Certified: The CompTIA DataX Audio Course
Dr. Jason Edwards
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Recent Episodes
Episode 120 — Ingestion and Storage: Formats, Structured vs Unstructured, and Pipeline Choices
This episode teaches ingestion and storage as foundational pipeline design decisions, because DataX scenarios often test whether you can choose formats and storage approaches that match data…
Episode 119 — External and Commercial Data: Availability, Licensing, and Restrictions
This episode covers external and commercial data as enrichment options with governance constraints, because DataX scenarios may ask you to evaluate whether third-party data is worth using and whether…
Episode 118 — Data Acquisition: Surveys, Sensors, Transactions, Experiments, and DGP Thinking
This episode teaches data acquisition as a source-driven decision, because DataX scenarios often require you to choose the right data collection approach and to reason about the data-generating…
Episode 117 — Compliance and Privacy: PII, Proprietary Data, and Risk-Aware Handling
This episode covers compliance and privacy as design constraints that shape the entire data lifecycle, because DataX scenarios frequently test whether you can identify PII and proprietary data, apply…
Episode 116 — Business Alignment: Requirements, KPIs, and “Need vs Want” Tradeoffs
This episode teaches business alignment as the first constraint layer in DataX scenarios, because many questions are designed to test whether you can translate stakeholder language into measurable…
Episode 115 — Domain 3 Mixed Review: Model Selection and ML Scenario Drills
This episode is a mixed review designed to convert Domain 3 model-selection knowledge into fast scenario decisions, because DataX questions often present multiple plausible algorithms and reward the…
Episode 114 — Recommenders: Similarity, Collaborative Filtering, and ALS in Plain Terms
This episode explains recommender systems as methods for predicting preference or relevance, focusing on similarity-based approaches, collaborative filtering intuition, and ALS in plain terms,…
Episode 113 — SVD and Nearest Neighbors: Where They Appear in DataX Scenarios
This episode teaches SVD and nearest neighbors as foundational tools that appear across recommendation, dimensionality reduction, similarity search, and clustering, because DataX scenarios may…
Episode 112 — Nonlinear Reduction: t-SNE and UMAP for Structure, Not “Truth”
This episode covers t-SNE and UMAP as nonlinear dimensionality reduction methods, emphasizing how to interpret their outputs correctly, because DataX scenarios may test whether you understand that…
Episode 111 — Dimensionality Reduction: PCA Intuition and What Components Represent
This episode teaches PCA as a linear dimensionality reduction technique, focusing on intuition and component meaning, because DataX scenarios often test whether you can explain what components…
Episode 110 — Cluster Validation: Elbow, Silhouette, and “Does This Grouping Matter”
This episode teaches cluster validation as a reality check, because DataX scenarios may ask you how to pick k, how to evaluate whether clusters are meaningful, and how to avoid convincing yourself…
Episode 109 — Clustering: k-Means, Hierarchical, DBSCAN and Choosing the Right One
This episode teaches clustering as an unsupervised grouping task and trains you to choose among k-means, hierarchical clustering, and DBSCAN based on data geometry, scale, and the meaning of…
Episode 108 — AutoML and Few-Shot Concepts: Where Automation Fits and Where It Fails
This episode teaches AutoML and few-shot concepts as automation tools with clear boundaries, because DataX scenarios may ask you to choose when automation accelerates delivery and when it creates…
Episode 107 — Transfer Learning and Embeddings: Reuse, Fine-Tune, and Cold Start
This episode explains transfer learning and embeddings as strategies for reusing learned representations, because DataX scenarios may test whether you can recognize when leveraging prior learning is…
Episode 106 — Deep Model Families: CNN, RNN, LSTM, Autoencoders, GANs, Transformers
This episode introduces major deep model families at the conceptual level, focusing on what each family is designed to capture and how to recognize their appropriate use cases in DataX scenarios…
Episode 105 — Regularizing Deep Models: Dropout, Batch Norm, Early Stopping, Schedulers
This episode teaches deep model regularization as a toolkit for controlling overfitting and stabilizing training, because DataX scenarios may test whether you can choose among dropout, batch…
Episode 104 — Optimizers: SGD, Momentum, Adam, RMSprop and Practical Differences
This episode explains optimizers as the rules that turn gradients into parameter updates, because DataX scenarios may ask you to recognize why different optimizers behave differently in practice and…
Episode 103 — Training Mechanics: Backpropagation as Error Correction
This episode explains backpropagation as the mechanism neural networks use to adjust parameters, focusing on the intuitive idea of error correction rather than math details, because DataX questions…
Episode 102 — Activation Functions: ReLU, Sigmoid, Tanh, Softmax and Output Behavior
This episode teaches activation functions as the mechanism that gives neural networks nonlinearity and shapes output behavior, because DataX scenarios may ask you to recognize which activation fits…
Episode 101 — Neural Network Basics: Neurons, Layers, and What “Representation” Means
This episode introduces neural networks as function approximators that learn internal representations of data, because DataX scenarios may test whether you understand the vocabulary—neurons, layers,…
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Certified: The CompTIA DataX Audio Course has published 121 episodes since January 2026, covering topics in Courses, Education.
Certified: The CompTIA DataX Audio Course is currently dormant with new episodes hourly. Average episode length is 18m.
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