All Episodes
Displaying 21 - 40 of 121 in total
Episode 20 — Bayes’ Rule in Plain English: Updating Beliefs With Evidence
This episode explains Bayes’ Rule as a practical updating framework, focusing on the plain-English meaning that DataX scenarios typically test: how new evidence should...
Episode 21 — Distribution Families: Normal, Uniform, Binomial, Poisson, and t-Distribution
This episode teaches you to recognize common distribution families in DataX scenarios and to choose appropriate assumptions and methods based on how the data is genera...
Episode 22 — Real-World Distributions: Skew, Heavy Tails, and Power Laws
This episode focuses on distribution behavior that breaks “textbook normal” assumptions, because DataX frequently tests whether you can reason correctly when data is s...
Episode 23 — Shape Descriptors: Skewness and Kurtosis as “Data Personality”
This episode teaches skewness and kurtosis as practical descriptors of distribution shape, helping you communicate “data personality” and choose appropriate modeling a...
Episode 24 — Variance Behavior: Homoskedasticity vs Heteroskedasticity and Why It Matters
This episode explains variance behavior as an assumption that quietly affects regression validity, confidence in estimates, and the reliability of predictions across d...
Episode 25 — PDF, PMF, and CDF: The Three Views of Probability You Must Recognize
This episode teaches you to recognize three core probability representations—PMF, PDF, and CDF—because DataX questions often test whether you understand what kind of v...
Episode 26 — Simulation Thinking: Monte Carlo for Uncertainty and Risk
This episode explains Monte Carlo simulation as a general-purpose way to reason about uncertainty when analytic solutions are hard or when you need to propagate multip...
Episode 27 — Resampling Methods: Bootstrapping for Confidence Without New Data
This episode teaches bootstrapping as a resampling approach for estimating uncertainty when you cannot assume a convenient parametric form or when the analytic confide...
Episode 28 — Missing Data Types: MCAR vs MAR vs NMAR and Correct Responses
This episode teaches missing data mechanisms as a decision framework, because DataX scenarios often ask what kind of missingness you are facing and what response is de...
Episode 29 — Sampling Strategies: Stratification, Oversampling, and Class Balance
This episode teaches sampling strategies as tools to make analysis and modeling more reliable, especially when data is imbalanced or when subpopulations must be repres...
Episode 30 — Math for Modeling: Vectors, Matrices, and What Linear Algebra Enables
This episode introduces the linear algebra vocabulary that underpins many DataX modeling concepts, focusing on how vectors and matrices represent data and transformati...
Episode 31 — Matrix Operations You Must Understand: Multiply, Transpose, Invert, Decompose
This episode builds practical intuition for core matrix operations that DataX expects you to recognize conceptually, even if you never compute them by hand, because th...
Episode 32 — Eigenvalues and Eigenvectors: The Intuition Behind “Important Directions”
This episode explains eigenvalues and eigenvectors as a way to understand dominant patterns in data, which is a concept DataX may test indirectly through dimensionalit...
Episode 33 — Distance and Similarity Metrics: Euclidean, Manhattan, Cosine, and When to Use
This episode teaches distance and similarity metrics as modeling choices that shape how algorithms perceive “closeness,” which is a subtle but important concept in Dat...
Episode 34 — Calculus for ML: Derivatives as “Slope,” Partial Derivatives, and the Chain Rule
This episode introduces calculus concepts as intuitive tools for understanding learning and optimization, focusing on meaning rather than computation, which aligns wit...
Episode 35 — Logs and Exponentials: Why They Show Up in Models and Transformations
This episode explains logarithms and exponentials as tools for managing scale, growth, and multiplicative effects, which frequently appear in DataX scenarios involving...
Episode 36 — Time Series Basics: Trend, Seasonality, Noise, and Stationarity
This episode introduces time series concepts as patterns over time that require different reasoning than cross-sectional data, which is a frequent distinction in DataX...
Episode 37 — AR, MA, and ARIMA: Choosing the Right Time Series Family
This episode explains autoregressive and moving-average models as tools for capturing temporal dependence, focusing on when each family is appropriate rather than on e...
Episode 38 — Differencing and Lag Features: Fixing Non-Stationarity Without Overfitting
This episode teaches practical techniques for addressing non-stationarity, focusing on differencing and lag features as controlled ways to make temporal patterns learn...
Episode 39 — Survival Analysis Concepts: What “Time to Event” Modeling Solves
This episode introduces survival analysis as a framework for modeling time until an event occurs, which DataX scenarios may reference in contexts like churn, failure, ...