All Episodes
Displaying 1 - 20 of 121 in total
Welcome to the DataX Audio Course
If you are preparing for the CompTIA DataX DY0-001 exam, welcome to the DataX PrepCast, an audio first course built to train how the exam actually thinks by teaching y...
Episode 1 — Welcome to DataX DY0-001 and How This Audio Course Works
This episode orients you to the DataX DY0-001 exam and sets the operational approach for learning complex analytics and machine learning concepts through audio only. Y...
Episode 2 — How CompTIA DataX Questions Are Built and What They Reward
This episode explains the mechanics behind CompTIA DataX question design so you can target what the exam actually rewards: disciplined interpretation, defensible trade...
Episode 3 — Reading the Prompt Like an Analyst: Keywords, Constraints, and “Best Next Step”
This episode builds the analyst mindset for reading DataX prompts: extracting decision-driving keywords, honoring constraints, and selecting the best next step rather ...
Episode 4 — Performance-Based Questions in Audio: How to Think Without a Keyboard
This episode prepares you for performance-based questions by teaching an internal, stepwise problem-solving method that works without typing, tooling, or visual aids. ...
Episode 5 — The Data Science Lifecycle at Exam Level: From Problem to Production
This episode covers the data science lifecycle as the exam expects you to understand it: an end-to-end sequence from defining the problem through deployment and ongoin...
Episode 6 — Statistical Foundations: Populations, Samples, Parameters, and Estimates
This episode refreshes the statistical foundation that DataX scenarios assume you can use fluently: the distinction between populations and samples, what parameters re...
Episode 7 — Hypothesis Testing Basics: Null, Alternative, and What p-Values Really Mean
This episode builds the hypothesis testing vocabulary and decision logic that appears repeatedly in DataX questions, especially when you must justify whether an observ...
Episode 8 — Type I vs Type II Errors and Why Power Matters in Decisions
This episode explains error types and statistical power as decision tradeoffs, which is exactly how the DataX exam tends to frame them: not as memorized definitions, b...
Episode 9 — Confidence Intervals: Interpretation, Width, and Common Traps
This episode teaches confidence intervals as an estimation tool, emphasizing interpretation and decision use rather than formula memorization, because DataX questions ...
Episode 10 — Selecting Tests: t-Test vs Chi-Squared vs ANOVA in Scenarios
This episode gives you a scenario-driven method for choosing among common statistical tests the exam expects you to recognize, focusing on what each test answers and w...
Episode 11 — Correlation and Association: Pearson vs Spearman vs “No Relationship”
This episode explains correlation and association in a way that helps you avoid common exam mistakes, especially confusing correlation strength with causation, and cho...
Episode 12 — Regression Evaluation: R², Adjusted R², RMSE, and Residual Intuition
This episode teaches how the exam expects you to evaluate regression models: not by memorizing metric names, but by understanding what each metric emphasizes and how t...
Episode 13 — Classification Evaluation: Confusion Matrix Thinking Under Pressure
This episode builds your ability to reason through classification evaluation using the confusion matrix as the mental model, because DataX commonly tests whether you c...
Episode 14 — Precision, Recall, F1, and When Accuracy Lies
This episode deepens classification metric selection by focusing on precision, recall, and F1, and by explaining when accuracy becomes a deceptive summary that leads t...
Episode 15 — Thresholding and Tradeoffs: ROC Curves, AUC, and Operating Points
This episode teaches thresholding as a control mechanism for classification systems, which is a recurring DataX theme because many scenarios are really asking you to p...
Episode 16 — Model Comparison Criteria: AIC, BIC, and Parsimony Without Hand-Waving
This episode explains model comparison through information criteria, focusing on how AIC and BIC operationalize the idea that a model should fit well without being nee...
Episode 17 — Central Limit Theorem: Why Averages Behave and When They Don’t
This episode teaches the Central Limit Theorem as a practical intuition you will use for interpreting estimates, confidence intervals, and hypothesis tests across Data...
Episode 18 — Law of Large Numbers: Stability, Variance, and Practical Implications
This episode clarifies the Law of Large Numbers as a convergence principle that supports many data science practices, and it equips you to recognize when “more data” h...
Episode 19 — Probability Essentials: Events, Conditional Probability, and Independence
This episode builds the probability fundamentals you need for DataX, emphasizing how to translate scenario language into event logic, how to reason about conditional p...