Dell Data Science Associate study plan

Build a one-page reference sheet that defines bias-variance tradeoff, overfitting, train/validation/test splits, and the difference between supervised and unsupervised learning, then explain each aloud in under two minutes

Distinguish descriptive, inferential, and predictive analytics by classifying ten short business scenarios into the correct category with a written justification for each

Compute and interpret mean, median, standard deviation, correlation, and z-scores by hand for a small dataset, then verify results in Python with pandas

Identify three common data-quality defects (missing values, duplicates, inconsistent formats) in a provided sample table and document the remediation options for each

Write SQL queries using SELECT, WHERE, GROUP BY, HAVING, and JOIN across two related tables and explain the row-count effect of each join type

Clean a messy CSV by handling nulls, standardizing date formats, removing duplicates, and documenting each transformation decision

Reshape and merge datasets in pandas using merge, concat, pivot, and melt, and verify row counts after every operation

Describe the difference between normalization and standardization and state when each is appropriate

Engineer at least five new features from raw columns (date parts, ratios, aggregations) and justify each with a hypothesis about its predictive value

Implement one-hot encoding and ordinal encoding and explain when each is preferred over the other

Split data into train, validation, and test sets and articulate why leakage across splits invalidates results

Detect and handle outliers using IQR and z-score methods and record how many rows each method flags

Fit linear and logistic regression models and interpret coefficients, p-values, and odds ratios in plain business language

Evaluate classification models using accuracy, precision, recall, F1, and ROC-AUC, and explain why accuracy alone misleads on imbalanced data

Explain the assumptions behind linear regression and test at least two of them on real data

Distinguish Type I and Type II errors and connect each to a concrete business cost

Train decision tree, random forest, and gradient boosting models and compare their performance on the same dataset

Tune at least two hyperparameters using cross-validation and report the validation curve

Apply k-means clustering and interpret the resulting segments with descriptive statistics per cluster

Explain regularization (L1 vs L2) and demonstrate its effect on coefficient magnitude

Translate model outputs into three recommended business actions with estimated impact and stated assumptions

Build a stakeholder-facing dashboard or slide deck that leads with the decision, not the methodology

Write an executive summary of under 200 words that states the finding, the confidence level, and the recommended next step

Identify and rewrite three examples of misleading chart design or truncated axes

Construct a full narrative from raw data to recommendation that a non-technical audience can follow without jargon

Address data privacy, consent, and fairness concerns by listing three risks in your project and a mitigation for each

Revisit weak fundamentals from weeks 1-3 by re-deriving the five statistics you missed on a self-quiz

Practice explaining a model's limitations and uncertainty to a skeptical stakeholder in a five-minute mock conversation

Complete two timed full-length practice sets covering all four domains and log every missed question by domain

Re-study the two domains with the lowest practice scores using targeted drills rather than full re-reads

Memorize and rehearse the definitions most likely to be tested: p-value, confidence interval, cross-validation, and precision-recall tradeoff

Simulate exam-day pacing by completing a timed section without pausing to look anything up