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Dell Data Science Associate study materials
Dell Data Science Associate study resources

Dell Data Science Associate: Statistical Modeling and Machine Learning: Inference, Prediction, and Evaluation

Dell Data Science Associate · Work through your exam, one topic at a time.

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Statistical Modeling and Machine Learning: Inference, Prediction, and Evaluation

Domain scope

Statistical Modeling and Machine Learning tests the ability to choose, fit, evaluate, and interpret models. Expect inference questions (confidence intervals, hypothesis tests, regression coefficients) and predictive questions (classification, clustering, cross-validation, metrics).

Core rules and concepts

  • Descriptive statistics summarize a sample; inferential statistics generalize with uncertainty. A p-value is the probability of data at least as extreme under the null, not the probability the null is true.
  • Linear regression assumes linearity, independent errors, homoscedasticity, and no severe multicollinearity for stable coefficient interpretation. R-squared measures explained variance in-sample, not predictive accuracy.
  • Logistic regression models log-odds; coefficients are additive on the log-odds scale. Odds ratios above 1 increase odds, not probability.
  • Bias-variance: high bias underfits; high variance overfits. Regularization (L1/L2) trades variance for bias. L1 can zero coefficients; L2 shrinks them.
  • Cross-validation estimates generalization; use stratified k-fold for classification and time-series splits for temporal data.
  • Classification metrics: precision = TP/(TP+FP); recall = TP/(TP+FN); F1 balances them. ROC-AUC ranks across thresholds; the precision-recall curve is more informative for rare positives.
  • Clustering: k-means minimizes within-cluster variance and needs k; hierarchical clustering gives a dendrogram; choose linkage and distance deliberately.

Worked example

A fraud model scores 10,000 transactions. At threshold 0.5: TP=80, FP=120, FN=20, TN=9,780. Accuracy = (80+9,780)/10,000 = 98.6%, which looks strong but is misleading because fraud is rare. Precision = 80/200 = 40%; recall = 80/100 = 80%. If a missed fraud costs $500 and a false alarm costs $20, expected cost = 20×500 + 120×20 = $12,400. Lowering the threshold may raise recall and cost less if the false-alarm cost is low. The reasoning is to optimize the business cost, not raw accuracy.

Traps

  • Reporting accuracy on imbalanced data.
  • Interpreting a p-value as the probability the hypothesis is true.
  • Tuning hyperparameters on the test set or leaking validation data into preprocessing.
  • Extrapolating a regression beyond the observed range.
  • Treating correlated predictors as independent causal effects.