
Episode 55 — Seasonality and Granularity: Fixing “Wrong Time Scale” Analysis
This episode teaches seasonality and granularity as time-related framing choices that can make an analysis correct or useless, because DataX scenarios often include “w...
Episode 56 — Multicollinearity: How to Spot It and What to Do About It
This episode explains multicollinearity as a structural feature problem that can destabilize estimates, distort interpretation, and confuse feature importance, which i...
Episode 57 — Weak Features and Insufficient Signal: When Better Modeling Won’t Save You
This episode teaches you to recognize when the limiting factor is signal quality rather than algorithm choice, because DataX often frames scenarios where candidates ar...
Episode 58 — Outliers in Context: Univariate vs Multivariate and Why They Break Assumptions
This episode covers outliers as context-dependent phenomena, emphasizing the difference between univariate extremes and multivariate anomalies, because DataX scenarios...
Episode 59 — Enrichment Strategy: New Sources vs Better Features vs Better Labels
This episode teaches enrichment as a strategic decision, because DataX scenarios often present performance or reliability issues where the best improvement comes not f...